Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components

A 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

Universal neural differential equations with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods

Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.

Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.

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

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

High performance differential equation solvers for ordinary differential equations, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)

Local, global, and beyond optimization 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

Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software

Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization

Linear operators for discretizations of differential equations and scientific machine learning (SciML)

Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem

Surrogate modeling and optimization for scientific machine learning (SciML)

Julia interface to Sundials, including a nonlinear solver (KINSOL), ODE's (CVODE and ARKODE), and DAE's (IDA) in a SciML scientific machine learning enabled manner

Benchmarks for scientific machine learning (SciML) software and differential equation solvers

The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems

GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem

Assorted basic Ordinary Differential Equation solvers for scientific machine learning (SciML)

Extension functionality which uses Stan.jl, DynamicHMC.jl, and Turing.jl to estimate the parameters to differential equations and perform Bayesian probabilistic scientific machine learning

Tools for easily handling objects like arrays of arrays and deeper nestings in scientific machine learning (SciML) and other applications

A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, and more for ODEs, SDEs, DDEs, DAEs, etc.

A differentiable simulator for scientific machine learning (SciML) with N-body problems, including astrophysical and molecular dynamics

Reservoir computing utilities for scientific machine learning (SciML)

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

Automatic optimization and parallelization for Scientific Machine Learning (SciML)

A simple domain-specific language (DSL) for defining differential equations for use in scientific machine learning (SciML) and other applications

Functions generated at runtime without world-age issues or overhead

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

A common interface for quadrature and numerical integration for the SciML scientific machine learning organization

Automatic detection of sparsity in pure Julia functions for sparsity-enabled scientific machine learning (SciML)

A framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations

Uncertainty quantification for scientific machine learning (SciML) and differential equations

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

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)

A library of noise processes for stochastic systems like stochastic differential equations (SDEs) and other systems that are present in scientific machine learning (SciML)

Easy scientific machine learning (SciML) parameter estimation with pre-built loss functions

Helicopter Scientific Machine Learning (SciML) Challenge Problem

Utility functions for exponential integrators for the SciML scientific machine learning ecosystem