SciML Open Source Scientific Machine Learning
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- Reservoir computing utilities 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.
- A general interface for symbolic indexing of SciML objects used in conjunction with Domain-Specific Languages
- GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem
- The Base interface of the SciML ecosystem
- A standard library of components to model the world and beyond
- SciML-Bench Benchmarks for Scientific Machine Learning (SciML), Physics-Informed Machine Learning (PIML), and Scientific AI Performance
- 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
- Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
- Fast and automatic structural identifiability software for ODE systems
- High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
- Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
- A common interface for quadrature and numerical integration for the SciML scientific machine learning organization
- 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.
Surrogates.jl
PublicSurrogate modeling and optimization for scientific machine learning (SciML)SciMLStructures.jl
PublicDiffEqFlux.jl
PublicPre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methodsSciMLDocs
PublicGlobal documentation for the Julia SciML Scientific Machine Learning OrganizationOptimizationBase.jl
Public- Implicit Layer Machine Learning via Deep Equilibrium Networks, O(1) backpropagation with accelerated convergence.
DiffEqParamEstim.jl
PublicEasy scientific machine learning (SciML) parameter estimation with pre-built loss functionsRecursiveArrayTools.jl
PublicSciMLExpectations.jl
PublicFast uncertainty quantification for scientific machine learning (SciML) and differential equationsHighDimPDE.jl
PublicA Julia package for Deep Backwards Stochastic Differential Equation (Deep BSDE) and Feynman-Kac methods to solve high-dimensional PDEs without the curse of dimensionalityComponentArrays.jl
PublicArrays with arbitrarily nested named components.QuasiMonteCarlo.jl
PublicLightweight 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)