SciML Open Source Scientific Machine Learning

Reservoir computing utilities for scientific machine learning (SciML)

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Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R

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

Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation

A scientific machine learning (SciML) wrapper for the FEniCS Finite Element library in the Julia programming language

A library for building differential equations arising from physical problems for physics-informed and scientific machine learning (SciML)

This package contains a macro for converting expressions to use muladd calls and fused-multiply-add (FMA) operations for high-performance in the SciML scientific machine learning ecosystem

A style guide for stylish Julia developers

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.

Boundary value problem (BVP) solvers for 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)

Adds the common API onto ODEInterface classic Fortran methods for the SciML Scientific Machine Learning organization

Wrappers for calling the R deSolve differential equation solvers from Julia for scientific machine learning (SciML)

A common solve function for scientific machine learning (SciML) and beyond

A structure interface for SciML to give queryable properties from user data and parameters

GPU accelerated Particle Swarm Optimization

Fast and differentiable implementations of matrix exponentials, Krylov exponential matrix-vector multiplications ("expmv"), KIOPS, ExpoKit functions, and more. All your exponential needs in SciML form.

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.

Black-box optimization for Julia

Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)

Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.

The Base interface of the SciML ecosystem

CellMLToolkit.jl is a Julia library that connects CellML models to the Scientific Julia ecosystem.

Fast and automatic structural identifiability software for ODE systems

Line Search Algorithms

SciML-Bench Benchmarks for Scientific Machine Learning (SciML), Physics-Informed Machine Learning (PIML), and Scientific AI Performance

Arrays which also have a label for each element for easy scientific machine learning (SciML)

High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.

Julia-based implementation of ellipsis array indexing notation `..`