SciML Open Source Scientific Machine Learning
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SciMLBase.jl
PublicThe Base interface of the SciML ecosystem- Pre-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 methods
- Global documentation for the Julia SciML Scientific Machine Learning Organization
OptimizationBase.jl
PublicOrdinaryDiffEq.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)- A 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.
- Implicit Layer Machine Learning via Deep Equilibrium Networks, O(1) backpropagation with accelerated convergence.
- Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
- Easy scientific machine learning (SciML) parameter estimation with pre-built loss functions
- A common interface for quadrature and numerical integration for the SciML scientific machine learning organization
- Fast uncertainty quantification for scientific machine learning (SciML) and differential equations
- Surrogate modeling and optimization for scientific machine learning (SciML)
- A Julia package for Deep Backwards Stochastic Differential Equation (Deep BSDE) and Feynman-Kac methods to solve high-dimensional PDEs without the curse of dimensionality
- Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
- Arrays with arbitrarily nested named components.
- A general interface for symbolic indexing of SciML objects used in conjunction with Domain-Specific Languages
- Lightweight 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)
- An 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
- Fast and automatic structural identifiability software for ODE systems
- Fast Poisson Random Numbers in pure Julia for scientific machine learning (SciML)
GlobalSensitivity.jl
PublicRobust, Fast, and Parallel Global Sensitivity Analysis (GSA) in JuliaReservoirComputing.jl
PublicReservoir computing utilities for scientific machine learning (SciML)- Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.