SciML Open Source Scientific Machine Learning
Change the repository type filter
All
Repositories list
183 repositories
- Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
- CellMLToolkit.jl is a Julia library that connects CellML models to the Scientific Julia 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
- A general interface for symbolic indexing of SciML objects used in conjunction with Domain-Specific Languages
- Tools for building non-allocating pre-cached functions in Julia, allowing for GC-free usage of automatic differentiation in complex codes
- A simple domain-specific language (DSL) for defining differential equations for use in scientific machine learning (SciML) and other applications
- A common solve function for scientific machine learning (SciML) and beyond
- The Base interface of the SciML ecosystem
- Symbolic-Numeric Universal Differential Equations for Automating 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.
- Reservoir computing utilities for scientific machine learning (SciML)
- 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.
- Fast and automatic structural identifiability software for ODE systems
- A library of useful callbacks for hybrid scientific machine learning (SciML) with augmented differential equation solvers
- High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and 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
- 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)
- 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.
- A standard library of components to model the world and beyond
- Julia Catalyst.jl importers for various reaction network file formats like BioNetGen and stoichiometry matrices
- High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.
- Fast Poisson Random Numbers in pure Julia for scientific machine learning (SciML)