SciML Open Source Scientific Machine Learning
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180 repositories
- The Base interface of the SciML ecosystem
- High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- 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)
- Arrays with arbitrarily nested named components.
- Julia Catalyst.jl importers for various reaction network file formats like BioNetGen and stoichiometry matrices
- Boundary value problem (BVP) solvers for scientific machine learning (SciML)
- Fast and automatic structural identifiability software for ODE systems
- Fast Poisson Random Numbers in pure Julia for scientific machine learning (SciML)
EllipsisNotation.jl
PublicGlobalSensitivity.jl
PublicRobust, Fast, and Parallel Global Sensitivity Analysis (GSA) in Julia- SciML-Bench Benchmarks for Scientific Machine Learning (SciML), Physics-Informed Machine Learning (PIML), and Scientific AI Performance
SciMLBenchmarks.jl
PublicScientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R- Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization
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.- CellMLToolkit.jl is a Julia library that connects CellML models to the Scientific Julia ecosystem.
- Tools for building non-allocating pre-cached functions in Julia, allowing for GC-free usage of automatic differentiation in complex codes
- Easy scientific machine learning (SciML) parameter estimation with pre-built loss functions
DiffEqFlux.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 methods- A simple domain-specific language (DSL) for defining differential equations for use in scientific machine learning (SciML) and other applications
CommonSolve.jl
PublicA common solve function for scientific machine learning (SciML) and beyond- 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
- A standard library of components to model the world and beyond
- High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.