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- High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.
- Symbolic-Numeric Neural DAEs and Universal Differential Equations for Automating Scientific Machine Learning (SciML)
- A common solve function for scientific machine learning (SciML) and beyond
NeuralPDE.jl
PublicPhysics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation- Fast uncertainty quantification for scientific machine learning (SciML) and differential equations
GlobalSensitivity.jl
PublicRobust, Fast, and Parallel Global Sensitivity Analysis (GSA) in JuliaReservoirComputing.jl
PublicReservoir computing utilities for scientific machine learning (SciML)Surrogates.jl
PublicSurrogate modeling and optimization for scientific machine learning (SciML)CellMLToolkit.jl
PublicCellMLToolkit.jl is a Julia library that connects CellML models to the Scientific Julia ecosystem.- 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.
IRKGaussLegendre.jl
Public- A general interface for symbolic indexing of SciML objects used in conjunction with Domain-Specific Languages
DiffEqCallbacks.jl
PublicA library of useful callbacks for hybrid scientific machine learning (SciML) with augmented differential equation solversDataInterpolationsND.jl
PublicBridgeDiffEq.jl
PublicA thin wrapper over Bridge.jl for the SciML scientific machine learning common interface, enabling new methods for neural stochastic differential equations (neural SDEs)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.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 equationsSBMLToolkitTestSuite.jl
PublicDiffEqParamEstim.jl
PublicEasy scientific machine learning (SciML) parameter estimation with pre-built loss functionsModelOrderReduction.jl
PublicHigh-level model-order reduction to automate the acceleration of large-scale simulationsCatalyst.jl
PublicChemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.SciMLBook
PublicParallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)DiffEqGPU.jl
PublicGPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystemMultiScaleArrays.jl
PublicA framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations- Automatic Finite Difference PDE solving with Julia SciML
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)SymbolicAnalysis.jl
PublicODEInterfaceDiffEq.jl
Public