SciML Open Source Scientific Machine Learning
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CommonSolve.jl
PublicA common solve function for scientific machine learning (SciML) and beyond- Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.
- LinearSolve.jl: High-Performance Unified Interface for Linear Solvers in Julia. Easily switch between factorization and Krylov methods, add preconditioners, and all in one interface.
- Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
- High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.
- Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem
- 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
- Automatic Finite Difference PDE solving with Julia SciML
- Delay differential equation (DDE) solvers in Julia for the SciML scientific machine learning ecosystem. Covers neutral and retarded delay differential equations, and differential-algebraic equations.
- A general interface for symbolic indexing of SciML objects used in conjunction with Domain-Specific Languages
- High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
- Build and simulate jump equations like Gillespie simulations and jump diffusions with constant and state-dependent rates and mix with differential equations and scientific machine learning (SciML)
- The Base interface of the SciML ecosystem
- Stochastic delay differential equations (SDDE) solvers for the SciML scientific machine learning ecosystem
- Fast and automatic structural identifiability software for ODE systems
- A standard library of components to model the world and beyond
- 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.
- 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)
- Julia Catalyst.jl importers for various reaction network file formats like BioNetGen and stoichiometry matrices
- The SciML Scientific Machine Learning Software Organization Website
- SciML-Bench Benchmarks for Scientific Machine Learning (SciML), Physics-Informed Machine Learning (PIML), and Scientific AI Performance
- Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
- Fast Poisson Random Numbers in pure Julia for scientific machine learning (SciML)