vortex_equation/scheduled_alpha-decrease.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sympy\n",
    "import numpy as np\n",
    "import torch\n",
    "torch.manual_seed(12)\n",
    "torch.cuda.manual_seed(12)\n",
    "torch.cuda.manual_seed_all\n",
    "\n",
    "dtype = torch.float\n",
    "device = torch.device(\"cuda:0\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sys.path.append('/u/wangnisn/devel/machine_learning_kinetics/ml_kinetics')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlk import learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Set up model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "vortex = {'eqn_type':'vortex',\n",
    "        'fcn':'exp(-1/2*(x-cos(t))**2-1/2*(y-sin(t))**2)',\n",
    "        'domain':{'x':[-2,2],'y':[-2,2],'t':[0,2]},\n",
    "        \n",
    "        'err_vec':[0,0,0,1,-1,0,0]}\n",
    "\n",
    "u_t,u_x,u_y,u,x,y = sympy.symbols('u_t u_x u_y u x y')\n",
    "\n",
    "dictionary = (u_x,u_y,x*u_x,y*u_x,x*u_y,y*u_y,u)\n",
    "vortex['dictionary'] = dictionary\n",
    "pde = vortex\n",
    "domain =pde['domain']\n",
    "fcn = pde['fcn']\n",
    "n_points = 50000\n",
    "x = sympy.symbols([x for x in domain.keys()])\n",
    "u_exact = sympy.lambdify(x,sympy.sympify(fcn),'numpy')\n",
    "inputs = {}\n",
    "for key in domain.keys():\n",
    "    min_d = domain[key][0]\n",
    "    max_d = domain[key][1]\n",
    "    #iputs[key] = ((max_d-min_d)*torch.rand((n_points,1),device=device,dtype=dtype,requires_grad=True)+min_d)\n",
    "    tt = np.random.uniform(min_d,max_d,(n_points,1))\n",
    "    inputs[key] = torch.tensor(tt,device=device,dtype=dtype,requires_grad=True)\n",
    "u_exact = sympy.lambdify(x,sympy.sympify(fcn),'numpy')\n",
    "u = u_exact(*[i.detach().cpu() for i in inputs.values()])\n",
    "pde = {}\n",
    "u = u.type(dtype).to(device)\n",
    "pde['inputs'] = inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:    0,  Loss u: 1.381e-01, Loss pde: 9.824e-04, Loss_norm: 7.488e+00, Loss tot: 1.381e-01\n",
      "  coefs: -5.6304e-01   -2.8970e+00    1.4370e-01   -6.2471e-01   -1.4866e+00    1.3500e+00    4.2288e-01  \n",
      "Epoch:   50,  Loss u: 1.582e-02, Loss pde: 3.499e-01, Loss_norm: 7.488e+00, Loss tot: 1.582e-02\n",
      "  coefs: -5.6304e-01   -2.8970e+00    1.4370e-01   -6.2471e-01   -1.4866e+00    1.3500e+00    4.2288e-01  \n",
      "Epoch:  100,  Loss u: 2.466e-03, Loss pde: 9.053e-01, Loss_norm: 7.488e+00, Loss tot: 2.466e-03\n",
      "  coefs: -5.6304e-01   -2.8970e+00    1.4370e-01   -6.2471e-01   -1.4866e+00    1.3500e+00    4.2288e-01  \n",
      "Epoch:  150,  Loss u: 1.351e-03, Loss pde: 9.366e-01, Loss_norm: 7.488e+00, Loss tot: 1.351e-03\n",
      "  coefs: -5.6304e-01   -2.8970e+00    1.4370e-01   -6.2471e-01   -1.4866e+00    1.3500e+00    4.2288e-01  \n",
      "Epoch:  200,  Loss u: 9.331e-04, Loss pde: 9.299e-01, Loss_norm: 7.488e+00, Loss tot: 9.331e-04\n",
      "  coefs: -5.6304e-01   -2.8970e+00    1.4370e-01   -6.2471e-01   -1.4866e+00    1.3500e+00    4.2288e-01  \n",
      "Epoch:  250,  Loss u: 6.144e-04, Loss pde: 9.488e-01, Loss_norm: 7.488e+00, Loss tot: 6.144e-04\n",
      "  coefs: -5.6304e-01   -2.8970e+00    1.4370e-01   -6.2471e-01   -1.4866e+00    1.3500e+00    4.2288e-01  \n",
      "Epoch:  300,  Loss u: 4.713e-03, Loss pde: 1.030e+00, Loss_norm: 7.488e+00, Loss tot: 4.713e-03\n",
      "  coefs: -5.6304e-01   -2.8970e+00    1.4370e-01   -6.2471e-01   -1.4866e+00    1.3500e+00    4.2288e-01  \n",
      "Epoch:  350,  Loss u: 3.604e-04, Loss pde: 9.510e-01, Loss_norm: 7.488e+00, Loss tot: 3.604e-04\n",
      "  coefs: -5.6304e-01   -2.8970e+00    1.4370e-01   -6.2471e-01   -1.4866e+00    1.3500e+00    4.2288e-01  \n",
      "Epoch:  400,  Loss u: 2.635e-04, Loss pde: 9.513e-01, Loss_norm: 7.488e+00, Loss tot: 2.635e-04\n",
      "  coefs: -5.6304e-01   -2.8970e+00    1.4370e-01   -6.2471e-01   -1.4866e+00    1.3500e+00    4.2288e-01  \n",
      "Epoch:  450,  Loss u: 2.825e-04, Loss pde: 9.688e-01, Loss_norm: 7.488e+00, Loss tot: 2.825e-04\n",
      "  coefs: -5.6304e-01   -2.8970e+00    1.4370e-01   -6.2471e-01   -1.4866e+00    1.3500e+00    4.2288e-01  \n",
      "Epoch:  500,  Loss u: 1.795e-04, Loss pde: 2.578e-03, Loss_norm: 1.927e+00, Loss tot: 2.648e-03\n",
      "  coefs:  1.2074e-01   -1.0065e-01    1.4603e-02    8.5312e-01   -8.2739e-01   -5.0117e-03    5.4788e-03  \n",
      "Epoch:  550,  Loss u: 3.686e-04, Loss pde: 3.827e-04, Loss_norm: 1.722e+00, Loss tot: 7.475e-04\n",
      "  coefs:  3.2387e-01   -2.2147e-01    1.2379e-02    5.7754e-01   -5.7323e-01   -1.3861e-02    0.0000e+00  \n",
      "Epoch:  600,  Loss u: 2.439e-04, Loss pde: 2.772e-04, Loss_norm: 1.753e+00, Loss tot: 5.220e-04\n",
      "  coefs:  3.0341e-01   -1.9956e-01    1.0744e-02    6.1933e-01   -6.1076e-01   -9.4545e-03   -3.1581e-05  \n",
      "Epoch:  650,  Loss u: 3.503e-04, Loss pde: 2.119e-04, Loss_norm: 1.724e+00, Loss tot: 5.657e-04\n",
      "  coefs:  3.3494e-01   -2.0650e-01    1.1031e-02    5.8017e-01   -5.7977e-01   -1.2050e-02    0.0000e+00  \n",
      "Epoch:  700,  Loss u: 1.988e-04, Loss pde: 2.010e-04, Loss_norm: 1.775e+00, Loss tot: 4.035e-04\n",
      "  coefs:  2.8957e-01   -1.8254e-01    5.5698e-03    6.4520e-01   -6.4131e-01   -9.0919e-03   -2.1930e-03  \n",
      "Epoch:  750,  Loss u: 1.672e-04, Loss pde: 1.829e-04, Loss_norm: 1.790e+00, Loss tot: 3.542e-04\n",
      "  coefs:  2.6699e-01   -1.6576e-01    2.2291e-03    6.7258e-01   -6.7147e-01   -8.7011e-03   -2.1745e-03  \n",
      "Epoch:  800,  Loss u: 2.263e-04, Loss pde: 1.562e-04, Loss_norm: 1.789e+00, Loss tot: 3.879e-04\n",
      "  coefs:  3.0689e-01   -1.3922e-01   -6.6550e-04    6.5754e-01   -6.5799e-01   -1.8112e-02   -8.2875e-03  \n",
      "Epoch:  850,  Loss u: 5.895e-04, Loss pde: 1.761e-04, Loss_norm: 1.798e+00, Loss tot: 7.686e-04\n",
      "  coefs:  2.4179e-01   -1.1900e-01    1.2817e-03    7.0403e-01   -7.0073e-01   -2.2880e-02   -8.3158e-03  \n",
      "Epoch:  900,  Loss u: 1.327e-04, Loss pde: 1.468e-04, Loss_norm: 1.832e+00, Loss tot: 2.846e-04\n",
      "  coefs:  2.4164e-01   -1.4706e-01   -3.8252e-03    7.1133e-01   -7.1229e-01   -1.0228e-02   -5.4065e-03  \n",
      "Epoch:  950,  Loss u: 4.516e-04, Loss pde: 1.480e-04, Loss_norm: 1.800e+00, Loss tot: 6.036e-04\n",
      "  coefs:  2.5581e-01   -1.5337e-01   -2.4837e-03    6.8855e-01   -6.8901e-01   -5.9022e-03   -5.3445e-03  \n",
      "Epoch: 1000,  Loss u: 1.078e-04, Loss pde: 1.305e-04, Loss_norm: 1.849e+00, Loss tot: 2.438e-04\n",
      "  coefs:  2.1977e-01   -1.3243e-01   -3.0765e-03    7.4071e-01   -7.3743e-01   -9.3997e-03   -6.4043e-03  \n",
      "Epoch: 1050,  Loss u: 1.561e-04, Loss pde: 1.335e-04, Loss_norm: 1.865e+00, Loss tot: 2.942e-04\n",
      "  coefs:  1.9929e-01   -1.3538e-01   -2.8296e-03    7.6289e-01   -7.5909e-01   -1.6415e-03   -3.9286e-03  \n",
      "Epoch: 1100,  Loss u: 1.058e-04, Loss pde: 1.140e-04, Loss_norm: 1.851e+00, Loss tot: 2.259e-04\n",
      "  coefs:  1.8342e-01   -9.0745e-02    0.0000e+00    7.7671e-01   -7.7138e-01   -1.8054e-02   -1.0664e-02  \n",
      "Epoch: 1150,  Loss u: 8.004e-05, Loss pde: 1.097e-04, Loss_norm: 1.884e+00, Loss tot: 1.960e-04\n",
      "  coefs:  1.7995e-01   -1.0841e-01   -3.3225e-03    7.8873e-01   -7.8890e-01   -8.5471e-03   -6.4766e-03  \n",
      "Epoch: 1200,  Loss u: 1.127e-04, Loss pde: 9.959e-05, Loss_norm: 1.881e+00, Loss tot: 2.191e-04\n",
      "  coefs:  2.2704e-01   -1.1409e-01   -7.3228e-03    7.5852e-01   -7.5797e-01   -7.4334e-03   -8.1733e-03  \n",
      "Epoch: 1250,  Loss u: 7.316e-05, Loss pde: 9.415e-05, Loss_norm: 1.883e+00, Loss tot: 1.744e-04\n",
      "  coefs:  1.7663e-01   -1.0255e-01   -1.8290e-03    7.9497e-01   -7.9218e-01   -7.9487e-03   -6.7053e-03  \n",
      "Epoch: 1300,  Loss u: 9.419e-04, Loss pde: 2.905e-04, Loss_norm: 2.003e+00, Loss tot: 1.215e-03\n",
      "  coefs:  2.0188e-01   -2.1562e-01   -2.0364e-02    7.6080e-01   -7.6419e-01    3.4951e-02    5.4241e-03  \n",
      "Epoch: 1350,  Loss u: 6.701e-05, Loss pde: 8.850e-05, Loss_norm: 1.909e+00, Loss tot: 1.627e-04\n",
      "  coefs:  1.7529e-01   -1.1658e-01   -8.0978e-03    8.0156e-01   -8.0197e-01   -1.3504e-03   -4.1014e-03  \n",
      "Epoch: 1400,  Loss u: 2.408e-04, Loss pde: 1.136e-04, Loss_norm: 1.931e+00, Loss tot: 3.578e-04\n",
      "  coefs:  1.8635e-01   -1.0848e-01   -8.6563e-03    8.0431e-01   -8.1235e-01    9.4039e-04   -9.6862e-03  \n",
      "Epoch: 1450,  Loss u: 7.688e-05, Loss pde: 8.024e-05, Loss_norm: 1.914e+00, Loss tot: 1.646e-04\n",
      "  coefs:  1.8429e-01   -9.1911e-02   -5.2619e-03    8.0805e-01   -8.1042e-01   -7.7958e-03   -6.4044e-03  \n",
      "Epoch: 1500,  Loss u: 6.709e-05, Loss pde: 7.629e-05, Loss_norm: 1.909e+00, Loss tot: 1.510e-04\n",
      "  coefs:  1.4645e-01   -8.8832e-02   -2.0921e-03    8.3055e-01   -8.2998e-01   -5.0416e-03   -5.7936e-03  \n",
      "Epoch: 1550,  Loss u: 3.127e-04, Loss pde: 1.163e-04, Loss_norm: 1.883e+00, Loss tot: 4.298e-04\n",
      "  coefs:  1.0639e-01   -7.8947e-02    1.9392e-03    8.4705e-01   -8.3912e-01   -7.2087e-03   -2.0063e-03  \n",
      "Epoch: 1600,  Loss u: 6.125e-05, Loss pde: 6.779e-05, Loss_norm: 1.913e+00, Loss tot: 1.373e-04\n",
      "  coefs:  1.6333e-01   -9.4801e-02   -5.0833e-03    8.2149e-01   -8.1914e-01   -3.3807e-03   -5.4179e-03  \n",
      "Epoch: 1650,  Loss u: 4.503e-05, Loss pde: 6.730e-05, Loss_norm: 1.917e+00, Loss tot: 1.204e-04\n",
      "  coefs:  1.3719e-01   -8.1959e-02   -2.5947e-03    8.4363e-01   -8.4291e-01   -3.9523e-03   -4.5893e-03  \n",
      "Epoch: 1700,  Loss u: 1.514e-04, Loss pde: 8.172e-05, Loss_norm: 1.914e+00, Loss tot: 2.384e-04\n",
      "  coefs:  1.2933e-01   -5.4378e-02   -2.2631e-03    8.5130e-01   -8.5557e-01   -1.2883e-02   -7.9544e-03  \n",
      "Epoch: 1750,  Loss u: 7.888e-05, Loss pde: 6.743e-05, Loss_norm: 1.950e+00, Loss tot: 1.540e-04\n",
      "  coefs:  1.4345e-01   -1.0412e-01   -9.6559e-03    8.4252e-01   -8.4569e-01    3.5506e-03   -9.6442e-04  \n",
      "Epoch: 1800,  Loss u: 4.995e-05, Loss pde: 6.379e-05, Loss_norm: 1.922e+00, Loss tot: 1.215e-04\n",
      "  coefs:  1.2409e-01   -6.3105e-02   -1.5020e-03    8.6068e-01   -8.6170e-01   -6.1182e-03   -5.2149e-03  \n",
      "Epoch: 1850,  Loss u: 4.911e-05, Loss pde: 6.455e-05, Loss_norm: 1.938e+00, Loss tot: 1.211e-04\n",
      "  coefs:  1.1425e-01   -7.8501e-02   -1.6585e-03    8.7073e-01   -8.6997e-01    9.6309e-04   -1.4528e-03  \n",
      "Epoch: 1900,  Loss u: 9.051e-05, Loss pde: 6.279e-05, Loss_norm: 1.935e+00, Loss tot: 1.607e-04\n",
      "  coefs:  1.5413e-01   -6.4609e-02   -6.4194e-03    8.4561e-01   -8.5011e-01   -7.9488e-03   -6.3094e-03  \n",
      "Epoch: 1950,  Loss u: 5.597e-05, Loss pde: 5.518e-05, Loss_norm: 1.912e+00, Loss tot: 1.195e-04\n",
      "  coefs:  1.0117e-01   -5.9821e-02    6.8542e-04    8.7049e-01   -8.6895e-01   -8.0610e-03   -3.1077e-03  \n",
      "Epoch: 2000,  Loss u: 1.099e-04, Loss pde: 7.475e-05, Loss_norm: 1.909e+00, Loss tot: 1.888e-04\n",
      "  coefs:  8.0032e-02   -7.2269e-02    1.1437e-03    8.7989e-01   -8.7529e-01   -4.6379e-04   -1.8561e-04  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 2050,  Loss u: 1.484e-04, Loss pde: 7.163e-05, Loss_norm: 1.897e+00, Loss tot: 2.244e-04\n",
      "  coefs:  6.0244e-02   -7.2201e-02    3.9086e-03    8.8131e-01   -8.7598e-01   -2.3229e-03   -1.3259e-03  \n",
      "Epoch: 2100,  Loss u: 1.061e-04, Loss pde: 6.561e-05, Loss_norm: 1.943e+00, Loss tot: 1.774e-04\n",
      "  coefs:  1.2398e-01   -1.9965e-02    3.5372e-05    8.8433e-01   -8.8871e-01   -1.9258e-02   -6.5983e-03  \n",
      "Epoch: 2150,  Loss u: 7.493e-05, Loss pde: 5.738e-05, Loss_norm: 1.930e+00, Loss tot: 1.393e-04\n",
      "  coefs:  9.2264e-02   -5.9864e-02   -1.3514e-03    8.8333e-01   -8.8500e-01   -6.0005e-03   -2.4431e-03  \n",
      "Epoch: 2200,  Loss u: 1.513e-04, Loss pde: 8.042e-05, Loss_norm: 1.958e+00, Loss tot: 2.336e-04\n",
      "  coefs:  7.1949e-02   -1.0446e-01   -3.8569e-03    8.8357e-01   -8.8009e-01    1.1992e-02    2.5299e-03  \n",
      "Epoch: 2250,  Loss u: 3.866e-05, Loss pde: 4.941e-05, Loss_norm: 1.958e+00, Loss tot: 9.654e-05\n",
      "  coefs:  1.0909e-01   -4.2640e-02   -3.1027e-03    8.9548e-01   -8.9832e-01   -6.5121e-03   -3.2288e-03  \n",
      "Epoch: 2300,  Loss u: 2.606e-05, Loss pde: 4.241e-05, Loss_norm: 1.954e+00, Loss tot: 7.826e-05\n",
      "  coefs:  8.8059e-02   -4.6784e-02   -1.7846e-03    9.0478e-01   -9.0576e-01   -2.7483e-03   -3.6866e-03  \n",
      "Epoch: 2350,  Loss u: 4.816e-05, Loss pde: 4.777e-05, Loss_norm: 1.971e+00, Loss tot: 1.044e-04\n",
      "  coefs:  9.5832e-02   -4.0918e-02   -3.3761e-03    9.0911e-01   -9.1440e-01   -3.2769e-03   -4.1602e-03  \n",
      "Epoch: 2400,  Loss u: 5.897e-05, Loss pde: 4.966e-05, Loss_norm: 1.934e+00, Loss tot: 1.161e-04\n",
      "  coefs:  6.1747e-02   -3.6744e-02   -1.3393e-03    9.1145e-01   -9.1155e-01   -1.0136e-02    9.2058e-04  \n",
      "Epoch: 2450,  Loss u: 6.489e-05, Loss pde: 4.986e-05, Loss_norm: 1.938e+00, Loss tot: 1.219e-04\n",
      "  coefs:  6.0567e-02   -4.6337e-02    3.5886e-03    9.1152e-01   -9.0893e-01   -2.4533e-03   -4.8184e-03  \n",
      "Epoch: 2500,  Loss u: 1.373e-04, Loss pde: 6.459e-05, Loss_norm: 1.981e+00, Loss tot: 2.056e-04\n",
      "  coefs:  4.9376e-02   -9.5249e-02   -5.8531e-03    9.0682e-01   -9.0614e-01    1.2143e-02    5.6009e-03  \n",
      "Epoch: 2550,  Loss u: 5.638e-05, Loss pde: 4.001e-05, Loss_norm: 1.936e+00, Loss tot: 1.056e-04\n",
      "  coefs:  6.3427e-02   -1.9088e-03    2.0702e-03    9.2454e-01   -9.2298e-01   -1.6317e-02   -4.9850e-03  \n",
      "Epoch: 2600,  Loss u: 3.090e-05, Loss pde: 3.774e-05, Loss_norm: 1.970e+00, Loss tot: 7.855e-05\n",
      "  coefs:  6.4030e-02   -5.0042e-02   -1.4473e-03    9.2592e-01   -9.2534e-01    1.8115e-03   -1.8499e-03  \n",
      "Epoch: 2650,  Loss u: 1.662e-04, Loss pde: 6.297e-05, Loss_norm: 1.941e+00, Loss tot: 2.319e-04\n",
      "  coefs:  7.3534e-02    7.3275e-04    0.0000e+00    9.2440e-01   -9.2492e-01   -1.2641e-02   -4.4161e-03  \n",
      "Epoch: 2700,  Loss u: 4.766e-05, Loss pde: 3.456e-05, Loss_norm: 1.987e+00, Loss tot: 9.276e-05\n",
      "  coefs:  9.1202e-02   -3.0753e-02   -2.2480e-03    9.2596e-01   -9.2639e-01   -3.3523e-03   -6.8846e-03  \n",
      "Epoch: 2750,  Loss u: 1.976e-05, Loss pde: 3.019e-05, Loss_norm: 1.957e+00, Loss tot: 6.122e-05\n",
      "  coefs:  4.8432e-02   -3.9016e-02   -1.7848e-03    9.3330e-01   -9.3142e-01   -2.4723e-03    4.0394e-04  \n",
      "Epoch: 2800,  Loss u: 1.615e-05, Loss pde: 2.969e-05, Loss_norm: 1.965e+00, Loss tot: 5.718e-05\n",
      "  coefs:  5.2167e-02   -2.9981e-02    6.3492e-04    9.4047e-01   -9.3862e-01   -6.2554e-04   -2.6278e-03  \n",
      "Epoch: 2850,  Loss u: 2.172e-05, Loss pde: 3.184e-05, Loss_norm: 1.964e+00, Loss tot: 6.413e-05\n",
      "  coefs:  5.1226e-02   -3.1688e-02   -2.1255e-04    9.3639e-01   -9.3950e-01   -1.3027e-04   -4.8973e-03  \n",
      "Epoch: 2900,  Loss u: 5.841e-05, Loss pde: 3.403e-05, Loss_norm: 1.951e+00, Loss tot: 1.021e-04\n",
      "  coefs:  2.2266e-02   -5.3639e-02   -1.7303e-03    9.3511e-01   -9.3203e-01    2.7061e-03    3.4741e-03  \n",
      "Epoch: 2950,  Loss u: 2.651e-05, Loss pde: 2.789e-05, Loss_norm: 1.959e+00, Loss tot: 6.577e-05\n",
      "  coefs:  3.6880e-02   -2.7602e-02    1.0081e-03    9.4481e-01   -9.4381e-01   -2.1774e-03   -2.3610e-03  \n",
      "Epoch: 3000,  Loss u: 1.345e-05, Loss pde: 2.432e-05, Loss_norm: 1.965e+00, Loss tot: 5.013e-05\n",
      "  coefs:  4.0037e-02   -2.8000e-02   -9.9468e-04    9.4764e-01   -9.4685e-01   -1.1483e-03   -0.0000e+00  \n",
      "Epoch: 3050,  Loss u: 1.735e-04, Loss pde: 5.460e-05, Loss_norm: 2.008e+00, Loss tot: 2.316e-04\n",
      "  coefs:  3.2686e-02    4.4299e-02    1.1319e-02    9.4394e-01   -9.4011e-01   -3.2682e-02   -3.0665e-03  \n",
      "Epoch: 3100,  Loss u: 2.174e-05, Loss pde: 2.552e-05, Loss_norm: 1.959e+00, Loss tot: 5.894e-05\n",
      "  coefs:  2.4097e-02   -2.2704e-02    3.2454e-04    9.5379e-01   -9.5284e-01   -4.7830e-03   -0.0000e+00  \n",
      "Epoch: 3150,  Loss u: 7.345e-05, Loss pde: 3.616e-05, Loss_norm: 1.997e+00, Loss tot: 1.182e-04\n",
      "  coefs:  2.8634e-02   -7.0532e-02   -5.0481e-03    9.4069e-01   -9.3706e-01    1.1699e-02    2.8503e-03  \n",
      "Epoch: 3200,  Loss u: 3.663e-05, Loss pde: 2.636e-05, Loss_norm: 1.979e+00, Loss tot: 7.436e-05\n",
      "  coefs:  6.0840e-02   -1.9439e-03    2.0327e-03    9.5054e-01   -9.4838e-01   -6.6075e-03   -8.8930e-03  \n",
      "Epoch: 3250,  Loss u: 1.734e-04, Loss pde: 3.862e-05, Loss_norm: 1.996e+00, Loss tot: 2.195e-04\n",
      "  coefs:  7.4262e-02   -1.8889e-02   -8.6774e-03    9.4235e-01   -9.4817e-01   -2.6597e-03   -1.4085e-03  \n",
      "Epoch: 3300,  Loss u: 2.038e-05, Loss pde: 2.273e-05, Loss_norm: 1.982e+00, Loss tot: 5.544e-05\n",
      "  coefs:  5.0054e-02   -2.1187e-02   -1.0906e-03    9.5230e-01   -9.5338e-01   -1.5826e-03   -2.6888e-03  \n",
      "Epoch: 3350,  Loss u: 1.144e-05, Loss pde: 1.998e-05, Loss_norm: 1.970e+00, Loss tot: 4.443e-05\n",
      "  coefs:  3.4039e-02   -2.2590e-02   -0.0000e+00    9.5692e-01   -9.5495e-01   -8.0608e-04   -4.3483e-04  \n",
      "Epoch: 3400,  Loss u: 1.173e-05, Loss pde: 1.969e-05, Loss_norm: 1.979e+00, Loss tot: 4.452e-05\n",
      "  coefs:  3.5407e-02   -2.1804e-02   -1.1659e-04    9.6066e-01   -9.5993e-01    4.0329e-04   -6.1549e-04  \n",
      "Epoch: 3450,  Loss u: 1.383e-05, Loss pde: 2.438e-05, Loss_norm: 1.988e+00, Loss tot: 4.969e-05\n",
      "  coefs:  2.8306e-02   -5.9692e-03    3.2808e-03    9.7044e-01   -9.7206e-01   -4.0531e-03   -3.4576e-03  \n",
      "Epoch: 3500,  Loss u: 1.760e-05, Loss pde: 2.091e-05, Loss_norm: 1.973e+00, Loss tot: 5.093e-05\n",
      "  coefs:  2.9968e-02   -1.3103e-02   -1.5034e-04    9.6343e-01   -9.6244e-01   -3.3577e-03   -1.6016e-04  \n",
      "Epoch: 3550,  Loss u: 1.040e-05, Loss pde: 1.905e-05, Loss_norm: 1.986e+00, Loss tot: 4.256e-05\n",
      "  coefs:  3.3258e-02   -2.4832e-02   -1.2647e-03    9.6207e-01   -9.6224e-01    1.9829e-03   -7.3235e-04  \n",
      "Epoch: 3600,  Loss u: 9.343e-06, Loss pde: 1.784e-05, Loss_norm: 1.982e+00, Loss tot: 4.059e-05\n",
      "  coefs:  3.0599e-02   -1.5948e-02   -8.1224e-05    9.6715e-01   -9.6733e-01   -1.7886e-04   -3.2912e-04  \n",
      "Epoch: 3650,  Loss u: 5.709e-05, Loss pde: 3.789e-05, Loss_norm: 1.972e+00, Loss tot: 1.009e-04\n",
      "  coefs:  3.6611e-02   -2.3723e-02   -4.9528e-03    9.5190e-01   -9.5277e-01   -1.5658e-03    2.6126e-04  \n",
      "Epoch: 3700,  Loss u: 1.070e-05, Loss pde: 1.743e-05, Loss_norm: 1.981e+00, Loss tot: 4.149e-05\n",
      "  coefs:  2.9015e-02   -2.5739e-02   -0.0000e+00    9.6294e-01   -9.6118e-01    1.9324e-03    1.2622e-04  \n",
      "Epoch: 3750,  Loss u: 3.779e-04, Loss pde: 4.310e-05, Loss_norm: 1.983e+00, Loss tot: 4.246e-04\n",
      "  coefs:  4.6012e-02   -5.2613e-03   -4.0447e-03    9.5782e-01   -9.6115e-01   -6.7111e-03   -2.0318e-03  \n",
      "Epoch: 3800,  Loss u: 1.389e-05, Loss pde: 1.870e-05, Loss_norm: 1.982e+00, Loss tot: 4.531e-05\n",
      "  coefs:  3.5352e-02   -9.8325e-03    9.2335e-04    9.6755e-01   -9.6471e-01   -1.9238e-03   -1.8631e-03  \n",
      "Epoch: 3850,  Loss u: 4.172e-05, Loss pde: 2.019e-05, Loss_norm: 2.011e+00, Loss tot: 7.424e-05\n",
      "  coefs:  5.2030e-02   -1.7684e-02   -9.1947e-04    9.6712e-01   -9.6839e-01    1.5233e-03   -3.0748e-03  \n",
      "Epoch: 3900,  Loss u: 3.718e-05, Loss pde: 2.192e-05, Loss_norm: 1.985e+00, Loss tot: 7.041e-05\n",
      "  coefs:  1.3346e-02    1.0456e-02    1.8722e-03    9.7490e-01   -9.7324e-01   -1.0743e-02    4.6749e-04  \n",
      "Epoch: 3950,  Loss u: 4.837e-05, Loss pde: 2.891e-05, Loss_norm: 1.998e+00, Loss tot: 8.585e-05\n",
      "  coefs:  5.0147e-02   -4.2754e-04    2.6799e-04    9.6827e-01   -9.7052e-01   -3.6570e-03   -4.4165e-03  \n",
      "Epoch: 4000,  Loss u: 4.197e-05, Loss pde: 2.322e-05, Loss_norm: 1.966e+00, Loss tot: 7.557e-05\n",
      "  coefs:  6.5183e-03   -3.4052e-03    5.7809e-04    9.7326e-01   -9.7307e-01   -6.5899e-03    2.8884e-03  \n",
      "Epoch: 4050,  Loss u: 3.912e-05, Loss pde: 2.046e-05, Loss_norm: 2.036e+00, Loss tot: 7.166e-05\n",
      "  coefs:  3.5486e-02   -5.1829e-02   -4.5310e-03    9.6362e-01   -9.6667e-01    1.3397e-02    3.1075e-04  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 4100,  Loss u: 2.893e-05, Loss pde: 1.784e-05, Loss_norm: 1.987e+00, Loss tot: 5.934e-05\n",
      "  coefs:  1.0638e-02   -3.0360e-02    4.1709e-04    9.7154e-01   -9.7021e-01    3.6250e-03    2.2908e-04  \n",
      "Epoch: 4150,  Loss u: 1.211e-05, Loss pde: 1.509e-05, Loss_norm: 1.982e+00, Loss tot: 4.076e-05\n",
      "  coefs:  1.6439e-02   -1.9250e-02   -0.0000e+00    9.7267e-01   -9.7149e-01    1.1150e-03    7.0003e-04  \n",
      "Epoch: 4200,  Loss u: 7.920e-06, Loss pde: 1.435e-05, Loss_norm: 1.987e+00, Loss tot: 3.611e-05\n",
      "  coefs:  2.1234e-02   -1.4267e-02    4.9817e-05    9.7541e-01   -9.7463e-01    9.9740e-04   -1.3360e-04  \n",
      "Epoch: 4250,  Loss u: 1.177e-05, Loss pde: 1.420e-05, Loss_norm: 1.990e+00, Loss tot: 3.983e-05\n",
      "  coefs:  2.2097e-02   -1.4944e-02   -7.9173e-04    9.7528e-01   -9.7563e-01    7.4922e-04   -3.1403e-05  \n",
      "Epoch: 4300,  Loss u: 1.490e-05, Loss pde: 1.677e-05, Loss_norm: 1.982e+00, Loss tot: 4.429e-05\n",
      "  coefs:  1.7062e-03   -1.6559e-02    1.6377e-03    9.8124e-01   -9.7986e-01    1.1918e-04    1.0617e-03  \n",
      "Epoch: 4350,  Loss u: 1.428e-05, Loss pde: 1.576e-05, Loss_norm: 2.006e+00, Loss tot: 4.325e-05\n",
      "  coefs:  3.1825e-02   -1.2826e-02   -1.1225e-03    9.7801e-01   -9.7890e-01    1.7331e-03   -1.5803e-03  \n",
      "Epoch: 4400,  Loss u: 4.023e-05, Loss pde: 2.487e-05, Loss_norm: 2.003e+00, Loss tot: 7.420e-05\n",
      "  coefs:  3.4813e-02   -1.6927e-03   -1.2053e-03    9.7911e-01   -9.8315e-01   -2.0475e-03   -1.4342e-03  \n",
      "Epoch: 4450,  Loss u: 3.766e-05, Loss pde: 1.867e-05, Loss_norm: 1.977e+00, Loss tot: 6.780e-05\n",
      "  coefs: -8.3010e-03   -1.8984e-02    1.3292e-03    9.7358e-01   -9.7119e-01   -1.7776e-04    3.4747e-03  \n",
      "Epoch: 4500,  Loss u: 2.930e-05, Loss pde: 1.948e-05, Loss_norm: 2.013e+00, Loss tot: 6.015e-05\n",
      "  coefs:  3.8091e-02   -4.1046e-03   -7.7121e-04    9.8142e-01   -9.8466e-01    3.1651e-04   -3.2354e-03  \n",
      "Epoch: 4550,  Loss u: 1.520e-05, Loss pde: 1.459e-05, Loss_norm: 1.990e+00, Loss tot: 4.305e-05\n",
      "  coefs:  3.1894e-02   -1.0933e-02    7.1307e-04    9.7186e-01   -9.6925e-01   -1.6673e-03   -3.4842e-03  \n",
      "Epoch: 4600,  Loss u: 7.912e-06, Loss pde: 1.292e-05, Loss_norm: 1.987e+00, Loss tot: 3.477e-05\n",
      "  coefs:  2.4580e-02   -1.4157e-02   -3.4139e-04    9.7394e-01   -9.7327e-01    1.2658e-04   -6.5930e-04  \n",
      "Epoch: 4650,  Loss u: 6.832e-05, Loss pde: 2.367e-05, Loss_norm: 2.009e+00, Loss tot: 1.011e-04\n",
      "  coefs:  1.3856e-02   -2.6131e-02    7.1555e-04    9.7977e-01   -9.7905e-01    6.4818e-03    2.5899e-03  \n",
      "Epoch: 4700,  Loss u: 9.323e-06, Loss pde: 1.590e-05, Loss_norm: 1.996e+00, Loss tot: 3.771e-05\n",
      "  coefs:  2.1772e-02   -2.3345e-02   -2.5269e-04    9.7329e-01   -9.7290e-01    2.2608e-03    1.6835e-03  \n",
      "Epoch: 4750,  Loss u: 6.999e-06, Loss pde: 1.249e-05, Loss_norm: 1.988e+00, Loss tot: 3.344e-05\n",
      "  coefs:  1.8190e-02   -1.1178e-02    1.7308e-04    9.7889e-01   -9.7903e-01    3.8585e-04   -5.1576e-05  \n",
      "Epoch: 4800,  Loss u: 7.272e-06, Loss pde: 1.229e-05, Loss_norm: 1.988e+00, Loss tot: 3.355e-05\n",
      "  coefs:  1.3910e-02   -1.3710e-02   -0.0000e+00    9.7971e-01   -9.7937e-01    1.0908e-03    3.7450e-04  \n",
      "Epoch: 4850,  Loss u: 7.645e-05, Loss pde: 1.568e-05, Loss_norm: 1.988e+00, Loss tot: 1.044e-04\n",
      "  coefs:  1.8841e-02   -1.0269e-02    7.8324e-04    9.7637e-01   -9.7951e-01   -1.0301e-03    7.1242e-04  \n",
      "Epoch: 4900,  Loss u: 7.859e-06, Loss pde: 1.226e-05, Loss_norm: 1.983e+00, Loss tot: 3.395e-05\n",
      "  coefs:  2.4044e-02   -1.4916e-02   -4.9528e-04    9.7301e-01   -9.7049e-01    1.8529e-04   -4.6505e-05  \n",
      "Epoch: 4950,  Loss u: 1.171e-05, Loss pde: 1.328e-05, Loss_norm: 1.998e+00, Loss tot: 3.840e-05\n",
      "  coefs:  2.5580e-02   -8.2442e-03   -1.1409e-04    9.8111e-01   -9.8181e-01    2.0423e-04   -6.8957e-04  \n",
      "Epoch: 5000,  Loss u: 8.895e-06, Loss pde: 1.309e-05, Loss_norm: 1.998e+00, Loss tot: 3.542e-05\n",
      "  coefs:  1.9333e-02   -1.4055e-02   -6.6984e-04    9.8104e-01   -9.8059e-01    1.7812e-03    1.4206e-04  \n",
      "Epoch: 5050,  Loss u: 1.677e-05, Loss pde: 1.440e-05, Loss_norm: 1.992e+00, Loss tot: 4.383e-05\n",
      "  coefs:  1.9530e-02   -9.3986e-03   -0.0000e+00    9.8048e-01   -9.8190e-01    1.1180e-04   -4.4794e-04  \n",
      "Epoch: 5100,  Loss u: 7.743e-05, Loss pde: 3.885e-05, Loss_norm: 2.008e+00, Loss tot: 1.166e-04\n",
      "  coefs:  2.6829e-02    7.9464e-03   -3.9921e-04    9.8017e-01   -9.8357e-01   -6.2673e-03   -3.3106e-03  \n",
      "Epoch: 5150,  Loss u: 1.172e-05, Loss pde: 1.211e-05, Loss_norm: 1.977e+00, Loss tot: 3.737e-05\n",
      "  coefs:  1.9214e-02   -3.8171e-03    1.3419e-03    9.7611e-01   -9.7310e-01   -3.1843e-03   -5.6990e-04  \n",
      "Epoch: 5200,  Loss u: 7.856e-06, Loss pde: 1.184e-05, Loss_norm: 1.985e+00, Loss tot: 3.340e-05\n",
      "  coefs:  1.0993e-02   -1.0809e-02    2.8108e-04    9.8148e-01   -9.8100e-01   -1.6304e-04    2.9372e-04  \n",
      "Epoch: 5250,  Loss u: 2.442e-04, Loss pde: 4.328e-05, Loss_norm: 2.032e+00, Loss tot: 2.851e-04\n",
      "  coefs:  3.4034e-02   -4.0676e-02   -5.7285e-03    9.6817e-01   -9.6930e-01    1.0445e-02    3.4432e-03  \n",
      "Epoch: 5300,  Loss u: 7.882e-06, Loss pde: 1.200e-05, Loss_norm: 1.987e+00, Loss tot: 3.339e-05\n",
      "  coefs:  1.8393e-02   -1.2164e-02   -1.3210e-03    9.7718e-01   -9.7677e-01   -2.5427e-05    6.9306e-04  \n",
      "Epoch: 5350,  Loss u: 2.565e-05, Loss pde: 1.705e-05, Loss_norm: 2.007e+00, Loss tot: 5.366e-05\n",
      "  coefs:  3.4170e-02   -6.0515e-03   -1.4844e-03    9.8041e-01   -9.8290e-01    6.5689e-05   -1.5353e-03  \n",
      "Epoch: 5400,  Loss u: 3.311e-05, Loss pde: 2.099e-05, Loss_norm: 1.978e+00, Loss tot: 6.255e-05\n",
      "  coefs:  4.2241e-03   -1.1388e-02    1.9966e-03    9.8156e-01   -9.7783e-01   -0.0000e+00    9.0297e-04  \n",
      "Epoch: 5450,  Loss u: 2.603e-05, Loss pde: 1.948e-05, Loss_norm: 1.982e+00, Loss tot: 5.472e-05\n",
      "  coefs:  8.7659e-03   -1.9780e-02    9.9242e-04    9.7586e-01   -9.7246e-01    2.8548e-03    9.3056e-04  \n",
      "Epoch: 5500,  Loss u: 5.964e-06, Loss pde: 1.170e-05, Loss_norm: 1.992e+00, Loss tot: 3.116e-05\n",
      "  coefs:  6.5179e-03   -6.8557e-03    0.0000e+00    9.8773e-01   -9.8923e-01   -3.8392e-04    1.1312e-03  \n",
      "Epoch: 5550,  Loss u: 1.771e-04, Loss pde: 2.480e-05, Loss_norm: 1.988e+00, Loss tot: 2.080e-04\n",
      "  coefs:  7.3358e-03   -1.2734e-02    3.7894e-03    9.8046e-01   -9.8017e-01    2.7591e-03    8.9183e-04  \n",
      "Epoch: 5600,  Loss u: 9.670e-06, Loss pde: 1.249e-05, Loss_norm: 1.999e+00, Loss tot: 3.516e-05\n",
      "  coefs:  8.3146e-03   -1.6053e-02    3.8439e-04    9.8569e-01   -9.8544e-01    2.7836e-03    2.6685e-04  \n",
      "Epoch: 5650,  Loss u: 5.726e-06, Loss pde: 1.091e-05, Loss_norm: 1.990e+00, Loss tot: 3.038e-05\n",
      "  coefs:  1.1899e-02   -5.8371e-03    1.6022e-05    9.8617e-01   -9.8628e-01   -0.0000e+00   -0.0000e+00  \n",
      "Epoch: 5700,  Loss u: 6.624e-05, Loss pde: 1.995e-05, Loss_norm: 2.010e+00, Loss tot: 9.493e-05\n",
      "  coefs:  1.5287e-02   -1.5549e-02    5.4567e-04    9.8601e-01   -9.8739e-01    3.1466e-03    2.1312e-03  \n",
      "Epoch: 5750,  Loss u: 7.902e-06, Loss pde: 1.139e-05, Loss_norm: 1.991e+00, Loss tot: 3.266e-05\n",
      "  coefs:  1.3266e-02   -2.5576e-03    9.2594e-04    9.8661e-01   -9.8553e-01   -1.8769e-03    5.9745e-05  \n",
      "Epoch: 5800,  Loss u: 5.731e-06, Loss pde: 1.059e-05, Loss_norm: 1.994e+00, Loss tot: 3.012e-05\n",
      "  coefs:  1.1034e-02   -8.6888e-03    2.5165e-04    9.8663e-01   -9.8630e-01    5.4566e-04    7.2764e-05  \n",
      "Epoch: 5850,  Loss u: 7.512e-05, Loss pde: 1.938e-05, Loss_norm: 2.009e+00, Loss tot: 1.033e-04\n",
      "  coefs:  3.2496e-02   -1.2671e-02   -3.0662e-03    9.7768e-01   -9.8080e-01    1.8017e-03   -7.8802e-04  \n",
      "Epoch: 5900,  Loss u: 9.273e-06, Loss pde: 1.097e-05, Loss_norm: 1.984e+00, Loss tot: 3.361e-05\n",
      "  coefs:  8.4136e-03   -1.5352e-02   -6.3244e-04    9.7896e-01   -9.7799e-01    6.9738e-04    1.4986e-03  \n",
      "Epoch: 5950,  Loss u: 7.584e-06, Loss pde: 1.053e-05, Loss_norm: 1.983e+00, Loss tot: 3.168e-05\n",
      "  coefs:  4.3958e-03   -1.0838e-02    4.6022e-04    9.8316e-01   -9.8298e-01    4.6919e-04    5.9957e-04  \n",
      "Epoch: 6000,  Loss u: 1.926e-04, Loss pde: 4.569e-05, Loss_norm: 2.011e+00, Loss tot: 2.310e-04\n",
      "  coefs:  2.5687e-02    1.4182e-02   -1.6786e-03    9.7670e-01   -9.7944e-01   -9.4804e-03   -3.9241e-03  \n",
      "Epoch: 6050,  Loss u: 2.301e-05, Loss pde: 1.557e-05, Loss_norm: 2.007e+00, Loss tot: 4.924e-05\n",
      "  coefs:  1.6230e-02   -3.0458e-02   -2.0215e-03    9.7471e-01   -9.7559e-01    6.7772e-03    9.0963e-04  \n",
      "Epoch: 6100,  Loss u: 1.223e-05, Loss pde: 1.242e-05, Loss_norm: 1.992e+00, Loss tot: 3.700e-05\n",
      "  coefs:  1.2184e-02   -2.5262e-03   -3.9759e-04    9.8657e-01   -9.8828e-01   -1.3989e-03   -4.7758e-04  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 6150,  Loss u: 1.310e-05, Loss pde: 1.021e-05, Loss_norm: 1.992e+00, Loss tot: 3.696e-05\n",
      "  coefs:  1.0741e-02   -9.4009e-03    0.0000e+00    9.8476e-01   -9.8583e-01    1.1449e-03   -2.8668e-04  \n",
      "Epoch: 6200,  Loss u: 1.810e-05, Loss pde: 1.489e-05, Loss_norm: 1.981e+00, Loss tot: 4.357e-05\n",
      "  coefs:  7.5150e-03   -4.9490e-03   -2.6595e-04    9.8356e-01   -9.8239e-01   -1.8324e-03    3.8845e-04  \n",
      "Epoch: 6250,  Loss u: 6.860e-06, Loss pde: 1.094e-05, Loss_norm: 1.990e+00, Loss tot: 3.087e-05\n",
      "  coefs:  1.0935e-02   -6.5022e-03    1.7480e-04    9.8597e-01   -9.8588e-01   -9.7821e-05    1.1900e-04  \n",
      "Epoch: 6300,  Loss u: 8.815e-06, Loss pde: 1.009e-05, Loss_norm: 1.995e+00, Loss tot: 3.250e-05\n",
      "  coefs:  8.6569e-03   -8.3965e-03    5.6972e-04    9.8763e-01   -9.8818e-01    9.5574e-04    2.1997e-04  \n",
      "Epoch: 6350,  Loss u: 9.504e-06, Loss pde: 1.156e-05, Loss_norm: 2.002e+00, Loss tot: 3.375e-05\n",
      "  coefs:  1.1457e-02   -1.3337e-02   -1.7090e-03    9.8536e-01   -9.8821e-01    1.8977e-03   -1.4866e-04  \n",
      "Epoch: 6400,  Loss u: 6.832e-06, Loss pde: 1.055e-05, Loss_norm: 1.988e+00, Loss tot: 3.051e-05\n",
      "  coefs:  5.4724e-03   -9.1899e-03    2.7950e-04    9.8525e-01   -9.8655e-01    0.0000e+00    8.1352e-04  \n",
      "Epoch: 6450,  Loss u: 1.968e-05, Loss pde: 1.400e-05, Loss_norm: 1.985e+00, Loss tot: 4.450e-05\n",
      "  coefs: -2.8706e-04   -1.7016e-02    5.8659e-04    9.8198e-01   -9.8067e-01    2.3689e-03    1.6954e-03  \n",
      "Epoch: 6500,  Loss u: 2.635e-05, Loss pde: 1.598e-05, Loss_norm: 2.028e+00, Loss tot: 5.223e-05\n",
      "  coefs:  2.3960e-02   -2.3192e-02   -4.5340e-03    9.8405e-01   -9.8605e-01    5.9804e-03    5.1325e-04  \n",
      "Epoch: 6550,  Loss u: 8.762e-06, Loss pde: 1.108e-05, Loss_norm: 2.002e+00, Loss tot: 3.261e-05\n",
      "  coefs:  1.3788e-02   -1.2116e-02   -1.0314e-03    9.8635e-01   -9.8640e-01    2.3492e-03    1.8872e-04  \n",
      "Epoch: 6600,  Loss u: 5.018e-06, Loss pde: 1.002e-05, Loss_norm: 1.992e+00, Loss tot: 2.835e-05\n",
      "  coefs:  8.9566e-03   -3.3314e-03    4.3323e-04    9.8911e-01   -9.8921e-01   -5.6686e-04   -5.0333e-05  \n",
      "Epoch: 6650,  Loss u: 6.073e-05, Loss pde: 1.486e-05, Loss_norm: 1.991e+00, Loss tot: 8.563e-05\n",
      "  coefs:  2.4348e-03   -1.7001e-02    1.5709e-03    9.8390e-01   -9.8339e-01    2.4346e-03    4.1209e-04  \n",
      "Epoch: 6700,  Loss u: 6.237e-06, Loss pde: 1.037e-05, Loss_norm: 1.998e+00, Loss tot: 2.965e-05\n",
      "  coefs:  1.8035e-02   -3.3779e-03   -0.0000e+00    9.8807e-01   -9.8743e-01   -3.4009e-04   -9.6366e-04  \n",
      "Epoch: 6750,  Loss u: 6.528e-06, Loss pde: 1.035e-05, Loss_norm: 1.999e+00, Loss tot: 2.989e-05\n",
      "  coefs:  1.5565e-02   -1.9260e-03    0.0000e+00    9.9034e-01   -9.9057e-01   -2.1122e-04   -5.8485e-04  \n",
      "Epoch: 6800,  Loss u: 9.395e-05, Loss pde: 2.455e-05, Loss_norm: 1.980e+00, Loss tot: 1.216e-04\n",
      "  coefs: -5.5285e-04    3.3328e-03    1.5337e-03    9.8467e-01   -9.8424e-01   -4.1223e-03   -1.1387e-03  \n",
      "Epoch: 6850,  Loss u: 1.160e-05, Loss pde: 1.131e-05, Loss_norm: 2.009e+00, Loss tot: 3.526e-05\n",
      "  coefs:  1.0599e-02   -1.8568e-02   -2.6189e-03    9.8518e-01   -9.8642e-01    4.7592e-03    1.0129e-03  \n",
      "Epoch: 6900,  Loss u: 7.677e-06, Loss pde: 1.094e-05, Loss_norm: 1.995e+00, Loss tot: 3.102e-05\n",
      "  coefs:  1.2075e-02    1.6010e-04    4.5989e-04    9.9006e-01   -9.9014e-01   -1.3833e-03   -4.9693e-04  \n",
      "Epoch: 6950,  Loss u: 2.317e-05, Loss pde: 1.161e-05, Loss_norm: 1.995e+00, Loss tot: 4.667e-05\n",
      "  coefs:  6.3711e-03   -1.1510e-02    1.2880e-03    9.8803e-01   -9.8626e-01    1.1493e-03    3.0602e-04  \n",
      "Epoch: 7000,  Loss u: 7.935e-06, Loss pde: 1.109e-05, Loss_norm: 1.993e+00, Loss tot: 3.120e-05\n",
      "  coefs:  1.0635e-02    0.0000e+00    2.6460e-05    9.8987e-01   -9.9060e-01   -1.5446e-03   -6.8570e-04  \n",
      "Epoch: 7050,  Loss u: 4.483e-06, Loss pde: 9.700e-06, Loss_norm: 1.993e+00, Loss tot: 2.728e-05\n",
      "  coefs:  8.2591e-03   -2.6546e-03    2.0795e-04    9.9055e-01   -9.9095e-01   -5.3926e-05    0.0000e+00  \n",
      "Epoch: 7100,  Loss u: 3.557e-05, Loss pde: 1.577e-05, Loss_norm: 2.019e+00, Loss tot: 6.035e-05\n",
      "  coefs:  2.2794e-02   -9.2910e-03   -3.0131e-03    9.9052e-01   -9.9048e-01    2.8254e-03   -3.3610e-04  \n",
      "Epoch: 7150,  Loss u: 5.817e-06, Loss pde: 1.002e-05, Loss_norm: 1.987e+00, Loss tot: 2.855e-05\n",
      "  coefs:  7.0640e-03   -1.5191e-03    8.0636e-04    9.8840e-01   -9.8808e-01   -1.3362e-03   -1.7294e-04  \n",
      "Epoch: 7200,  Loss u: 1.289e-05, Loss pde: 1.116e-05, Loss_norm: 1.991e+00, Loss tot: 3.593e-05\n",
      "  coefs:  1.4774e-02   -8.9973e-03   -4.4967e-04    9.8214e-01   -9.8400e-01   -0.0000e+00   -3.8621e-04  \n",
      "Epoch: 7250,  Loss u: 4.155e-06, Loss pde: 9.720e-06, Loss_norm: 1.999e+00, Loss tot: 2.682e-05\n",
      "  coefs:  1.0365e-02   -6.2768e-03   -3.6153e-04    9.9040e-01   -9.9087e-01    4.3946e-04   -9.4757e-05  \n",
      "Epoch: 7300,  Loss u: 4.223e-06, Loss pde: 9.787e-06, Loss_norm: 1.994e+00, Loss tot: 2.681e-05\n",
      "  coefs:  8.0725e-03   -2.5634e-03    2.2828e-04    9.9085e-01   -9.9148e-01   -5.1293e-04   -2.4103e-04  \n",
      "Epoch: 7350,  Loss u: 1.126e-05, Loss pde: 1.345e-05, Loss_norm: 2.001e+00, Loss tot: 3.485e-05\n",
      "  coefs:  9.1265e-03   -1.5653e-02   -9.4697e-04    9.8497e-01   -9.8618e-01    3.4451e-03    7.7894e-04  \n",
      "Epoch: 7400,  Loss u: 6.104e-06, Loss pde: 1.037e-05, Loss_norm: 1.995e+00, Loss tot: 2.875e-05\n",
      "  coefs:  5.2182e-03   -7.3034e-03    4.2625e-04    9.9069e-01   -9.9002e-01    6.5436e-04    4.6043e-04  \n",
      "Epoch: 7450,  Loss u: 1.024e-05, Loss pde: 1.155e-05, Loss_norm: 1.997e+00, Loss tot: 3.317e-05\n",
      "  coefs:  6.3123e-03   -1.5011e-02   -4.9639e-04    9.8649e-01   -9.8618e-01    2.1910e-03    6.3905e-04  \n",
      "Epoch: 7500,  Loss u: 3.784e-06, Loss pde: 9.838e-06, Loss_norm: 1.996e+00, Loss tot: 2.621e-05\n",
      "  coefs:  9.2516e-03   -3.1919e-03    9.3580e-05    9.9191e-01   -9.9158e-01   -1.3232e-04   -5.6253e-05  \n",
      "Epoch: 7550,  Loss u: 3.296e-06, Loss pde: 9.453e-06, Loss_norm: 1.994e+00, Loss tot: 2.556e-05\n",
      "  coefs:  6.4341e-03   -4.3063e-03    1.1381e-04    9.9130e-01   -9.9188e-01    0.0000e+00   -0.0000e+00  \n",
      "Epoch: 7600,  Loss u: 2.022e-05, Loss pde: 1.633e-05, Loss_norm: 2.005e+00, Loss tot: 4.420e-05\n",
      "  coefs:  1.3966e-02    6.2572e-03    1.7245e-03    9.9031e-01   -9.9032e-01   -1.8746e-03   -5.3718e-05  \n",
      "Epoch: 7650,  Loss u: 4.821e-06, Loss pde: 1.034e-05, Loss_norm: 2.001e+00, Loss tot: 2.727e-05\n",
      "  coefs:  4.7379e-03   -8.2149e-03    0.0000e+00    9.9287e-01   -9.9346e-01    1.4720e-03    3.1818e-04  \n",
      "Epoch: 7700,  Loss u: 3.316e-06, Loss pde: 9.472e-06, Loss_norm: 1.995e+00, Loss tot: 2.545e-05\n",
      "  coefs:  5.8155e-03   -4.3106e-03    1.9432e-04    9.9199e-01   -9.9211e-01    2.4937e-04    0.0000e+00  \n",
      "Epoch: 7750,  Loss u: 2.009e-05, Loss pde: 1.254e-05, Loss_norm: 2.003e+00, Loss tot: 4.295e-05\n",
      "  coefs:  1.5320e-02   -9.5352e-03   -1.8977e-03    9.8712e-01   -9.8764e-01    1.7342e-03   -9.4057e-05  \n",
      "Epoch: 7800,  Loss u: 3.493e-06, Loss pde: 9.729e-06, Loss_norm: 1.996e+00, Loss tot: 2.560e-05\n",
      "  coefs:  8.6382e-03   -4.6516e-03   -0.0000e+00    9.9133e-01   -9.9113e-01   -0.0000e+00    8.5812e-05  \n",
      "Epoch: 7850,  Loss u: 3.055e-06, Loss pde: 9.522e-06, Loss_norm: 1.995e+00, Loss tot: 2.506e-05\n",
      "  coefs:  6.7449e-03   -3.5406e-03    7.6911e-05    9.9222e-01   -9.9263e-01    0.0000e+00    0.0000e+00  \n",
      "Epoch: 7900,  Loss u: 2.917e-06, Loss pde: 9.466e-06, Loss_norm: 1.996e+00, Loss tot: 2.487e-05\n",
      "  coefs:  6.2328e-03   -3.4945e-03    1.0675e-04    9.9259e-01   -9.9319e-01    0.0000e+00   -0.0000e+00  \n",
      "Epoch: 7950,  Loss u: 1.180e-05, Loss pde: 1.234e-05, Loss_norm: 2.001e+00, Loss tot: 3.435e-05\n",
      "  coefs:  9.9788e-03   -4.1935e-03   -1.0723e-03    9.9241e-01   -9.9309e-01    2.4391e-05    0.0000e+00  \n",
      "Epoch: 8000,  Loss u: 3.130e-06, Loss pde: 9.632e-06, Loss_norm: 1.995e+00, Loss tot: 2.501e-05\n",
      "  coefs:  6.0347e-03   -2.5791e-03    9.2122e-05    9.9283e-01   -9.9287e-01   -1.4904e-04   -0.0000e+00  \n",
      "Epoch: 8050,  Loss u: 3.638e-06, Loss pde: 9.562e-06, Loss_norm: 1.995e+00, Loss tot: 2.546e-05\n",
      "  coefs:  4.8119e-03   -3.0233e-03    3.2095e-04    9.9321e-01   -9.9355e-01    0.0000e+00   -0.0000e+00  \n",
      "Epoch: 8100,  Loss u: 8.631e-06, Loss pde: 1.163e-05, Loss_norm: 2.002e+00, Loss tot: 3.087e-05\n",
      "  coefs:  3.2153e-03    4.4681e-03    1.5521e-03    9.9532e-01   -9.9361e-01   -3.0995e-03   -7.8831e-04  \n",
      "Epoch: 8150,  Loss u: 2.954e-06, Loss pde: 9.626e-06, Loss_norm: 1.995e+00, Loss tot: 2.470e-05\n",
      "  coefs:  6.4450e-03   -3.1527e-03    8.4825e-05    9.9274e-01   -9.9298e-01    0.0000e+00    0.0000e+00  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 8200,  Loss u: 2.782e-06, Loss pde: 9.586e-06, Loss_norm: 1.995e+00, Loss tot: 2.447e-05\n",
      "  coefs:  5.7632e-03   -2.9633e-03    9.2328e-05    9.9304e-01   -9.9355e-01   -0.0000e+00    0.0000e+00  \n",
      "Epoch: 8250,  Loss u: 4.806e-06, Loss pde: 1.023e-05, Loss_norm: 1.997e+00, Loss tot: 2.658e-05\n",
      "  coefs:  7.2838e-03   -1.6631e-03    1.2943e-03    9.9356e-01   -9.9196e-01   -1.2118e-03   -1.4741e-04  \n",
      "Epoch: 8300,  Loss u: 3.430e-06, Loss pde: 9.597e-06, Loss_norm: 1.997e+00, Loss tot: 2.504e-05\n",
      "  coefs:  5.1351e-03   -6.3015e-03    0.0000e+00    9.9212e-01   -9.9275e-01    9.3525e-04    1.0501e-04  \n",
      "Epoch: 8350,  Loss u: 8.528e-06, Loss pde: 1.036e-05, Loss_norm: 1.997e+00, Loss tot: 3.021e-05\n",
      "  coefs:  8.5129e-03   -0.0000e+00    4.9216e-04    9.9359e-01   -9.9284e-01   -1.3117e-03   -6.5198e-05  \n",
      "Epoch: 8400,  Loss u: 2.726e-06, Loss pde: 9.606e-06, Loss_norm: 1.996e+00, Loss tot: 2.423e-05\n",
      "  coefs:  5.5670e-03   -4.3355e-03    0.0000e+00    9.9275e-01   -9.9327e-01    3.1360e-04    0.0000e+00  \n",
      "Epoch: 8450,  Loss u: 2.566e-06, Loss pde: 9.636e-06, Loss_norm: 1.996e+00, Loss tot: 2.403e-05\n",
      "  coefs:  4.9827e-03   -2.5509e-03    1.6828e-04    9.9382e-01   -9.9435e-01    0.0000e+00    0.0000e+00  \n",
      "Epoch: 8500,  Loss u: 1.206e-05, Loss pde: 1.161e-05, Loss_norm: 2.005e+00, Loss tot: 3.386e-05\n",
      "  coefs:  1.4426e-02   -6.0382e-03   -1.1536e-03    9.9154e-01   -9.9145e-01    3.9389e-04   -0.0000e+00  \n",
      "Epoch: 8550,  Loss u: 2.963e-06, Loss pde: 9.903e-06, Loss_norm: 1.997e+00, Loss tot: 2.438e-05\n",
      "  coefs:  7.0182e-03   -2.7158e-03    0.0000e+00    9.9375e-01   -9.9390e-01    0.0000e+00   -8.5278e-05  \n",
      "Epoch: 8600,  Loss u: 2.453e-06, Loss pde: 9.789e-06, Loss_norm: 1.996e+00, Loss tot: 2.379e-05\n",
      "  coefs:  5.0010e-03   -2.9126e-03    1.1918e-04    9.9386e-01   -9.9437e-01    0.0000e+00    0.0000e+00  \n",
      "Epoch: 8650,  Loss u: 4.333e-05, Loss pde: 2.067e-05, Loss_norm: 2.013e+00, Loss tot: 6.627e-05\n",
      "  coefs:  1.1180e-02    1.2938e-02    2.7572e-04    9.8955e-01   -9.9152e-01   -5.5679e-03   -2.2219e-03  \n",
      "Epoch: 8700,  Loss u: 3.051e-06, Loss pde: 1.025e-05, Loss_norm: 1.996e+00, Loss tot: 2.435e-05\n",
      "  coefs:  5.8369e-03   -1.2471e-04    3.2331e-04    9.9401e-01   -9.9411e-01   -1.0455e-03   -2.8590e-04  \n",
      "Epoch: 8750,  Loss u: 8.715e-06, Loss pde: 1.106e-05, Loss_norm: 2.007e+00, Loss tot: 3.018e-05\n",
      "  coefs:  1.2686e-02   -5.7634e-03   -2.9044e-04    9.9294e-01   -9.9393e-01    1.0502e-03   -1.3978e-04  \n",
      "Epoch: 8800,  Loss u: 2.632e-06, Loss pde: 1.008e-05, Loss_norm: 1.997e+00, Loss tot: 2.382e-05\n",
      "  coefs:  5.6684e-03   -1.7411e-03    8.7342e-05    9.9454e-01   -9.9496e-01   -1.9151e-05   -9.2737e-05  \n",
      "Epoch: 8850,  Loss u: 1.224e-05, Loss pde: 1.098e-05, Loss_norm: 2.001e+00, Loss tot: 3.352e-05\n",
      "  coefs:  1.1592e-02   -3.5326e-03   -2.0822e-04    9.9232e-01   -9.9236e-01    5.4253e-04   -6.0712e-04  \n",
      "Epoch: 8900,  Loss u: 2.440e-06, Loss pde: 1.008e-05, Loss_norm: 1.997e+00, Loss tot: 2.353e-05\n",
      "  coefs:  4.9883e-03   -2.5866e-03    2.8826e-05    9.9435e-01   -9.9496e-01    0.0000e+00   -0.0000e+00  \n",
      "Epoch: 8950,  Loss u: 3.857e-05, Loss pde: 1.074e-05, Loss_norm: 2.000e+00, Loss tot: 5.970e-05\n",
      "  coefs:  6.8760e-03   -3.0329e-03   -8.3161e-04    9.9382e-01   -9.9428e-01    1.6342e-04    7.1334e-04  \n",
      "Epoch: 9000,  Loss u: 4.275e-06, Loss pde: 1.067e-05, Loss_norm: 1.999e+00, Loss tot: 2.534e-05\n",
      "  coefs:  5.1468e-03   -1.5712e-03    4.4054e-04    9.9578e-01   -9.9584e-01   -3.0778e-04   -1.2579e-04  \n",
      "Epoch: 9050,  Loss u: 2.165e-06, Loss pde: 1.029e-05, Loss_norm: 1.997e+00, Loss tot: 2.312e-05\n",
      "  coefs:  3.8588e-03   -2.5550e-03    1.3813e-04    9.9476e-01   -9.9519e-01    0.0000e+00    0.0000e+00  \n",
      "Epoch: 9100,  Loss u: 2.229e-05, Loss pde: 1.459e-05, Loss_norm: 1.997e+00, Loss tot: 4.358e-05\n",
      "  coefs:  1.2390e-02    1.4185e-03   -5.6051e-04    9.8915e-01   -9.9089e-01   -1.8256e-03   -7.2007e-04  \n",
      "Epoch: 9150,  Loss u: 2.266e-06, Loss pde: 1.041e-05, Loss_norm: 1.998e+00, Loss tot: 2.314e-05\n",
      "  coefs:  4.3757e-03   -2.2455e-03    1.6212e-04    9.9540e-01   -9.9577e-01   -0.0000e+00   -6.7623e-05  \n",
      "Epoch: 9200,  Loss u: 2.297e-05, Loss pde: 1.733e-05, Loss_norm: 1.989e+00, Loss tot: 4.426e-05\n",
      "  coefs: -1.2169e-04    3.9269e-03    7.9814e-04    9.8912e-01   -9.9094e-01   -3.5112e-03   -2.4728e-04  \n",
      "Epoch: 9250,  Loss u: 2.659e-06, Loss pde: 1.088e-05, Loss_norm: 1.996e+00, Loss tot: 2.344e-05\n",
      "  coefs:  3.4726e-03   -8.7860e-04    3.9290e-04    9.9508e-01   -9.9522e-01   -5.6484e-04   -4.1716e-05  \n",
      "Epoch: 9300,  Loss u: 5.366e-05, Loss pde: 1.258e-05, Loss_norm: 1.989e+00, Loss tot: 7.445e-05\n",
      "  coefs:  6.1751e-03   -5.6225e-04   -5.8013e-04    9.9049e-01   -9.8976e-01   -1.1482e-03    7.2322e-04  \n",
      "Epoch: 9350,  Loss u: 2.409e-06, Loss pde: 1.100e-05, Loss_norm: 1.997e+00, Loss tot: 2.311e-05\n",
      "  coefs:  2.7722e-03   -3.0406e-03    2.3262e-04    9.9546e-01   -9.9565e-01    1.6356e-04    0.0000e+00  \n",
      "Epoch: 9400,  Loss u: 1.947e-06, Loss pde: 1.090e-05, Loss_norm: 1.997e+00, Loss tot: 2.258e-05\n",
      "  coefs:  3.4582e-03   -1.8560e-03    1.6561e-04    9.9553e-01   -9.9611e-01   -0.0000e+00    0.0000e+00  \n",
      "Epoch: 9450,  Loss u: 1.875e-06, Loss pde: 1.099e-05, Loss_norm: 1.998e+00, Loss tot: 2.247e-05\n",
      "  coefs:  3.4857e-03   -1.7585e-03    1.4871e-04    9.9569e-01   -9.9648e-01    0.0000e+00    4.4597e-06  \n",
      "Epoch: 9500,  Loss u: 3.219e-06, Loss pde: 1.279e-05, Loss_norm: 1.998e+00, Loss tot: 2.385e-05\n",
      "  coefs:  3.5412e-03   -2.7282e-03    2.4416e-04    9.9572e-01   -9.9556e-01   -0.0000e+00    1.6138e-05  \n",
      "Epoch: 9550,  Loss u: 1.949e-06, Loss pde: 1.145e-05, Loss_norm: 1.997e+00, Loss tot: 2.244e-05\n",
      "  coefs:  2.9377e-03   -2.1374e-03    1.7645e-04    9.9576e-01   -9.9593e-01   -0.0000e+00    2.5627e-06  \n",
      "Epoch: 9600,  Loss u: 1.811e-06, Loss pde: 1.144e-05, Loss_norm: 1.998e+00, Loss tot: 2.226e-05\n",
      "  coefs:  3.2637e-03   -1.7136e-03    2.0143e-04    9.9584e-01   -9.9652e-01   -0.0000e+00    2.8133e-05  \n",
      "Epoch: 9650,  Loss u: 4.214e-05, Loss pde: 2.536e-05, Loss_norm: 2.013e+00, Loss tot: 6.319e-05\n",
      "  coefs: -1.0669e-03   -2.0466e-02   -0.0000e+00    9.9157e-01   -9.9141e-01    5.7794e-03    3.0209e-03  \n",
      "Epoch: 9700,  Loss u: 2.126e-06, Loss pde: 1.195e-05, Loss_norm: 1.998e+00, Loss tot: 2.247e-05\n",
      "  coefs:  3.6344e-03   -6.3313e-04    2.5298e-04    9.9621e-01   -9.9679e-01   -1.6580e-04   -9.6087e-05  \n",
      "Epoch: 9750,  Loss u: 5.575e-06, Loss pde: 1.282e-05, Loss_norm: 2.004e+00, Loss tot: 2.595e-05\n",
      "  coefs:  5.7780e-03   -4.2581e-03    0.0000e+00    9.9633e-01   -9.9695e-01    5.3426e-04    2.5249e-05  \n",
      "Epoch: 9800,  Loss u: 1.755e-06, Loss pde: 1.231e-05, Loss_norm: 1.997e+00, Loss tot: 2.199e-05\n",
      "  coefs:  2.5205e-03   -1.3477e-03    2.3491e-04    9.9622e-01   -9.9684e-01   -6.1826e-05    2.4655e-05  \n",
      "Epoch: 9850,  Loss u: 2.075e-06, Loss pde: 1.254e-05, Loss_norm: 1.998e+00, Loss tot: 2.225e-05\n",
      "  coefs:  2.0590e-03   -1.4153e-03    4.1012e-04    9.9642e-01   -9.9733e-01   -0.0000e+00    6.2664e-05  \n",
      "Epoch: 9900,  Loss u: 4.686e-06, Loss pde: 1.438e-05, Loss_norm: 2.003e+00, Loss tot: 2.488e-05\n",
      "  coefs:  4.5282e-03   -3.1409e-03    6.0024e-05    9.9736e-01   -9.9807e-01    1.7328e-04   -2.2048e-05  \n",
      "Epoch: 9950,  Loss u: 1.662e-06, Loss pde: 1.339e-05, Loss_norm: 1.998e+00, Loss tot: 2.172e-05\n",
      "  coefs:  2.2212e-03   -1.1495e-03    2.9611e-04    9.9643e-01   -9.9725e-01   -1.9439e-04    4.2487e-05  \n",
      "CPU times: user 2h 6min 49s, sys: 2min 13s, total: 2h 9min 3s\n",
      "Wall time: 3min 23s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "params = {'n_epochs':10000,\n",
    "          'alpha_pde_start':1,\n",
    "           'alpha_pde_end':0.001,\n",
    "           'alpha_l1':1e-5, \n",
    "          'warmup_nsteps':500,\n",
    "          'linearRegInterval':5,\n",
    "          'linearRegression':True,\n",
    "          'width':50,\n",
    "          'layers':8,\n",
    "          'lr':0.002,\n",
    "          'update_coef_in_dl':False,\n",
    "          'logfile':'alpha_decrease.txt'}\n",
    "\n",
    "model = learning(inputs=inputs, u=u, dictionary=dictionary,**params)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "log = pd.read_csv('alpha_decrease.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Epoch</th>\n",
       "      <th>Loss_u</th>\n",
       "      <th>Loss_pde</th>\n",
       "      <th>Loss_l1</th>\n",
       "      <th>Loss_tot</th>\n",
       "      <th>p0</th>\n",
       "      <th>p1</th>\n",
       "      <th>p2</th>\n",
       "      <th>p3</th>\n",
       "      <th>p4</th>\n",
       "      <th>p5</th>\n",
       "      <th>p6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.138140</td>\n",
       "      <td>0.000982</td>\n",
       "      <td>7.4879</td>\n",
       "      <td>0.138140</td>\n",
       "      <td>-0.563040</td>\n",
       "      <td>-2.897000</td>\n",
       "      <td>0.143700</td>\n",
       "      <td>-0.62471</td>\n",
       "      <td>-1.48660</td>\n",
       "      <td>1.350000</td>\n",
       "      <td>0.422880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.130960</td>\n",
       "      <td>0.000563</td>\n",
       "      <td>7.4879</td>\n",
       "      <td>0.130960</td>\n",
       "      <td>-0.563040</td>\n",
       "      <td>-2.897000</td>\n",
       "      <td>0.143700</td>\n",
       "      <td>-0.62471</td>\n",
       "      <td>-1.48660</td>\n",
       "      <td>1.350000</td>\n",
       "      <td>0.422880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.119430</td>\n",
       "      <td>0.000707</td>\n",
       "      <td>7.4879</td>\n",
       "      <td>0.119430</td>\n",
       "      <td>-0.563040</td>\n",
       "      <td>-2.897000</td>\n",
       "      <td>0.143700</td>\n",
       "      <td>-0.62471</td>\n",
       "      <td>-1.48660</td>\n",
       "      <td>1.350000</td>\n",
       "      <td>0.422880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.099913</td>\n",
       "      <td>0.004417</td>\n",
       "      <td>7.4879</td>\n",
       "      <td>0.099913</td>\n",
       "      <td>-0.563040</td>\n",
       "      <td>-2.897000</td>\n",
       "      <td>0.143700</td>\n",
       "      <td>-0.62471</td>\n",
       "      <td>-1.48660</td>\n",
       "      <td>1.350000</td>\n",
       "      <td>0.422880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.073070</td>\n",
       "      <td>0.022871</td>\n",
       "      <td>7.4879</td>\n",
       "      <td>0.073070</td>\n",
       "      <td>-0.563040</td>\n",
       "      <td>-2.897000</td>\n",
       "      <td>0.143700</td>\n",
       "      <td>-0.62471</td>\n",
       "      <td>-1.48660</td>\n",
       "      <td>1.350000</td>\n",
       "      <td>0.422880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>9995</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>1.9979</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.002407</td>\n",
       "      <td>-0.000973</td>\n",
       "      <td>0.000287</td>\n",
       "      <td>0.99642</td>\n",
       "      <td>-0.99755</td>\n",
       "      <td>-0.000182</td>\n",
       "      <td>0.000129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>9996</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>1.9979</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.002407</td>\n",
       "      <td>-0.000973</td>\n",
       "      <td>0.000287</td>\n",
       "      <td>0.99642</td>\n",
       "      <td>-0.99755</td>\n",
       "      <td>-0.000182</td>\n",
       "      <td>0.000129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>9997</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>1.9979</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.002407</td>\n",
       "      <td>-0.000973</td>\n",
       "      <td>0.000287</td>\n",
       "      <td>0.99642</td>\n",
       "      <td>-0.99755</td>\n",
       "      <td>-0.000182</td>\n",
       "      <td>0.000129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>9998</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>1.9979</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.002407</td>\n",
       "      <td>-0.000973</td>\n",
       "      <td>0.000287</td>\n",
       "      <td>0.99642</td>\n",
       "      <td>-0.99755</td>\n",
       "      <td>-0.000182</td>\n",
       "      <td>0.000129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>9999</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>1.9979</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.002407</td>\n",
       "      <td>-0.000973</td>\n",
       "      <td>0.000287</td>\n",
       "      <td>0.99642</td>\n",
       "      <td>-0.99755</td>\n",
       "      <td>-0.000182</td>\n",
       "      <td>0.000129</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Epoch    Loss_u  Loss_pde  Loss_l1  Loss_tot        p0        p1  \\\n",
       "0         0  0.138140  0.000982   7.4879  0.138140 -0.563040 -2.897000   \n",
       "1         1  0.130960  0.000563   7.4879  0.130960 -0.563040 -2.897000   \n",
       "2         2  0.119430  0.000707   7.4879  0.119430 -0.563040 -2.897000   \n",
       "3         3  0.099913  0.004417   7.4879  0.099913 -0.563040 -2.897000   \n",
       "4         4  0.073070  0.022871   7.4879  0.073070 -0.563040 -2.897000   \n",
       "...     ...       ...       ...      ...       ...       ...       ...   \n",
       "9995   9995  0.000002  0.000014   1.9979  0.000022  0.002407 -0.000973   \n",
       "9996   9996  0.000002  0.000014   1.9979  0.000022  0.002407 -0.000973   \n",
       "9997   9997  0.000002  0.000014   1.9979  0.000022  0.002407 -0.000973   \n",
       "9998   9998  0.000002  0.000014   1.9979  0.000022  0.002407 -0.000973   \n",
       "9999   9999  0.000002  0.000014   1.9979  0.000022  0.002407 -0.000973   \n",
       "\n",
       "            p2       p3       p4        p5        p6  \n",
       "0     0.143700 -0.62471 -1.48660  1.350000  0.422880  \n",
       "1     0.143700 -0.62471 -1.48660  1.350000  0.422880  \n",
       "2     0.143700 -0.62471 -1.48660  1.350000  0.422880  \n",
       "3     0.143700 -0.62471 -1.48660  1.350000  0.422880  \n",
       "4     0.143700 -0.62471 -1.48660  1.350000  0.422880  \n",
       "...        ...      ...      ...       ...       ...  \n",
       "9995  0.000287  0.99642 -0.99755 -0.000182  0.000129  \n",
       "9996  0.000287  0.99642 -0.99755 -0.000182  0.000129  \n",
       "9997  0.000287  0.99642 -0.99755 -0.000182  0.000129  \n",
       "9998  0.000287  0.99642 -0.99755 -0.000182  0.000129  \n",
       "9999  0.000287  0.99642 -0.99755 -0.000182  0.000129  \n",
       "\n",
       "[10000 rows x 12 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.0, 1.0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(log['Epoch'].values, log['p3'].values)\n",
    "plt.plot([0,10000],[1,1])\n",
    "plt.ylim(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2ac6aff9b990>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(log['Epoch'], log['p4'])\n",
    "plt.plot([0,10000],[1,1])\n",
    "plt.ylim(-2,-0.4)\n",
    "plt.plot([0,10000],[-1,-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.96, 1.0)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CygkbTRSd+RcS6K6JqyqTXrTeeul8rjr70FDb+FHV8XUIBgBVAoqiBNhi58VC8S/9yfNcdudimjbHRxEBfP/xJu54Zg2vpSjYkjWw36RRfPbUAwAvCuexFZuZPqEOyAlwK4Rd8VgXU4FrfF3yLDquFnAS0d27DQWcwqcdknM+h3VAYWH+ntkNvXZ0lxJVAoqi5NFdJCHP0rUtADy0/J3EPjv2dCVei6OmMhPM6N94ZxedPdkgkVtO+FtlUDgEM84fscsvOjN1fG3qZ6qNjFMofPPi43I2/EpHC+zlksG9RpWAoigBNr1y2oIr335oJS+taynJZ+/nZOS88Id/AuCSEzyHcOALkPBviE/QFies7Tsd0JBz2haLgepNuKZrvnHvK3MdoEpAUZQcU8d55pfehIhecttzff68z5wyK/TZUXke5NGR8AogU0QJxDlw7S7hyl5sxuqrXd4N84z6K8oNVQKKogBeOgfrEH57a2ve9aS8PnYDWdIu40K4ArKmKpMn0CfVe1E/uagg8o7i5HScv9euBHoTh5+2PnCUjK4EFEUZalzwg2d5dYPnxLV1e298eCUvvO2FbbrlG6N86s7FPL6icLRQHK6AHFdXlbcSqK30HLPRdvc8bqY9qyF/85hdCYRMRRFl0VAfDjUtxe7dMl8IpFMCIrJQRFaKSJOIXBVzfaaIPCYiL4vIkyIy3bn2LRF51f/5sNM+S0Se98f8lYjEB/oqirJXcKN4tuzu5MRvPMb3Hm/ig7d49vnOAn6Cx17fXPB6Eu5+hMpMfoKFGj/Ov1B0UJycjhaDh1zYZ1Ks/w8uOYa7PnlCqK0UdX7Tpo0YLIoqARGpAG4GzsYrGn+xiESLx98A3GWMmQtcC1zv3/t+4BhgHnACcKWI2CTe3wJuMsYcBGwHPtX/11EUpRT8dumGIEUyeKaiQvl2IN6EFMdkfy/A8bMm8pulG4L2yopM3qzebqDKXwkUrtw1OiYn/54gJUa8snrfkVPzagwM5ErguxfN48YPHdXv8ftLmpXA8UCTMWa1MaYTuAc4N9JnDvC4f/yEc30O8EdjTLcxphV4GVgo3jd4BnCf3++nwHl9fw1FUQaSK365tGgtX7vb1/K3p8fHvldkhN/97cnc9OF5oXa3aHsSuU1juTarBNz8QYX8BJPH5IeIXvneg2M/r7c6YJ+x6cNPz503jQuOmV684wCTRglMA9Y55+v9NpdlwAX+8fnAGBGZ5LcvFJFRItIAnA7MACYBLcaY7gJjAiAil4vIEhFZ0tzcnOadFEUpMY+ueJdRRcoevr21LXR+9hFTY/tlRDhqxnimja8LtVdWSKLpJJouwu1nFcNXPuAZKOprKmNXB13ZZMfwgoPzzUfe2L3TAj//9Ancf8XJkTF6NcRep1SO4SuBBSKyFFgAbAB6jDEPA4uAZ4G7gT8BxXPROhhjbjXGzDfGzG9sbCx+g6IoIRav2cZNj7zR73GKpZEeFdm5myT8kjIqVGYyiTPvQo5hK/BtimdJ+Gy7EqhyNoDZDW2lEtQHTa5n7vTxkWctby2QJoHcBrzZu2W63xZgjNmIvxIQkXrgg8aYFv/adcB1/rVfAm8AW4HxIlLprwbyxlQUpTR85q4ltLR1Maq6gjPnTOHAxvyqVq+/Uzy1QzG6U+4tSHK2Ru3vbklIK0jjbrW3FdtAZnHz+iTtAL7h/xzFb5euTxyjN5S3Cki3ElgMzPajeaqBi4D73Q4i0iAidqyrgdv99grfLISIzAXmAg8bL2vTE8CF/j0fB37X35dRFCWfFj8d8/UPvM6Vv14W2+cjt/W/elc0OijJhVAo9v7Pa3J+hZ984rjEfgUFvhSe2cdFB0VXMRceO51ffPrE5EF6QZkvBIorAX+mfgXwELACuNcYs1xErhWRc/xupwErReQNYAr+zB+oAp4WkdeAW4GPOn6ALwNfFJEmPB/BT0r0ToqiJLB8Q/yMv1jkTylJuwvXXbHky/l8n4CENpAlf0ZcXqFiJpsLjp4W1CCIctbhUwrea5+rrohPZbBIVU/AGLMIz7bvtn3VOb6PXKSP26cdL0IobszVeJFHiqLsJbqzfSsW0xeSVgKFdEBS0ZacQ9gfw+kWZyoqJNNrHCVgd0gX00s3RiKZQtc+NI/tbclK1D7L3q4YlhbdMawoI4hiKaJLSbQmwafe4+UJKuQ76El4wEIrgTgK5fN3fQ/24/qzKWx0TSXTJ4xKvG5HrihQkGYwUSWgKGXColc2FUzNUIybn2jib3/5YgmfqH9EVwJ2JhwtJenSk7B8CMRnAQex2+bW+I3i+iTsxw2o3V7yP7ecUCWgKIPMrvYujDF8/hcv8sOnVhW/IYFvP7SS/3l5UwmfrO/84JJj8tI0W1/APuOSN1RlU68EkhEKZwoN2/9NTFtpsYpmb1UK6y2qBBRlENnT2cPcrz/Mdf+zImhLKnnYF9ZsyU/lsPA//khLARt2bzhi2tjY9tqqTN4OY7sSKDQjjqsSBjnzj/23iV0JRFTDQZPr+Ye/nJ3XzzUHBQJ6AOWzNXHpSkBRlDy2t3ViDPz4f98K2tqKbMoqxgonEdzdi9fmXX/9nV0s39j/fQEA08fH28LjbPZx9YGrInbyz592UPx4fretuzv98/zxo00Pf+FU/vGv8tNBuALfBG0DJ6Dt2NG8ROVCquggRVEGhrjQzM7uLKOTS9mG+PrvlzNz4ig+cfKsoO2CHzzLin9dCMDmnR2x99VW5eZ/SXUC0pAkO0XgiH3HxfZ1Ba53nFsxzJiY7GAFgprDhZ/JGz/J/JIJrQT8lUXRUcNcffahjIpJUhfHlLE1fO2v5/Dew/fp5afsHXQloCiDSEd3/qz/+gc809CrG3bw3UffLHj/Hc+s4V9+/1qozaZMvua3r/DbpfEb8Z9t2sqXfr0MYwx/f8/S2D7HzpxQ9PmTlYDkxeNH00EXuj9uPLd/bUzMfVpBHnIMR8ZPy2cXHMilJ85M1VdEuOzkWXm5ksoFVQKKMojEhUveu8RLV/D39yzlpkffKFr0PYlfPJ9vCrJ855E3+PUL63lt087EzJdpfBPFEr6F2mJWAmlz7Qf7A/x7a2I2fEU/Jwn38/eGT6DcUSWgKINIUly8MYbVzZ5T1wBL127n5G8+zvrtbbH9o9z952QF4JLNwuG+2WZ+ZOafyjeRMuGb27VvK4Hw77icP2nHcrcQWOf1QPoEyh1VAooyiHQnKIGHlr8bHBsDP39uLRta9vCnVVtTjZtWCXRns3T1ZDlqxvg8wdo4JqVjIoY4oZoT5O5KIB2BAvGPog7luL5JxEXpjGAdoEpAUQaTpDQOn/v5C8FxS1sn//WiZyJyBWjT5l2J43alzOjZvKuDddvbqKnMBILQ5rgplAoBvPz9SbIzrn1bq5fIrsvZLJbWFh/1CcRn/0w3VmVMjeFyT/c8kKgSUJRBJE365U/ftSQ4tqLKGMOZN/4x8Z6kEopRKjKCMbBzT1cgYCeP9VYAW3YlK4G7P3Min3rPrGThGdNsI3vcdBJxdzfU1/DeOeGkbNGPicvD0xc5njMH9f7e4YIqAUUZRHa1dxft8/L6HcGxtWfb9NBJpHUmZw28taWVw/cdF5habA3g3R3hZ7vi9IOCHP82HXOS7IwzB9lNWqHFT8wAj37xVL538dGhNlsf+Ok3t3i3FZD4vZnV9zU6aDihSkBRBhEbzpkWK6iLzfSjAjyJpWu3A1BXnRMFU8d5M/Y4U1U04VovFgLBbLtlT2fBfuNHVeeFgK5L4RDvixgfW1vV53uHC6oEFGUQKeTgjMOaUrqKpANN6xN40VcCFx23XyDQG+pryAhc5mxAs9hoJmuOSfQJxGgHu0nLjZdPOwPv6h6Y9KffunAupx3SWLa5/vcGumNYUQaR3m4BGF3t/S/bVSATJ6T3CVih7iZ1G1NbySv/chajqiu45clcQjuRnA3dmnaShHicjd1G5YR3DKd6TKYUSDqXe77Cgx01fRzLHNMawIKDG1lw8MiuXa5KQFEGkZ5eFnlp3u2lgYiaaqIbu2yM/+WnHsCtf1ydOF5rh9ev3kmBIOLlyI/S2ZPNJUMrIr3jawEXVhyFSFOQxfZIqpJ228fnp/LBjDRUCSjKIJK0TyAJa7bY2NIeak8apljqh7e3ehvSaiozzu7ZBIFr4lYCSSMnm4NceT4Qm7T2S8g/NHlMLZPHlPzjhjypfAIislBEVopIk4hcFXN9pog8JiIvi8iTIjLdufbvIrJcRFaIyPfEnwb4/VaKyEv+z+TSvZaiDA2iO4bH1lby5JWnFez/xMrNfOz2P4fa73jmrdj+p85OZ+oQkaKlFg25qKRACUSEvU3nEDeGbQuljSihDuiO+CuUdBRVAiJSAdwMnI1XL/hiEYnWDb4BuMsYMxe4Frjev/cvgJOBucARwHHAAue+S4wx8/yfzf19GUUZakSVwCUnzmT/htGJ/bPG8PM/vZ3X/m9OPQLLAQ2jE/PzW1o7exgd6ZNkrnFNThUJ0UE2qiduDKs4ClR+LMpnTz0g8VrgtC7TMo7lSpqv43igyRiz2hjTCdwDnBvpMwd43D9+wrlugFqgGqgBqoB3URQFyC+nWGwS25OFmqp0UrTGF8inH1J4NdDq+w+S8hhZdnf0BCYdq1yi+xFsVtS4fQo2Yilss++dwI7fKexh/SQV/dEyI5A0/1rTgHXO+Xq/zWUZcIF/fD4wRkQmGWP+hKcUNvk/Dxlj3CnLHb4p6CuSMP0QkctFZImILGlubk7xuIoydFi3bU/o3JpXjt5vfKh97nQvyVuPMXm1e5Po8PcgTCxSnGDGRC9kc4tfsKU1YY9B45iaYDVgTTpRn4YtuF4fqfH73YvmBaUj3aLsJTUH9ag5qC+USmVeCSwQkaV45p4NQI+IHAQcBkzHUxxniMgp/j2XGGOOBE7xfy6NG9gYc6sxZr4xZn5j48gO5VIGhh1tXZx78zNBzPzeZPyoqtC5lV+HTAl7MKf6IZLGGNZuS5dJdJw/drGU0D+97HgAmjbvBpJrAGfE3Szm/Z4wqjrUJ3AcR6T7ufOmsctXLnuc7KT9Edc/uOQY7v3sScH5xNHes5x52JSkW5QY0iiBDcAM53y63xZgjNlojLnAGHM0cI3f1oK3KnjOGLPbGLMbeAA4yb++wf+9C/glntlJUfY6z67awrJ1Lfz46eRQyoEiWlg9mijNUl3pmV96siZ1cZLxdb4SKNIvWvzF5aLjcv/rZ0TyVgJ5M/kCCdka6j0h7Vb26s9K4H1HTuX4WROD8xkTR/HgF07h86cf2PdBRyBplMBiYLaIzBKRauAi4H63g4g0iIgd62rgdv94Ld4KoVJEqvBWCSv88wb/3irgA8Cr/X8dRek9dvdtf/PHtHZ093o1ES3GnvQI1sTRkzVFhbrF2sajnxGlkJ3ddVJXZCS3EkjYMVwoIZv9HHdvRNqiMmk5dJ+xBd9Hyafov5Yxphu4AngIWAHca4xZLiLXisg5frfTgJUi8gYwBbjOb78PWAW8guc3WGaM+T2ek/ghEXkZeAlvZXFbyd5KUXrBbn8DUX9j1q9/YAUX/OBZtidsVooj6j9N2lBl24sJdBerOIrdEhWaSf3Pnbev8zz516eNryuYkK2h3vNNzHaC9RvGVOf1i0Ot/ANHqs1ixphFwKJI21ed4/vwBH70vh7gszHtrcCxvX1YRRkIdnd4se+bWvYU6VmYR1/zopzbY+oGJ5G3Eoj8tlg53Zs0EzZUspjiKFSq0fLZUw8IOXSjyuqAxtH89LLjueTHz/vX88c4bv8J3PyRYzjNiVa6/vy5/PV//m/Rzx+YzEEKaAI5RaHSN5tM7WchcGuocU0cb29t5aZH3kh0zkYFdCYhssUK3Y0te1JHB6VdCURTRBTbWwD5aSM+esJMZkwcFbxPnJlHRHj/3KmhzzvSj3pSBg9VAsqIx8bH98bUEkc2cIrm2j77sxf47mNvsn57/CqjJ2uYNDrfJBK1ptjZ+ti6Sjbvas/rX+h50nsRPM45Kmf2SfoniT6f7WZi/g1KgZqDBg5VAsqI4w8vbwxy5kAu1r1YKGUxAgHotNlwzmhWT2MMf35rGx3dWTIZCYRmkl9CRKiq8KqApXV82o1bvcxRl5fL33uA8FJPbS0AACAASURBVKl9Tvvb/tvZ36VWAmoOGjg0gZwyoujuyXLFL5dyxLSx/OHvvC0rNlqlt8Iyn/woIysk2zrDfoLn39rGRbc+B8CUsbnNXEEx9gRzStbEZ/CsqcwwuqYytBvXFocptMI56/C+xdQnpY2wnzQQieGUgUFXAsqIwqZifnXDzqDNrgTWt6TbhJVEXNaFtk4v8iia3niDYx6qEHEyeHq/ozK0qydLRryZdnRvAXhpnk+d3cC+zkavXESRdx41O33iL/bnOx+a5/RPfrcoecLf/4zsAK0EVKUMHKoElBHFzj35KRGsT2BMTVXetb7g2uCtAI6mMnDFeGjzFPHx9x8+bgYZEbLG5OUbAk8IN9TX8OzVf8m8GV7KCWs12tXuRT/NnBROsXxA4+hQHYF7P3sS34/U9o1y/tFexphgU1vMc7jvUSpsSuzjnM1hSmlQc5AyorAzc5e3t3orgN46UF3au3qC2X5nTNUvd+QnV27myl8vC87dmf1OX2C7JqVTZjcwd/p4MiL0ZJNrB1hlEmTr9MewPoTobVGz0vz98wVs9N/kuvOP4IozDkrslzMHxT9jX/mLgxp44Z/PZFJ94TxISu/RlYAyoli2riWvbZyfXqE/PoFHV+SS477nW0/wbNOW0HVrJslmDf9y//LQtV1OwrbJY/Pz9uSEujdOnDkIcnl8grz9NkTUF81jaqN5itJLajuzH1VdyYGN9bkxImGoJs47XiJUAQwMqgSUEcWmnV54pVvg3aYgjpZs7A3tXeF7H34tnDHdyu3vPvYma7aGfQ+umSbYLOYIUXuYyXi5e3qyJi/xHOQXbYkmcWtpC/slNu1IF2paCPsJ0RBRdQwPHVQJKCOKLbs8QTh5TG7G3dntSa5i+fQLETV/RCNy7HlcvV83/31cdJA44ZhZv8Tj8ftPzIvsiYZtRjee7RNZZUyMUSR9xb7uJSfsB8CoFBvOlPJAfQLKiCKXfiEnpHMrgb4rgR17ukLn3VkTOGQhZybZ05WfUqIiNOuPCQ31fwfmIGOoyEheuodAgUiuv/fZ9txrmD25nrXb2jjxwElpXq0wkcf9wpkH89GTZjKqOr1oed+R+3BAQ33xjsqAoEpAGVFYp61r+tnlJ5BLsxL41eK1PP/WNm50QisBNu/qCJ1ns4bVza3OefKYlTErAZedgTIRVr6zi+6sISPCJ98zi/9+aaNzb/jmqDnIfsz8/SfywD+cQmWKTWdp988F9YkzElplpeEHl2gascFEzUHKiMKWOLS/gSANw7s7i9vIv/xfr/CbF0PlNNjT2cMtT64KtW1o2cPbTvGXQrK0IhQi6nHmnMlBm930tWV3B0ve3s7q5lYyGWG/ieGQTztMkM454rS1lbeMMakUgEuSiX98neeMrq/R+eRQRb85ZUSxwc8U6tbAtdFBvakn0NHdQ41f6OXSnzyfd338qOqgvCMU3rXrFka3j3C8E64ZF265p7M7z3RkzT0d/mpnd6RMpK0NcNSMcOnK/nDJifuxs72LDx4zvWRjKnsXVQLKiMIK1FYnjYM1A8WlY0hiV3s3NfUVfOfhlSx5O76QjFsGslBeIjeSJtgsFpN6wqW6MhOTz8f7bUNIbQUya6pZcHAj582bxmFTw6Ur+8PY2iq+vPDQko2n7H3UHKSMKFz7u10NWHt9bxLIdfVk6erJ8v3Hm2KvC+EQzELuhi27HX+ChH4B8emlo7V9Iac47I7i6op8x/Gcfcf2u4KaMrxQJaCMKFyHsBWW9nex6KBQRFGPyXMGR3EjhgqZg2Y5JRzj9gnELVAyInl2ertiqPOzgG739wX0MzlqycZQyhNVAsqIwhXk9jj6OwlXqHf2ZEM2/ygiXtlKOxsvNHTIMRxTXjIubLQik99qhzl2pudPaI4oqb7k87F+hbh0G8rwIJUSEJGFIrJSRJpE5KqY6zNF5DEReVlEnhSR6c61fxeR5SKyQkS+J/5ft4gcKyKv+GMG7YoykHTHKAE7Sy+mBHa6SqA7Gzhg4xBgzdZWRtV4s/JCpqZivoj9IonfID5ax64Ejpg2FoDJforq/kziG/1UDboDePhSVAmISAVwM3A2MAe4WETmRLrdANxljJkLXAtc79/7F8DJwFzgCOA4YIF/zy3AZ4DZ/s/C/r6MohSj0EqgmDnIDSHd3toZmFviEBG6ekwQeVTInFIRmvXnc9nJ++e1eeagcG97uvDwffjG+Ufy0RNneg39qPZlFVR/dlMr5U2alcDxQJMxZrUxphO4Bzg30mcO8Lh//IRz3QC1QDVQA1QB74rIVGCsMeY5402R7gLO69ebKEoK4lYC9ndc9k+Xdue6IRd3D/B//+pgDt93bHAueA7fKf7GqV3tXXzn4ZWx44bNQfnX43bfxpmDrFKorMjwkRP2y7uvL3N565SOS1+tDA/SKIFpwDrnfL3f5rIMuMA/Ph8YIyKTjDF/wlMKm/yfh4wxK/z71xcZEwARuVxElojIkubm5hSPqyjJrG7eHRzH1RZ2y0A+tuJddrTlTEC/fTH3J9vR3ROaHe87vi6Iw4dcJbHpE7wwzfau5EiiYkogjnhzUHzf/qTItquUpMylytCnVI7hK4EFIrIUz9yzAegRkYOAw4DpeEL+DBE5pTcDG2NuNcbMN8bMb2xsLNHjKiMVNx2xnd2uctI7WCXwzo52PvXTJVy36DXAS0Htpmh4d2cHnY7CiJpLbKqH6f6u3kKCOLxjOJ0WiIsOau1MdlRD7zbD5Z7N+10ouqkQ7587lU+/Z1af7lX2Dmk2i20AZjjn0/22AGPMRvyVgIjUAx80xrSIyGeA54wxu/1rDwAnAT/zx0kcU1EGmtYOT2hOHlMTxPRbYd7qR8MsWeNtBPvF828DXrKzRa+8gxA2B02bUBcS39a0NKvBUwJvb00uXRnaLJZSTleI5CmMxoR8+/2x5AR7D/qYZfvmjxzT9w9X9gppVgKLgdkiMktEqoGLgPvdDiLSICJ2rKuB2/3jtXgrhEoRqcJbJawwxmwCdorIiX5U0MeA35XgfZQRTjEHpjGFnZ25jWN+gy9nl2/0ahL/8/u9mAiRsOloTG14PmVXCTZmv6pArp7QZrGUxJl+iimQPjmGgzrFag4arhRVAsaYbuAK4CFgBXCvMWa5iFwrIuf43U4DVorIG8AU4Dq//T5gFfAKnt9gmTHm9/61zwM/Bpr8Pg+U5I2UEcuDr25izlcfLChUjTHBbts7nnmL6x9YERJwOQeoXzDdP1uzpZWTDpgUCNKsye0Ifv+RUzl0n5xTGHIrgaqKTNHkaqHNYikltRTYLBalP+L7yOnjADjr8H36MYpSzqTKHWSMWQQsirR91Tm+D0/gR+/rAT6bMOYSvLBRRSkJtz+zho7uLM+t3soH5u4b28cANX7CtnsWe/EOjWNyZhS7o9guEjLiVfNq7exhVuPoQNAakyuc8rVz5ni5fBy27PbCR+1qo9CegkyRENE44mbmxVIf9SU66OApY3jt2rN6VR9AGVrojmFl2GBj8qN59F2yMWmUjTFO8rVcP/BMKHancH1NpbMSMIE5qNY3+bizePsR4+qqELxooiT6Eh3UGWOkT1wJ9NOUowpgeKNKQBk2xOXdiWJMOHUzeLN+qxisOcitxrWt1ZvVHzS53lkJmGDPQaUvxN1R393pmaRqfAXR0ZW8EuhLdFBjfU3+ew6AT0AZ/qgSUIYNOT9vsrQzxlCVCf/ZZ42hKhOOh8+tBCQIIa2rqghGzpqcqacyk/+/kS39WJkRcFYTY2oqOePQyaG+fUnJ4G0Wi68nEEVdukohVAkowwZbMKZQJEvcSqC7J2cisoLdDiHA65u8yKDD9x0bWglYc1BljDHerhKikUj/fuFcboqUpnRvTx0iGvOZiZvFUihHZeSiSkAZNkwa7UX9FMoBZCDPJ9DZk6Xdzwhq6w0H5qAMrN7irQRmTso5hrPGUx4Zic/3b4V+VYU3X3cL11RElFBceclixEUHFTMlqTlIiUOVgDJssEKup0BV96wxVEeEcGd3ln3G1YbGWL5xB+Cle9jT2eMJ74wgzg7aNVtbQymi44RshW8q6vKfqbJC8lYOfd8sFiY5bYSiJKNKQBk22OicFiffTxRjYExtVV57VcQc9O8PecnemjbvZk9XT5Aczg0RnTg6v7pXlMqMl+3T7i6uzGTyTDnh82QtcOzMCc49+deL7THQhYAShyoBZdhgJ/hJ2UAfXv4Oe7p68souQs6ub6ODdrfniqi8/s7OYOdvzjHs+QTcPQZxWP+D6z+IhrBmUoaIfv/io3P3xKSSTtwnoLt9lQKoElCGDdbWH924BfDWllYu/9kLidftSiAaHQRerL/NChqsBICunlxUEcTPtEdVeXsL7Gaxiozk+RDcYjWFZuv7jq8LspIWSiWdhNZtUuJQJaAMG6zcjpv4ujP7OCVgZ+zWxu8O0dmdDVI7uJvFunuyeU7mKNYJbENE43zW+46v5chp4/zx0zl348JB1Seg9AVVAsowIn8Wb3GLotTEKAFroomrN9zZnQ0Uh+sT6MqaULhpnACv9Gfs1tw0flS+P6I3aSN6fN9CJhMTHZSgQKaNrwueRVGiqBJQhg2FVgKPr3g3OLbpJVxsQfU4BbJxR3ugBIKVQNbwVnNr3sazKFGHsxXEt39iftAnIxLUGyhmsen0lUB7Z09qn8A17z+MK04/iDlTx8Z3UEY0qgSUIc3Dy9/hmw+8HmqLE+Tfc6p6xW20srZ2K6zHOJk/RaDFryfs+gQmjq5ma2vhNNAZ8Wbodu+C9QecceiUILtoRUZo82sbpE0H3Tg23yGdtBKYPmEUV551SOx+BkVRJaAMaS7/2Qv88KlVQM72XawSYpwotKkfrNlopl8MBjxTkfUJWDm6dlsbXT1ZDmisTxxXxC3KEl4JuLgbzgrlGILcKseO01BfPExVUQqhSkAZNtjZdrGauiKSF+Nvbft7OnswxvDqhp2hce2mLyvUJ9VX0501seGmFjf1hA0Rde3/xslPZI8nOM81qrqC2ZNzSsa9Jy5fUX1NReKzKEoSmiNWGTas2+aVcCwWFi8CMyaOCrKDem05JdDVkz+AGwpaU5kB4wn2aEWxpM8LVgIV+SuBCpFAbblKYtHfnxKkqbbYspex4xTxTyhKHPpXowwLslnDPmNrg+NCCMLESJSOnUVXV2ZYsmZb3j0VoSggZ5+AuxIoYHIPEso5Qt4Whs9kcorLHW7/htFBOosocWalQnUUFCUJVQLKsMAtshLVAa+/szN0vr2tM69QinUWG+AjP34+b/zK0KYwz3yzavPugmGXV519aHBH9HMg51/IOOagYvsExtdV+89j/9d1xtP/m5U+kOrPRkQWishKEWkSkatirs8UkcdE5GUReVJEpvvtp4vIS85Pu4ic51+7U0Tecq7Ni46rKGlp6+wJfAHR6KCF//F06Hza+LrArGKpCOL/41cRrg1eJJc7yM1TdP7R00L3jI3JUVTjmHesXyKTYA6Ko2VPZ/AMHrnnjYt6UpRiFFUCIlIB3AycDcwBLhaROZFuNwB3GWPmAtcC1wMYY54wxswzxswDzgDagIed+75krxtjXur/6ygjlawxuX0CTvuOmGRyInDIPmMibblNYHHsbA+ndjB49YhnNeaKxLsJ3gCm+WGnrlyvCtnyveOKjASKq5hJp6bSUyKTYqKC1Byk9IU0K4HjgSZjzGpjTCdwD3BupM8c4HH/+ImY6wAXAg8YY9r6+rCKkkTWmECQurP5m59siu3/qZNnRbJyWnNQvBaYPiEXMuqZb/ydxAWig2rj0lM4K4pdvmIRnEiilPsERlXlO6R1JaD0hTRKYBqwzjlf77e5LAMu8I/PB8aIyKRIn4uAuyNt1/kmpJtEJDYdo4hcLiJLRGRJc3NzisdVRiLG5HwBrjmotaM7r6+IMHlsLd/5P0cFbYESSFgJhGz/YrOImlAeomhKt7rqcOZRCO/qDdJXSLimcSH+39mHMXVcLaN9R/aW3bkIJ1UCSl8olSvpSmCBiCwFFgAbgB57UUSmAkcCDzn3XA0cChwHTAS+HDewMeZWY8x8Y8z8xsbGEj2uMtxwVwJpN4u5YZZW+P7Xi+tj74lW/zLGsKerJ2Teicrv/RtGh85tbYEoY2urgtVLMUH+oeNm8OxVZwSJ60KfrxUDlD6QRglsAGY459P9tgBjzEZjzAXGmKOBa/y2FqfLh4DfGmO6nHs2GY8O4A48s5Oi9ImssxJwZ/Prtu9h2vg6nv6n04M2K2dd04xte6Zpa+z4oeggEfb45ShbO3pi+//h794TOIaDzJ8JAr4i4zqGY7uEcBWJfed/WngI42KS0ylKMdIogcXAbBGZJSLVeGad+90OItIgYgvvcTVwe2SMi4mYgvzVAeL9RZ8HvNr7x1cUj2zWBLPpNX5NYPBKTe5s76KhPmdttELUXQkUm4GHVgIC9y7xVgwzJ42K7X+EnxraJSmctNJxDPc2v8+/nHM4sxpG8/nTDurVfYpiKbrd0RjTLSJX4JlyKoDbjTHLReRaYIkx5n7gNOB6ETHAH4G/tfeLyP54K4mnIkP/QkQa8VbXLwGf6/fbKCMW1xw02UmuJggHNNaHTDWBc7U6F65ZzBbvKgw3LDSuNkEUa6aJfsJOv8ZBRUb8fQsd7OmMX1kkcemJM7n0xJm9ukdRXFKljTDGLAIWRdq+6hzfB9yXcO8a8h3JGGPO6M2DKkohsgZsfXnXHNTZnaWuKiyorTB2Z/fFVwLxwt6GbELxyJ6eiNd533G1bNzRTkVG+MElx3DjI29wUCRXkKIMNLrHUBkWhB3DTkGYnmw4tQME0tqNq48qgbjCM3H0ZAtn/XQ+Lg+3KtlhU8dy28fm5+UKUpSBRpWAMiwwCZvFmjbvpqYyExLEVt5H7fwuX/lAeD/kxFHxKZv39at2QfHonH3H1YXO95vo+RN6ioUzKcoAollElWFB1uTMLa7VZVxdFTv3hPcKBDZ6R/K3R/L4R524VZXxAt6duSfN+G1z1H/w9XMP55fPr2X/SaPzb1KUvYQqAWVYkDWGt/yoIHfHcEd3DwdOrg/N0uOEdWMkDUM0Sicufz9AbWV6803ULHVgY33eikNR9jZqDlKGLK6w78kaJo+p8du9tt8v28iW3Z3UVVWEo4NixsoX+uHzJJPNlHFuJFI8dsXhRiMpSrmgSkAZsrhyeceeXNimzf/zd3cvBfJTR8StBKJCP+oontUQb7JJ8hXEcfCUMcU7KcpeRpWAMmRxZ+fVFZlgBZA10Oakit5nXG1olh7nwC20T+A9BzUk7gdwI3ySagHYFUu0pKWilAOqBJQhixsK6qWNyDmGF6/ZHly74JhpIQEdJ6ujM3937Clj46t7TRtfF9seZZe/EolLZqcog40qAWXI0t6V212bNbkk0AbDLX4K6Rs/dBQzJ40OrwRitEBUCbQ5O3eTFgm3f+K40HnSWsKuWGZMjE8xoSiDiSoBZcji+gSMyZldjCEoFn/2EVPz7ot1DEck/RinKliScC9QSiCEVQKa6lkpR1QJKEMW12QT2ixmDOPqqmior87l9I/JHeQSFdB1KeL/o4ojqV9QNUyVgFKGqBJQhiwhJeCcG2BPZ08ooqeYTyAq0Pdx/ABJO4Gjcf9JjmFdCSjljCoBZcji7gx2fQJZA29u3pWYhydOqLshohcdN4Mjp+dSQSfN8NPmF7JmK60BrJQjqgSUIUtedFA2V2N4/KjqUOlFl2LmoJMODFdGTUwH0UuhrisBpRxRJaAMWcKOYdcn4J0f0Bi/wStOeLs7hvP3DMQL797KdFUCSjmiSkAZspiQY5hQiKgxyRvA4lqrClQZS+sYLoYqAaUcUSWgDFlcn4DBhDaLZY1JDO3cHbNpy/UfRGV10ji9VQK9rRqmKHsDVQLKkCXkE8jipI3wnMRJE+9Gp95wHGlDP6WX//c0jin8uYoyGKT6MxaRhSKyUkSaROSqmOszReQxEXlZRJ4Ukel+++ki8pLz0y4i5/nXZonI8/6Yv/KL2CsjlKfeaObGR97o1T3ZSHRQdCWQNFOPywrtpoTOUwKJPgE1BylDn6JKQEQqgJuBs4E5wMUiEk2CfgNwlzFmLnAtcD2AMeYJY8w8Y8w84AygDXjYv+dbwE3GmIOA7cCnSvA+yhDlb37+At977M1emUyi+wTsWXfWkM0mR+/ECfVapw5xep9A6kctOI6iDCZpVgLHA03GmNXGmE7gHuDcSJ85wOP+8RMx1wEuBB4wxrSJ93/nGeSK0/8UOK+3D68MH2yuntbO9EnWTN6OYe983bY2jDHJQjc2lbSbDbRod7+9lysB1QJKGZJGCUwD1jnn6/02l2XABf7x+cAYEZkU6XMRcLd/PAloMcbY/+PjxgRARC4XkSUisqS5uTnF4ypDme6e9PV283MHecdTxtYW9AnENVcUjA5KWFH0UqZHC9coSjlQKsfwlcACEVkKLAA2AMG6XkSmAkcCD/V2YGPMrcaY+caY+Y2NjSV6XKVc6c5mi3fyiW4W6/a1gvUPJPoE4rKIOm1pbf299Qn0tr+i7A3S1BjeAMxwzqf7bQHGmI34KwERqQc+aIxpcbp8CPitMcaWf9oKjBeRSn81kDemMjLpzUrALSrjKg/PMdy7Gbw7+++OlJIslU9AFwJKOZJmJbAYmO1H81TjmXXudzuISINIEDB3NXB7ZIyLyZmCMJ7x9gk8PwHAx4Hf9f7xleFGVAAXYld7zn+wfvue4Djr+wcSQzuL5A6qrwnnHNLoIGU4U1QJ+DP1K/BMOSuAe40xy0XkWhE5x+92GrBSRN4ApgDX2ftFZH+8lcRTkaG/DHxRRJrwfAQ/6debKMOCpILucbi7fOtrcotaA/6O4fj7YrOIOp3zs4OmH6cQag5SypE05iCMMYuARZG2rzrH95GL9Ineu4YYp68xZjVe5JGiBLS05ZK+bWvt5Kk3NnPevGmxph1XX3zt/uXBsSniE3Cbf3Tpsdzy5CpGVzv1AyIz/8ToIPUJKMOAVEpAUfYWrmA95l8fAWB8XTWnHzo5r69JWDRYn0By7qBc+1mH78NZh+8TeYboM6V58uKoOUgpRzRthFJWxEUHvfHurti+2QQt4O4ejqOYUM9PG1Ea4Z2reKAo5YMqAaWs6EWEaKKgN/5/epNF1CWaVqJU8/fR1brwVsoPVQJKWRG3EkiciCdMrLMGdnV0JyqJYjP7vGigEmkBdQko5YgqAaWsiIsOSgrRTAwk8oV/a0zKaIDO7sLLjfxU0qWR3qUyKylKKVEloJQVxfYJvLSuhUt/8jztXT2JNnZvoxjsM6429nox23xUWPdGds+dPo5xdVXx46YfRlH2GmqkVMqK2JWAIz2/8/BKnn5zC0vXtiSuBLJ+qcmkmXexrQhpi8rEcedlx7OnKz4Tqi4ElHJElYBSVmxs2cPXf7+cQ6aMybv21pZWXtmwA4BP3rmYMbXxf75WyCfJ3EKRQ1B8JfA3px3I21tbY++dODq5LEapzEqKUkpUCShlRX1NJXc8syb22mV3/JmWNi/91J6unsQZt00pnTTzNkWUQDGfwJcXHlrw/rTjKko5oD4BpazoKmCrWbO1LdUY1qSUFCJaLAw1bXnJXqNKQClDVAkoZUV3T1yIaO+kZ49dCSRcL2YOirJzT1fxTilQc5BSjqgSUMoCK+e74pRAL8fKZouYg4rcHy3+MjYh2qe3qGNYKUdUCShlgU3l3NWLegJJBCuBBKmb5BOw3aO2+9qqivzOfUATyCnliCoBpSywydXWbUtn9y/Eum1ebYEkmZvkdrBCeqCEtaoApRxRJaCUBdZePm5UaUwv7phRjp81Mbb9khP2A/Jn/qXSCboQUMoRDRFVygK7i7ejqxe5g4qQdF9DfU1s+9f++nCuOP2gvB2/JUsboWsBpQzRlYBSFlgzfWeMY7iv9DYuvyIjTB4bn2qiFIj+36aUIfpnqZQF1kzfFZPcTYiPGoqjujL3J126xG8lGUbXAUpZkkoJiMhCEVkpIk0iclXM9Zki8piIvCwiT4rIdOfafiLysIisEJHX/JrDiMidIvKWiLzk/8wr1UspQxBfC6yNcQyLCDtSxupXOBK73IS3ZhFVypGiSkBEKoCbgbOBOcDFIjIn0u0G4C5jzFzgWuB659pdwLeNMYfh1RTe7Fz7kjFmnv/zUj/eQxniWJ/A829ti72+fvueVOO4crZUQrfclImilJI0K4HjgSZjzGpjTCdwD3BupM8c4HH/+Al73VcWlcaYRwCMMbuNMf2PAVSGHYUye4pAW0JtAIBvnH9kcOyGd5ab0NWFgFKOpFEC04B1zvl6v81lGXCBf3w+MEZEJgEHAy0i8hsRWSoi3/ZXFpbrfBPSTSISH7KhjAiKJXXrcHwC82dO4IcfPTY4P/OwXBH6TGglUJpnK5VvQTeLKeVIqRzDVwILRGQpsADYAPTghaCe4l8/DjgA+IR/z9XAoX77RODLcQOLyOUiskREljQ3N5focZVyo5AKEMIO44qM0FBfHe7g46Z8KJXQVdmtDGfSKIENwAznfLrfFmCM2WiMucAYczRwjd/WgrdqeMk3JXUD/w0c41/fZDw6gDvwzE55GGNuNcbMN8bMb2xs7OXrKUOFYjnd3NDRjEjI3p9JOO6v8P7kybP6N0AEVSZKOZJGCSwGZovILBGpBi4C7nc7iEiDSBAFfTVwu3PveBGx0vsM4DX/nqn+bwHOA17tz4sowxiRkGNYJGz2CSsB57a98Wy9QDeLKeVIUSXgz+CvAB4CVgD3GmOWi8i1InKO3+00YKWIvAFMAa7z7+3BMwU9JiKv4P1/eZt/zy/8tleABuDfSvZWypCimD8AoNaJ/8+IJAr+jJTOKVCsFnFv0aIySjmSKm2EMWYRsCjS9lXn+D7gvoR7HwHmxrSf0asnVYYtadL7tzs+AZFcwjkIz7B3O1FE5SZ0dZ+AJ2KgfQAACWBJREFUUo7ojmFl0Ekz337bqSrm+QRy19x0DDMnjQ6Od7cnh5WmodTmm4py00qKgioBpQxIYw6qczJ7ZiTZGVxdkTse38+MpKU2BylKOaJKQBl00ojazp5cUfmoT8CdX3/ouFwgW6mKwagZRxnOqBJQBp00PoHtbbncQSJChfOX6yqEw6aODfVTFKUwqgSUQadY4fdNLXv4n5c3BecZCQt4SREuOphccMw0DpkyZrAfQ1Fi0aIyStnz7s6O0Pmy9S3JG8ScftEdw++dM4UX17ak/tw0K5Q03HDhUXQXSo6kKIOIKgFl0CkmICsjU/p3d3aEhL0r6ysyySuB73/kaNo70xet2bLbUz5paxkkkckI1eWyLFGUCKoElEEnrpBM6HqMEB5V40YL5QRsXXWuPeoTqKmsoKYyvbN42oQ6oHQrAkUpR9QnoJQ90ZKT9TWVibZ/d9VQ0V/HsAp/ZQSgSkAZdIrJ2uZdYZ/AxcfPiCSKi/cPvLurvSTPpUFGynBGlYAy6BTbLFZVEf4zffrNLYmzfNcnsKufO4YtqgOU4YwqAWXQiVMB//z+w4Lj7mxM8fmEv1zXHNRfW36ancyKMtRRJaAMOv/1wvq8tukTRgXHPZHoIRFJXAm4RWWK7T8ohr1dzUHKcEaVgDKoGGO4/oHX89rd2X80hFTI3wNgZX80nLRfzxZ8nmoBZfiiSkAZVN7cvDs4dpPEdffkBH/+SgAykb/cxjE1/jVnJaAbtBSlKKoElEHl58+9HRx/4czZnHGoVzR+5qScOchVCGAri4Vn59++8ChOmd1AfU1u68tJB04aiEdWlGGFbhZTBpX2rlx20PraSn78sfms2drKAY31QXucbT+qBE49uJFTDw7XoD5qxvh+PZv6BJSRgK4ElEFlQ4tTOxghk5GQAgBC9YUtaUz//ZXdWk9AGQmoElAGlaOm52brSTPusbXhBatXWay4iI+uFvqKpqRWhjOplICILBSRlSLSJCJXxVyfKSKPicjLIvKkiEx3ru0nIg+LyAoReU1E9vfbZ4nI8/6YvxKR6lK9lDJ0cHP5JInaHpMfHZSGfmeN0IWAMgIoqgREpAK4GTgbmANcLCJzIt1uAO4yxswFrgWud67dBXzbGHMYcDyw2W//FnCTMeYgYDvwqf68iDI0cVM7JAntvibxLNUMXtcBynAmzUrgeKDJGLPaGNMJ3AOcG+kzB3jcP37CXveVRaUx5hEAY8xuY0ybeP93ngHc59/zU+C8fr2JMiTZ05lzDCfF4/d305eiKMlIsa3xInIhsNAY82n//FLgBGPMFU6fXwLPG2O+KyIXAP8FNACnAJ8GOoFZwKPAVcAE4Dl/FYCIzAAeMMYcEfP5lwOX+6eHACv7+K4NwJY+3jtU0XceGeg7D3/6+74zjTGNcRdKFSJ6JfCfIvIJ4I/ABqDHH/8U4GhgLfAr4BPA79IObIy5Fbi1vw8oIkuMMfP7O85QQt95ZKDvPPwZyPdNYw7aAMxwzqf7bQHGmI3GmAuMMUcD1/htLcB64CXflNQN/DdwDLAVGC8ilUljKoqiKANPGiWwGJjtR/NUAxcB97sdRKRBJMjreDVwu3PveBGxy5AzgNeMZ4N6ArjQb/84vVgdKIqiKKWhqBLwZ/BXAA8BK4B7jTHLReRaETnH73YasFJE3gCmANf59/bgmYoeE5FX8AItbvPv+TLwRRFpAiYBPynZW8XTb5PSEETfeWSg7zz8GbD3LeoYVhRFUYYvumNYURRlBKNKQFEUZQQzIpRAsbQXQwURmSEiT/jpN5aLyD/47RNF5BERedP/PcFvFxH5nv/eL4vIMc5YH/f7vykiHx+sd0qLiFSIyFIR+YN/Hpt2RERq/PMm//r+zhhX++0rReSswXmTdIjIeBG5T0Re91OunDTcv2cR+Uf/7/pVEblbRGqH2/csIreLyGYRedVpK9n3KiLHisgr/j3fE0mxbd4YM6x/gApgFXAAUA0sA+YM9nP18V2mAsf4x2OAN/B2a/87cJXffhXwLf/4fcADeA75E/E29AFMBFb7vyf4xxMG+/2KvPsXgV8Cf/DP7wUu8o9/CPyNf/x54If+8UXAr/zjOf53X4O3cXEVUDHY71XgfX8KfNo/rgbGD+fvGZgGvAXUOd/vJ4bb9wycihcm/6rTVrLvFfiz31f8e88u+kyD/Y+yF/7RTwIecs6vBq4e7Ocq0bv9DvgrvF3UU/22qcBK//hHwMVO/5X+9YuBHzntoX7l9oO3j+QxvBDjP/h/4FvwUpKEvmO8KLaT/ONKv59Ev3e3X7n9AON8gSiR9mH7PftKYJ0v2Cr97/ms4fg9A/tHlEBJvlf/2utOe6hf0s9IMAfZPy7Ler9tSOMvf48GngemGGM2+ZfewQvTheR3H2r/Jv8B/BNgU8lNAlqMF74M4ecP3s2/vsPvP5TeeRbQDNzhm8B+LCKjGcbfszFmA14iyrXAJrzv7QWG9/dsKdX3Os0/jrYXZCQogWGHiNTj5Wf6gjFmp3vNeFOAYRP3KyIfADYbY14Y7GfZi1TimQxuMd4u/FY8M0HAMPyeJ+AlnpwF7AuMBhYO6kMNAoPxvY4EJVA07cVQQkSq8BTAL4wxv/Gb3xWRqf71qeTSdSe9+1D6NzkZOEdE1uBlsD0D+C7JaUeCd/Ovj8NLUzKU3nk9sN4Y87x/fh+eUhjO3/OZwFvGmGZjTBfwG7zvfjh/z5ZSfa8b/ONoe0FGghIomvZiqOB7+n8CrDDG3Ohcuh8v9QaEU3DcD3zMjzI4EdjhLzsfAt4rIhP8Gdh7/baywxhztTFmujFmf7zv7nFjzCUkpx1x/y0u9Psbv/0iP6pkFjAbz4lWdhhj3gHWicghftNfAq8xjL9nPDPQiSIyyv87t+88bL9nh5J8r/61nSJyov9v+DHSpOMZbCfJXnLEvA8vkmYVcM1gP08/3uM9eEvFl4GX/J/34dlCHwPexEvXPdHvL3gFgVYBrwDznbE+CTT5P5cN9rulfP/TyEUHHYD3P3cT8Gugxm+v9c+b/OsHOPdf4/9brCRF1MQgv+s8YIn/Xf83XhTIsP6ega8DrwOvAj/Di/AZVt8zcDeez6MLb8X3qVJ+r8B8/99vFfCfRIIL4n40bYSiKMoIZiSYgxRFUZQEVAkoiqKMYFQJKIqijGBUCSiKooxgVAkoiqKMYFQJKIqijGBUCSiKooxg/j+WutMLCZJjBwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(log['Epoch'].values, log['p3'].values)\n",
    "plt.plot([0,10000],[1,1])\n",
    "plt.ylim(0.96,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2ac6e9fa3790>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(log['Epoch'], log['p4'])\n",
    "plt.plot([0,10000],[1,1])\n",
    "plt.ylim(-2,-0.4)\n",
    "plt.plot([0,10000],[-1,-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2ac6e9fbde50>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.semilogy(log['Epoch'], log['Loss_u'], label='loss_u')\n",
    "plt.semilogy(log['Epoch'], log['Loss_pde'], label='loss_pde')\n",
    "plt.semilogy(log['Epoch'], log['Loss_l1'], label='loss_l1')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_mesh = dt*np.arange(nsteps)\n",
    "x_mesh = dx*np.arange(nx)\n",
    "y_mesh = dy*np.arange(ny)\n",
    "t_mesh, x_mesh,y_mesh = np.meshgrid(t_mesh, x_mesh, y_mesh,indexing='ij')\n",
    "t_mesh = np.reshape(t_mesh, (-1,1))\n",
    "x_mesh = np.reshape(x_mesh, (-1,1))\n",
    "y_mesh = np.reshape(y_mesh, (-1,1))\n",
    "x_mesh /= x_max\n",
    "y_mesh /= y_max\n",
    "t_mesh /= t_max\n",
    "prediction = model(torch.cat([torch.tensor(x_mesh), torch.tensor(y_mesh), torch.tensor(t_mesh)],dim=1).double().cuda(0))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}