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LinearSolve.jl
PublicLinearSolve.jl: High-Performance Unified Interface for Linear Solvers in Julia. Easily switch between factorization and Krylov methods, add preconditioners, and all in one interface.- Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
- Reservoir computing utilities for scientific machine learning (SciML)
ModelingToolkit.jl
PublicAn 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 equationsNonlinearSolve.jl
PublicHigh-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.SciMLBook
PublicParallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)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)- Julia Catalyst.jl importers for various reaction network file formats like BioNetGen and stoichiometry matrices
- 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
DiffEqFlux.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 methodsPoissonRandom.jl
PublicFast Poisson Random Numbers in pure Julia for scientific machine learning (SciML)EllipsisNotation.jl
PublicSciMLBenchmarksOutput
PublicSciML-Bench Benchmarks for Scientific Machine Learning (SciML), Physics-Informed Machine Learning (PIML), and Scientific AI PerformanceSciMLBenchmarks.jl
PublicScientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, RSciMLBase.jl
PublicThe Base interface of the SciML ecosystemNeuralLyapunov.jl
PublicFindFirstFunctions.jl
PublicSurrogates.jl
PublicSurrogate modeling and optimization for scientific machine learning (SciML)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.DataInterpolations.jl
PublicCellMLToolkit.jl
PublicCellMLToolkit.jl is a Julia library that connects CellML models to the Scientific Julia ecosystem..github
PublicSciMLDocs
PublicGlobal documentation for the Julia SciML Scientific Machine Learning OrganizationComponentArrays.jl
PublicArrays with arbitrarily nested named components.OrdinaryDiffEq.jl
PublicHigh performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)PreallocationTools.jl
PublicTools for building non-allocating pre-cached functions in Julia, allowing for GC-free usage of automatic differentiation in complex codesSciMLStructures.jl
PublicBaseModelica.jl
Public