52 open source projects by SciML

High performance differential equation solvers for ordinary differential equations, including neural ordinary differential equations (neural ODEs) and 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

Local, global, and beyond optimization for scientific machine learning (SciML)

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

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

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

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

Surrogate modeling and optimization for scientific machine learning (SciML)

Documentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystem

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

Assorted basic Ordinary Differential Equation solvers for 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)

Automatic GPU, TPU, FPGA, Xeon Phi, Multithreaded, Distributed, etc. offloading for scientific machine learning (SciML) and differential equations

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.

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

Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization

The SciML Scientific Machine Learning Software Organization Website

The Base interface of the SciML ecosystem

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

Wrappers for the SciPy differential equation solvers for the SciML Scientific Machine Learning organization

Fast Poisson Random Numbers in pure Julia for scientific machine learning (SciML)

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

A collection of functionality around rooted trees to generate order conditions for Runge-Kutta methods in Julia for differential equations and scientific machine learning (SciML)

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

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

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

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

symbolicnumericintegration.sciml.ai/dev/

A library for building differential equations arising from physical problems for physics-informed 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, and more for ODEs, SDEs, DDEs, DAEs, etc.

SBML differential equation and chemical reaction model (Gillespie simulations) for Julia's SciML ModelingToolkit

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)

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

Speed at all costs

Automatic optimization and parallelization for Scientific Machine Learning (SciML)

Benchmarking, testing, and development tools for differential equations and scientific machine learning (SciML)

Julia Catalyst.jl importers for various reaction network file formats like BioNetGen and stoichiometry matrices

Helicopter Scientific Machine Learning (SciML) Challenge Problem

Boundary value problem (BVP) solvers for scientific machine learning (SciML)

A library of useful callbacks for hybrid scientific machine learning (SciML) with augmented differential equation solvers

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

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

Solving differential equations in R using DifferentialEquations.jl and the SciML Scientific Machine Learning ecosystem

Fast uncertainty quantification for scientific machine learning (SciML) and differential equations

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

Implicit Runge-Kutta Gauss-Legendre 16th order (Julia)

Finds relationships between the parameters of a mathematical model

Fast and automatic structural identifiability software for ODE systems