SciML Open Source Scientific Machine Learning
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180 repositories
- The Base interface of the SciML ecosystem
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 equationsOptimization.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.SciMLStructures.jl
Public- High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
SciMLBenchmarks.jl
PublicScientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, RBoundaryValueDiffEq.jl
PublicBoundary value problem (BVP) solvers for scientific machine learning (SciML)NonlinearSolve.jl
PublicHigh-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.SurrogatesBase.jl
PublicExponentialUtilities.jl
PublicFast and differentiable implementations of matrix exponentials, Krylov exponential matrix-vector multiplications ("expmv"), KIOPS, ExpoKit functions, and more. All your exponential needs in SciML form.NeuralOperators.jl
PublicDataInterpolations.jl
PublicQuasiMonteCarlo.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)ModelingToolkitCourse
PublicA course on composable system modeling, differential-algebraic equations, acausal modeling, compilers for simulation, and building digital twins of real-world devicesSciMLSensitivity.jl
PublicA 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.SciMLStyle
PublicModelOrderReduction.jl
PublicHigh-level model-order reduction to automate the acceleration of large-scale simulationsPDEBase.jl
PublicDiffEqNoiseProcess.jl
PublicA library of noise processes for stochastic systems like stochastic differential equations (SDEs) and other systems that are present in scientific machine learning (SciML)CommonSolve.jl
PublicA common solve function for scientific machine learning (SciML) and beyondSciMLOperators.jl
PublicSciMLOperators.jl: Matrix-Free Operators for the SciML Scientific Machine Learning Common Interface in JuliaDataDrivenDiffEq.jl
PublicData driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organizationSymbolicLimits.jl
Public- Fast and automatic structural identifiability software for ODE systems
- SymbolicNumericIntegration.jl: Symbolic-Numerics for Solving Integrals
FEniCS.jl
PublicA scientific machine learning (SciML) wrapper for the FEniCS Finite Element library in the Julia programming languageFastPower.jl
PublicStochasticDiffEq.jl
PublicSolvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystemSBMLToolkit.jl
PublicSBML differential equation and chemical reaction model (Gillespie simulations) for Julia's SciML ModelingToolkit