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QuasiMonteCarlo.jl
PublicLightweight and easy generation of quasi-Monte Carlo sequences with a ton of different methods on one API for easy parameter exploration in scientific machine learning (SciML)ComponentArrays.jl
PublicArrays with arbitrarily nested named components.DataInterpolations.jl
PublicModelingToolkit.jl
PublicAn acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations- Julia Catalyst.jl importers for various reaction network file formats like BioNetGen and stoichiometry matrices
SciMLBase.jl
PublicThe Base interface of the SciML ecosystemSciMLBenchmarks.jl
PublicScientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, RDiffEqFlux.jl
PublicPre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methodsSciMLStructures.jl
PublicSciMLDocs
PublicGlobal documentation for the Julia SciML Scientific Machine Learning OrganizationOptimizationBase.jl
PublicDifferenceEquations.jl
Public- Fast and automatic structural identifiability software for ODE systems
PoissonRandom.jl
PublicFast Poisson Random Numbers in pure Julia for scientific machine learning (SciML)EllipsisNotation.jl
PublicGlobalSensitivity.jl
PublicRobust, Fast, and Parallel Global Sensitivity Analysis (GSA) in JuliaSciMLSensitivity.jl
PublicA component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.OrdinaryDiffEq.jl
PublicHigh performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)- Implicit Layer Machine Learning via Deep Equilibrium Networks, O(1) backpropagation with accelerated convergence.
NeuralPDE.jl
PublicPhysics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulationIntegrals.jl
PublicA common interface for quadrature and numerical integration for the SciML scientific machine learning organizationSciMLExpectations.jl
PublicFast uncertainty quantification for scientific machine learning (SciML) and differential equationsRecursiveArrayTools.jl
PublicDiffEqParamEstim.jl
PublicEasy scientific machine learning (SciML) parameter estimation with pre-built loss functionsSurrogates.jl
PublicSurrogate modeling and optimization for scientific machine learning (SciML)HighDimPDE.jl
PublicA Julia package for Deep Backwards Stochastic Differential Equation (Deep BSDE) and Feynman-Kac methods to solve high-dimensional PDEs without the curse of dimensionalityReservoirComputing.jl
PublicReservoir computing utilities for scientific machine learning (SciML)Catalyst.jl
PublicChemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.FindFirstFunctions.jl
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