SciML Open Source Scientific Machine Learning
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DataInterpolations.jl
Public- 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.
- 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)
- Julia Catalyst.jl importers for various reaction network file formats like BioNetGen and stoichiometry matrices
- High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Fast Poisson Random Numbers in pure Julia for scientific machine learning (SciML)
- The Base interface of the 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
GlobalSensitivity.jl
PublicRobust, Fast, and Parallel Global Sensitivity Analysis (GSA) in JuliaBoundaryValueDiffEq.jl
PublicBoundary value problem (BVP) solvers for scientific machine learning (SciML)- Fast and automatic structural identifiability software for ODE systems
Optimization.jl
PublicMathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.SciMLBenchmarks.jl
PublicScientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, RNeuralPDE.jl
PublicPhysics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulationCellMLToolkit.jl
PublicCellMLToolkit.jl is a Julia library that connects CellML models to the Scientific Julia ecosystem.- A standard library of components to model the world and beyond
PreallocationTools.jl
PublicTools for building non-allocating pre-cached functions in Julia, allowing for GC-free usage of automatic differentiation in complex codesBaseModelica.jl
Public- A simple domain-specific language (DSL) for defining differential equations for use in scientific machine learning (SciML) and other applications
- 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.
- Symbolic-Numeric Universal Differential Equations for Automating Scientific Machine Learning (SciML)
- A common solve function for scientific machine learning (SciML) and beyond
- Surrogate modeling and optimization for scientific machine learning (SciML)