113 Packages since 2013
User Packages
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Sundials.jl188Julia interface to Sundials, including a nonlinear solver (KINSOL), ODE's (CVODE and ARKODE), and DAE's (IDA) in a SciML scientific machine learning enabled manner
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LinearSolve.jl178LinearSolve.jl: High-Performance Unified Interface for Linear Solvers in Julia. Easily switch between factorization and Krylov methods, add preconditioners, and all in one interface.
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NeuralOperators.jl151DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia
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ModelingToolkitStandardLibrary.jl76A standard library of components to model the world and beyond
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SciMLOperators.jl30SciMLOperators.jl: Matrix-Free Operators for the SciML Scientific Machine Learning Common Interface in Julia
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RootedTrees.jl34A collection of functionality around rooted trees to generate order conditions for Runge-Kutta methods in Julia for differential equations and scientific machine learning (SciML)
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Optimization.jl512Mathematical 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.
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GalacticOptim.jl512Mathematical 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.
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SciMLBenchmarks.jl252Benchmarks for scientific machine learning (SciML) software, scientific AI, and (differential) equation solvers
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SymbolicNumericIntegration.jl93SymbolicNumericIntegration.jl: Symbolic-Numerics for Solving Integrals
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ModelingToolkit.jl1212An 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
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HighDimPDE.jl60A Julia package that breaks down the curse of dimensionality in solving PDEs.
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SciMLSensitivity.jl248A 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.
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DiffEqSensitivity.jl248A 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.
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NonlinearSolve.jl112High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.
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DataDrivenDiffEq.jl372Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization
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OrdinaryDiffEq.jl425High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
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NeuralPDE.jl755Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
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NeuralNetDiffEq.jl755Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
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DiffEqFlux.jl771Universal neural differential equations with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods
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DifferentialEquations.jl2503Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.
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StochasticDiffEq.jl200Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem
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DiffEqBase.jl243The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
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ModelOrderReduction.jl27High-level model-order reduction to automate the acceleration of large-scale simulations
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GlobalSensitivity.jl31Robust, Fast, and Parallel Global Sensitivity Analysis (GSA) in Julia
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Surrogates.jl281Surrogate modeling and optimization for scientific machine learning (SciML)
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MethodOfLines.jl118Automatic Finite Difference PDE solving with Julia SciML
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PDEBase.jl9Common types and interface for discretizers of ModelingToolkit PDESystems.
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SciMLBase.jl90The Base interface of the SciML ecosystem
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Catalyst.jl342Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software
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DiffEqBiological.jl342Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software
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ReservoirComputing.jl172Reservoir computing utilities for scientific machine learning (SciML)
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TruncatedStacktraces.jl25Simpler stacktraces for the Julia Programming Language
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EasyModelAnalysis.jl74High level functions for analyzing the output of simulations
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RecursiveArrayTools.jl166Tools for easily handling objects like arrays of arrays and deeper nestings in scientific machine learning (SciML) and other applications
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DiffEqTutorials.jl694Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
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SciMLTutorials.jl694Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
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DiffEqDevTools.jl43Benchmarking, testing, and development tools for differential equations and scientific machine learning (SciML)
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Static.jl49Static types useful for dispatch and generated functions.
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DiffEqBayes.jl117Extension functionality which uses Stan.jl, DynamicHMC.jl, and Turing.jl to estimate the parameters to differential equations and perform Bayesian probabilistic scientific machine learning
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