BayesianOptimizationYJunction.ipynb

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   "source": [
    "# Bayesian optimization of a Y branch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note: the cost of running the entire notebook is larger than 10 FlexCredits.**\n",
    "\n",
    "[Bayesian optimization](https://en.wikipedia.org/wiki/Bayesian_optimization) is a popular technique for searching and optimizing a design space. It works by building a probabilistic model, such as a [Gaussian process](https://en.wikipedia.org/wiki/Gaussian_process), to predict the outcome of a more complex objective function. Unlike other optimizers like genetic algorithms or particle swarm optimization, Bayesian optimization uses an acquisition function to select new potential solutions, focusing on areas of the design space that the Gaussian process predicts will have high objective values and high uncertainty. Over the course of the optimization, the model's uncertainty reduces, improving its accuracy as a surrogate for the true objective function. This makes Bayesian optimization a direct approach to optimization, particularly well-suited to problems with small (< 30 dimensions) and expensive-to-evaluate objective functions. \n",
    "\n",
    "However, as Bayesian optimization is an iterative process with many individual components, it can be challenging to design and parallelize, leading to long run times and high computational costs.  Fortunately, the Design plugin in ``Tidy3D`` includes the Bayesian optimization method ``MethodBayOpt`` which eliminates much of this complexity. Users can quickly and easily run a Bayesian optimization for complex FDTD simulations and analyze the results. Further details on Bayesian optimization and the methods used in ``Tidy3D`` are available in the open-source Python library [bayesian_optimization](https://bayesian-optimization.github.io/BayesianOptimization/basic-tour.html).\n",
    "\n",
    "This notebook details how to perform a Bayesian optimization with the ``Tidy3D`` Design plugin for the development of a Y-junction. It is based on the work of `Zhengqi Gao, Zhengxing Zhang, and Duane S. Boning, \"Automatic Synthesis of Broadband Silicon Photonic Devices via Bayesian Optimization\" J. Light. Technol. 40, 7879-7892 (2022)` [DOI:10.1109/JLT.2022.3207052](https://doi.org/10.1109/JLT.2022.3207052). A detailed description of how to build a Y-junction can be found in the [Waveguide Y junction](https://www.flexcompute.com/tidy3d/examples/notebooks/YJunction/).\n",
    "\n",
    "\n",
    "<img src=\"img/bayesian_opt_y_junction.png\" width=\"400\" alt=\"Schematic of the waveguide Y junction\">\n",
    "\n",
    "If you are curious about other `Design` plugin features, see these notebooks:\n",
    "\n",
    "1. [Intro to Design Plugin](https://www.flexcompute.com/tidy3d/examples/notebooks/Design/)\n",
    "\n",
    "2. [Particle Swarm Optimizer PBS](https://www.flexcompute.com/tidy3d/examples/notebooks/ParticleSwarmOptimizedPBS/)\n",
    "\n",
    "3. [Particle Swarm Optimizer Bullseye Cavity](https://www.flexcompute.com/tidy3d/examples/notebooks/BullseyeCavityPSO/)\n",
    "\n",
    "4. [Genetic Algorithm Reflector](https://www.flexcompute.com/tidy3d/examples/notebooks/GeneticAlgorithmReflector/)\n",
    "\n",
    "5. [All-Dielectric Structural Colors](https://www.flexcompute.com/tidy3d/examples/notebooks/AllDielectricStructuralColor/)\n",
    "\n",
    "6. [Parameter Scan](https://www.flexcompute.com/tidy3d/examples/notebooks/ParameterScan/)"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "# The Bayesian optimizer uses the bayesian-optimization external package version 1.5.1.\n",
    "# Uncomment the following line to install the package\n",
    "# pip install bayesian-optimization==1.5.1\n",
    "\n",
    "import gdstk\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tidy3d as td\n",
    "import tidy3d.plugins.design as tdd\n",
    "import tidy3d.web as web\n",
    "from scipy.interpolate import make_interp_spline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulation Setup"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simulation is defined across the 1.5 $\\mu m$ to 1.6 $\\mu m$ wavelength range with 100 sampling points. The base of the model is silicon, the top of the model is silicon dioxide; we use the material constants included in the Tidy3D Tidy3D's [material library](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/material_library.html#)."
   ]
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  {
   "cell_type": "code",
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   "source": [
    "lda0 = 1.55  # central wavelength\n",
    "freq0 = td.C_0 / lda0  # central frequency\n",
    "n_wav = 100  # Number of wavelengths to sample in the range\n",
    "ldas = np.linspace(1.5, 1.6, n_wav)  # wavelength range\n",
    "freqs = td.C_0 / ldas  # frequency range\n",
    "fwidth = 0.5 * (np.max(freqs) - np.min(freqs))  # width of the source frequency range\n",
    "\n",
    "si = td.material_library[\"cSi\"][\"Palik_Lossless\"]\n",
    "sio2 = td.material_library[\"SiO2\"][\"Palik_Lossless\"]"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following parameters describe the geometry of the waveguide. The optimizable design space is the junction which is split into 13 discrete segments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = 0.22  # thickness of the silicon layer\n",
    "num_d = 13  # dimensional space of the design region\n",
    "\n",
    "l_in = 1  # input waveguide length\n",
    "l_junction = 2  # length of the junction\n",
    "l_bend = 6  # horizontal length of the waveguide bend\n",
    "h_bend = 2  # vertical offset of the waveguide bend\n",
    "l_out = 1  # output waveguide length\n",
    "branch_width = 0.5  # width of one Y branch\n",
    "branch_sep = 0.2  # distance between y branches at the junction\n",
    "inf_eff = 100  # effective infinity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most effective way to use the Design plugin is to split the workflow into \"pre\" and \"post\" functions which can surround a call to the Tidy3D cloud that carries out the computation. This takes advantage of automated simulation batching, allowing for parallelization which saves a considerable amount of time. The pre-function returns a `Simulation`; the post-function then analyzes the corresponding `SimulationData`. Together they can be considered the fitness function (or objective function / figure of merit)  which the Bayesian optimization process is working to predict.\n",
    "\n",
    "The function ``fn_pre`` needs to take the parameters that are being optimized: these are the 13 width segments of the junction. They always input as a dictionary and can be unpacked within the function (as below) or included as keyword arguments. These parameters are used to build a Y-junction ``Simulation`` object.\n",
    "\n",
    "The function ``fn_post`` then takes the ``SimulationData`` object output by the simulation and computes the objective function. The value of objective is then fed back into the Bayesian optimization to inform the probabilistic model. In this case, we extract the power passing through the Y-junction and the power reflected back to the source. These values are evaluated in a custom loss function described by ``Gao et al.`` to determine the effectiveness of the junction design:\n",
    "\n",
    "\\begin{equation}\n",
    "  -\\frac{N_\\lambda}{3} \\sum_\\lambda \\left( R^2 + 2(T-0.5)^2 \\right)\n",
    "\\end{equation}\n",
    "where the summation is performed over the simulated wavelengths, $N_\\lambda$ is the number of wavelength points, $R$ is the reflected power and $T$ is the transmitted power in one branch. \n",
    "\n",
    "The aim of this function is to achieve a transmitted power of 0.5 through each branch, whilst driving the power reflected towards the source to zero. Note there is a minus sign in front of the output value as this loss function is a minimizing function, whilst all the optimizers in the Design plugin are built to maximize the objective function. "
   ]
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  {
   "cell_type": "code",
   "execution_count": 5,
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   "source": [
    "def fn_pre(**w_params: dict) -> td.Simulation:\n",
    "    \"\"\"Create a Simulation of a Y splitter from a series of junction widths.\n",
    "\n",
    "    Includes mode monitors to measure the power transmitted and reflected to source.\n",
    "    \"\"\"\n",
    "    w_start = 0.5\n",
    "    w_end = branch_width * 2 + branch_sep\n",
    "\n",
    "    widths = [w_start]  # Ensures input waveguide is included in spline for first point of junction\n",
    "    widths.extend(list(w_params.values()))\n",
    "    widths.append(w_end)  # Ensures final point of junction smoothly converts to the branches\n",
    "\n",
    "    x_junction = np.linspace(\n",
    "        l_in, l_in + l_junction, num_d + 2\n",
    "    )  # x coordinates of the top edge vertices\n",
    "    y_junction = np.array(widths)  # y coordinates of the top edge vertices\n",
    "\n",
    "    # pass vertices through spline and increase sampling to smooth the geometry\n",
    "    new_x_junction = np.linspace(\n",
    "        l_in, l_in + l_junction, 100\n",
    "    )  # x coordinates of the top edge vertices\n",
    "    spline = make_interp_spline(x_junction, y_junction, k=2)\n",
    "    spline_yjunction = spline(new_x_junction)\n",
    "\n",
    "    # using concatenate to include bottom edge vertices\n",
    "    x_junction = np.concatenate((new_x_junction, np.flipud(new_x_junction)))\n",
    "    y_junction = np.concatenate((spline_yjunction / 2, -np.flipud(spline_yjunction / 2)))\n",
    "\n",
    "    # stacking x and y coordinates to form vertices pairs\n",
    "    vertices = np.transpose(np.vstack((x_junction, y_junction)))\n",
    "\n",
    "    junction = td.Structure(\n",
    "        geometry=td.PolySlab(vertices=vertices, axis=2, slab_bounds=(0, t)), medium=si\n",
    "    )\n",
    "\n",
    "    x_start = l_in + l_junction  # x coordinate of the starting point of the waveguide bends\n",
    "\n",
    "    x_bend = np.linspace(x_start, x_start + l_bend, 100)  # x coordinates of the top edge vertices\n",
    "\n",
    "    y_bend = (\n",
    "        (x_bend - x_start) * h_bend / l_bend\n",
    "        - h_bend * np.sin(2 * np.pi * (x_bend - x_start) / l_bend) / (np.pi * 2)\n",
    "        + w_end / 2\n",
    "        - w_start / 2\n",
    "    )  # y coordinates of the top edge vertices\n",
    "\n",
    "    # adding the last point to include the straight waveguide at the output\n",
    "    x_bend = np.append(x_bend, inf_eff)\n",
    "    y_bend = np.append(y_bend, y_bend[-1])\n",
    "\n",
    "    # add path to the cell\n",
    "    cell = gdstk.Cell(\"bends\")\n",
    "    cell.add(\n",
    "        gdstk.FlexPath(x_bend + 1j * y_bend, branch_width, layer=1, datatype=0)\n",
    "    )  # top waveguide bend\n",
    "    cell.add(\n",
    "        gdstk.FlexPath(x_bend - 1j * y_bend, branch_width, layer=1, datatype=0)\n",
    "    )  # bottom waveguide bend\n",
    "\n",
    "    # define top waveguide bend structure\n",
    "    wg_bend_1 = td.Structure(\n",
    "        geometry=td.PolySlab.from_gds(\n",
    "            cell,\n",
    "            gds_layer=1,\n",
    "            axis=2,\n",
    "            slab_bounds=(0, t),\n",
    "        )[0],\n",
    "        medium=si,\n",
    "    )\n",
    "\n",
    "    # define bottom waveguide bend structure\n",
    "    wg_bend_2 = td.Structure(\n",
    "        geometry=td.PolySlab.from_gds(\n",
    "            cell,\n",
    "            gds_layer=1,\n",
    "            axis=2,\n",
    "            slab_bounds=(0, t),\n",
    "        )[1],\n",
    "        medium=si,\n",
    "    )\n",
    "\n",
    "    # straight input waveguide\n",
    "    wg_in = td.Structure(\n",
    "        geometry=td.Box.from_bounds(rmin=(-inf_eff, -w_start / 2, 0), rmax=(l_in, w_start / 2, t)),\n",
    "        medium=si,\n",
    "    )\n",
    "\n",
    "    # the entire model is the collection of all structures defined so far\n",
    "    model_structure = [wg_in, junction, wg_bend_1, wg_bend_2]\n",
    "\n",
    "    Lx = l_in + l_junction + l_out + l_bend  # simulation domain size in x direction\n",
    "    Ly = w_end + 2 * h_bend + 1.5 * lda0  # simulation domain size in y direction\n",
    "    Lz = 10 * t  # simulation domain size in z direction\n",
    "    sim_size = (Lx, Ly, Lz)\n",
    "\n",
    "    # add a mode source as excitation\n",
    "    mode_spec = td.ModeSpec(num_modes=1, target_neff=3.5)\n",
    "    mode_source = td.ModeSource(\n",
    "        center=(l_in / 2, 0, t / 2),\n",
    "        size=(0, 4 * w_start, 6 * t),\n",
    "        source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
    "        direction=\"+\",\n",
    "        mode_spec=mode_spec,\n",
    "        mode_index=0,\n",
    "    )\n",
    "\n",
    "    # add a mode monitor to measure transmission at the output waveguide\n",
    "    mode_monitor_11 = td.ModeMonitor(\n",
    "        center=(l_in / 3, 0, t / 2),\n",
    "        size=(0, 4 * w_start, 6 * t),\n",
    "        freqs=freqs,\n",
    "        mode_spec=mode_spec,\n",
    "        name=\"mode_11\",\n",
    "    )\n",
    "\n",
    "    mode_monitor_12 = td.ModeMonitor(\n",
    "        center=(l_in + l_junction + l_bend + l_out / 2, w_end / 2 - w_start / 2 + h_bend, t / 2),\n",
    "        size=(0, 4 * w_start, 6 * t),\n",
    "        freqs=freqs,\n",
    "        mode_spec=mode_spec,\n",
    "        name=\"mode_12\",\n",
    "    )\n",
    "\n",
    "    # add a field monitor to visualize field distribution at z=t/2\n",
    "    field_monitor = td.FieldMonitor(\n",
    "        center=(0, 0, t / 2), size=(td.inf, td.inf, 0), freqs=[freq0], name=\"field\"\n",
    "    )\n",
    "\n",
    "    run_time = 5e-13  # simulation run time\n",
    "\n",
    "    # construct simulation\n",
    "    sim = td.Simulation(\n",
    "        center=(Lx / 2, 0, 0),\n",
    "        size=sim_size,\n",
    "        grid_spec=td.GridSpec.auto(min_steps_per_wvl=20, wavelength=lda0),\n",
    "        structures=model_structure,\n",
    "        sources=[mode_source],\n",
    "        monitors=[mode_monitor_11, mode_monitor_12, field_monitor],\n",
    "        run_time=run_time,\n",
    "        boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
    "        medium=sio2,\n",
    "    )\n",
    "\n",
    "    return sim\n",
    "\n",
    "\n",
    "def fn_post(sim_data: td.SimulationData) -> float:\n",
    "    \"\"\"Calculate the loss function from the power at the mode monitors in the SimulationData.\"\"\"\n",
    "    # Calculate the power reflected back to source and transmitted to one branch\n",
    "    power_reflected = np.squeeze(\n",
    "        np.abs(sim_data[\"mode_11\"].amps.sel(direction=\"-\", mode_index=0)) ** 2\n",
    "    )\n",
    "    power_transmitted = np.squeeze(\n",
    "        np.abs(sim_data[\"mode_12\"].amps.sel(direction=\"+\", mode_index=0)) ** 2\n",
    "    )\n",
    "\n",
    "    # Loss function proposed by Gao et al. which takes advantage of branch symmetry\n",
    "    loss_fn = 1 / 3 * n_wav * np.sum(power_reflected**2 + 2 * (power_transmitted - 0.5) ** 2)\n",
    "    output = -float(loss_fn.values)  # Negative value as this is a minimizing loss function\n",
    "\n",
    "    return output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can quickly check that ``fn_pre`` is working correctly by passing a set of potential ``test_params`` and plotting the result. Note that the gap between the `Polyslab` junction and the branches is a plotting artifact and doesn't exist in the `Simulation`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_params = {\n",
    "    \"w1\": 0.5,\n",
    "    \"w2\": 0.5,\n",
    "    \"w3\": 0.6,\n",
    "    \"w4\": 0.7,\n",
    "    \"w5\": 0.9,\n",
    "    \"w6\": 1.26,\n",
    "    \"w7\": 1.4,\n",
    "    \"w8\": 1.4,\n",
    "    \"w9\": 1.4,\n",
    "    \"w10\": 1.4,\n",
    "    \"w11\": 1.31,\n",
    "    \"w12\": 0.5,\n",
    "    \"w13\": 0.5,\n",
    "}\n",
    "sim = fn_pre(**test_params)\n",
    "sim.plot(z=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we setup the Bayesian optimization method. This is done with the ``MethodBayOpt`` object. We don't have the ``lcb`` (lower confidence bound) acquisition function used in the paper available to us, but by making our loss function negative and using the ``ucb`` (upper confidence bound) acquisition we achieve the same optimizer design. The `initial_iter` and `n_iter` options control the number of initial random samples and subsequent optimization iterations respectively. We can also set the random `seed` to ensure reproducible results (optional).\n",
    "\n",
    "We also create a list of ``ParameterFloat`` objects corresponding to the 13 segment widths. The `span` option defines the bounds of each parameter. \n",
    "\n",
    "The ``method`` and ``parameters`` are then passed to a ``DesignSpace`` object which contains all we need to run the Bayesian optimization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "method = tdd.MethodBayOpt(\n",
    "    initial_iter=30,\n",
    "    n_iter=70,\n",
    "    acq_func=\"ucb\",\n",
    "    kappa=0.3,\n",
    "    seed=1,\n",
    ")\n",
    "\n",
    "parameters = [tdd.ParameterFloat(name=f\"w_{i}\", span=(0.5, 1.6)) for i in range(num_d)]\n",
    "\n",
    "design_space = tdd.DesignSpace(\n",
    "    method=method, parameters=parameters, task_name=\"bay_opt_notebook\", path_dir=\"./data\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is then very easy to the launch this optimization with ``design_space.run()``. This launches an initial random batch of 30 simulations, as specified by `initial_iter`, followed by sequential computation of 70 simulations, as specified by `n_iter`. In the latter phase, the Bayesian optimizer chooses potential candidates based on simultaneously maximizing the objective value and efficiently exploring the design space. The total of 100 simulations takes around 90 minutes to compute. Once complete, the results are returned in a ``pandas`` dataframe for analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">15:18:55 EDT </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span> Simulations                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m15:18:55 EDT\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m30\u001b[0m Simulations                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">17:14:35 EDT </span>Latest Best Fit on Iter <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">69</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-2.77</span>                                  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>                                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m17:14:35 EDT\u001b[0m\u001b[2;36m \u001b[0mLatest Best Fit on Iter \u001b[1;36m69\u001b[0m: \u001b[1;36m-2.77\u001b[0m                                  \n",
       "\u001b[2;36m             \u001b[0m                                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Best Result: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-2.770382176989957</span>                                    \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Best Parameters: w_0: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.5309302341102854</span> w_1: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.5</span> w_10:            \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.036137990749239</span> w_11: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.8488755244945626</span> w_12: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.6</span> w_2: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.6</span> w_3: \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.6</span> w_4: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.6</span> w_5: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.4349135306156366</span> w_6: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.471742406951237</span> w_7:   \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.2941922008374072</span> w_8: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.6</span> w_9: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.130824837197553</span>                 \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>                                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mBest Result: \u001b[1;36m-2.770382176989957\u001b[0m                                    \n",
       "\u001b[2;36m             \u001b[0mBest Parameters: w_0: \u001b[1;36m0.5309302341102854\u001b[0m w_1: \u001b[1;36m0.5\u001b[0m w_10:            \n",
       "\u001b[2;36m             \u001b[0m\u001b[1;36m1.036137990749239\u001b[0m w_11: \u001b[1;36m0.8488755244945626\u001b[0m w_12: \u001b[1;36m1.6\u001b[0m w_2: \u001b[1;36m1.6\u001b[0m w_3: \n",
       "\u001b[2;36m             \u001b[0m\u001b[1;36m1.6\u001b[0m w_4: \u001b[1;36m1.6\u001b[0m w_5: \u001b[1;36m1.4349135306156366\u001b[0m w_6: \u001b[1;36m1.471742406951237\u001b[0m w_7:   \n",
       "\u001b[2;36m             \u001b[0m\u001b[1;36m1.2941922008374072\u001b[0m w_8: \u001b[1;36m1.6\u001b[0m w_9: \u001b[1;36m1.130824837197553\u001b[0m                 \n",
       "\u001b[2;36m             \u001b[0m                                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = design_space.run(fn_pre, fn_post, verbose=True)\n",
    "df = results.to_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best result can be extracted directly from the optimizer object. Plotting this, we see what the optimizer has returned as the optimized structure for this design of Y-junction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best fitness: -2.770382176989957\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_params = results.optimizer.max[\"params\"]\n",
    "print(f\"Best fitness: {results.optimizer.max['target']}\")\n",
    "sim = fn_pre(**best_params)\n",
    "sim.plot(z=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then create a plot to evaluate if the Bayesian optimization has converged on a fitness value. The first 30 iterations are from the random initialization, so the fitness values are expected to be more varied. The fitness has converged before the end of the remaining 70 iterations; an early stop criteria could have been included to finish the optimization sooner."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df[\"output\"].plot(xlabel=\"Simulation Number\", ylabel=\"Fitness\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mean power at the reflected and transmitted monitors can be calculated and compared to the paper from ``Gao et al.``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Reflected Power: 0.002 (Paper: 0.004)\n",
      "Mean Transmitted Power: 0.48 (Paper: 0.460)\n"
     ]
    }
   ],
   "source": [
    "idx_best_result = df[\"output\"].idxmax()\n",
    "best_sim_filename = results.task_paths[idx_best_result]\n",
    "best_sim = td.SimulationData.from_file(best_sim_filename)\n",
    "\n",
    "power_reflected = np.array(\n",
    "    np.squeeze(np.abs(best_sim[\"mode_11\"].amps.sel(direction=\"-\", mode_index=0)) ** 2)\n",
    ").mean()\n",
    "power_transmitted = np.array(\n",
    "    np.squeeze(np.abs(best_sim[\"mode_12\"].amps.sel(direction=\"+\", mode_index=0)) ** 2)\n",
    ").mean()\n",
    "\n",
    "print(f\"Mean Reflected Power: {round(power_reflected, 3)} (Paper: 0.004)\")\n",
    "print(f\"Mean Transmitted Power: {round(power_transmitted, 3)} (Paper: 0.460)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final Optimized Design"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can simulate the optimal solution with an additional `FieldMonitor` to visualise the field distribution throughout the splitter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">17:45:45 EDT </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'BO_notebook_final_sim'</span> with task_id                  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-0e2fa139-898e-4bcd-9471-943d668ba333'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m17:45:45 EDT\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'BO_notebook_final_sim'\u001b[0m with task_id                  \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-0e2fa139-898e-4bcd-9471-943d668ba333'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-0e2fa139-898e-4bcd-9471-943d668ba333\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-0e2fa139-898</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-0e2fa139-898e-4bcd-9471-943d668ba333\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">e-4bcd-9471-943d668ba333'</span></a>.                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=263419;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0e2fa139-898e-4bcd-9471-943d668ba333\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=981432;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0e2fa139-898e-4bcd-9471-943d668ba333\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=263419;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0e2fa139-898e-4bcd-9471-943d668ba333\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=10161;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0e2fa139-898e-4bcd-9471-943d668ba333\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=263419;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0e2fa139-898e-4bcd-9471-943d668ba333\u001b\\\u001b[32m-0e2fa139-898\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=263419;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0e2fa139-898e-4bcd-9471-943d668ba333\u001b\\\u001b[32me-4bcd-9471-943d668ba333'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/folder-7d2988e3-13d2-49df-8e7b-f9b5036adc0b\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=900202;https://tidy3d.simulation.cloud/folders/folder-7d2988e3-13d2-49df-8e7b-f9b5036adc0b\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fcbf8d10ffbf4bca91b9751bbe48b6c7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">17:45:47 EDT </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.111</span>. Minimum cost depends on task       \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m17:45:47 EDT\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.111\u001b[0m. Minimum cost depends on task       \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6f3363a08a714f1e9cc9c39e56ec9ae5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">17:46:02 EDT </span>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m17:46:02 EDT\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">17:46:03 EDT </span>running solver                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m17:46:03 EDT\u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fa04dbf32be144dd9e616391cfa42eb4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">17:46:21 EDT </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">76</span>%, exiting.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m17:46:21 EDT\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m76\u001b[0m%, exiting.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>status = postprocess                                               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "81a2015da01b471eaec619ff85d956d1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">17:46:25 EDT </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m17:46:25 EDT\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">17:46:27 EDT </span>View simulation result at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-0e2fa139-898e-4bcd-9471-943d668ba333\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-0e2fa139-898</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-0e2fa139-898e-4bcd-9471-943d668ba333\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">e-4bcd-9471-943d668ba333'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m17:46:27 EDT\u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=443328;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0e2fa139-898e-4bcd-9471-943d668ba333\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=107683;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0e2fa139-898e-4bcd-9471-943d668ba333\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=443328;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0e2fa139-898e-4bcd-9471-943d668ba333\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=921091;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0e2fa139-898e-4bcd-9471-943d668ba333\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=443328;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0e2fa139-898e-4bcd-9471-943d668ba333\u001b\\\u001b[4;34m-0e2fa139-898\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=443328;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0e2fa139-898e-4bcd-9471-943d668ba333\u001b\\\u001b[4;34me-4bcd-9471-943d668ba333'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "494a5de5d4ff4d6d97d1928e4ebe9aac",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">17:46:29 EDT </span>loading simulation from simulation_data.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m17:46:29 EDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_sim = fn_pre(**best_params)\n",
    "\n",
    "# Define a field monitor to help visualize the field distribution\n",
    "field_monitor = td.FieldMonitor(\n",
    "    center=(0, 0, 0), size=(td.inf, td.inf, 0), freqs=[freq0], name=\"field\"\n",
    ")\n",
    "\n",
    "mode_12 = final_sim.monitors[1]\n",
    "\n",
    "final_sim = final_sim.copy(update={\"monitors\": (field_monitor, mode_12)})\n",
    "\n",
    "final_sim_data = web.run(final_sim, task_name=\"BO_notebook_final_sim\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the field shows how it splits evenly between each branch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_sim_data.plot_field(\"field\", \"E\", \"abs^2\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can visualise how the power transmitted varies over the frequency range. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "power_transmitted = np.squeeze(\n",
    "    np.abs(final_sim_data[\"mode_12\"].amps.sel(direction=\"+\", mode_index=0)) ** 2\n",
    ")\n",
    "plt.plot(freqs, power_transmitted)\n",
    "plt.title(\"Power transmitted across frequency range\")\n",
    "plt.xlabel(\"Frequency / Hz\")\n",
    "plt.ylabel(\"Power\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Through comparison of the junction geometry and the transmitted and reflected power, we can show that these results closely follow the results published by ``Gao et al.`` for the design of a Y-junction. This notebook demonstrates how to carry out Bayesian optimization with the Design plugin, and can be readily adapted to other use cases.\n",
    "\n",
    "Learn more about the Design plugin:\n",
    "\n",
    "1. [Intro to Design Plugin](https://www.flexcompute.com/tidy3d/examples/notebooks/Design/)\n",
    "\n",
    "2. [Particle Swarm Optimizer PBS](https://www.flexcompute.com/tidy3d/examples/notebooks/ParticleSwarmOptimizedPBS/)\n",
    "\n",
    "3. [Particle Swarm Optimizer Bullseye Cavity](https://www.flexcompute.com/tidy3d/examples/notebooks/BullseyeCavityPSO/)\n",
    "\n",
    "4. [Genetic Algorithm Reflector](https://www.flexcompute.com/tidy3d/examples/notebooks/GeneticAlgorithmReflector/)\n",
    "\n",
    "5. [All-Dielectric Structural Colors](https://www.flexcompute.com/tidy3d/examples/notebooks/AllDielectricStructuralColor/)\n",
    "\n",
    "6. [Parameter Scan](https://www.flexcompute.com/tidy3d/examples/notebooks/ParameterScan/)"
   ]
  }
 ],
 "metadata": {
  "applications": [
   "Passive photonic integrated circuit components"
  ],
  "description": "Bayesian optimization is a popular technique to search a design space. In this notebook Bayesian optimization is used to optimize a Y-junction to transmit power equally through each branch.",
  "feature_image": "./img/bayesian_opt_y_junction.png",
  "features": [
   "Design plugin",
   "Global optimization"
  ],
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "Bayesian optimization, Y Junction, FDTD, Tidy3d",
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   "name": "python",
   "nbconvert_exporter": "python",
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  "title": "How to optimize a Y-junction using Bayesian optimization with Tidy3D FDTD simulations"
 },
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