Dependency Packages
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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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Turing.jl1807Bayesian inference with probabilistic programming.
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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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Symbolics.jl1169A fast and modern CAS for a fast and modern language.
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DSGE.jl798Solve and estimate Dynamic Stochastic General Equilibrium models (including the New York Fed DSGE)
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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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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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DynamicalSystems.jl725Award winning software library for nonlinear dynamics and nonlinear timeseries analysis
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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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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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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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QuantumOptics.jl459Library for the numerical simulation of closed as well as open quantum systems.
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SymbolicUtils.jl454Expression rewriting and simplification
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ControlSystems.jl430A Control Systems Toolbox for Julia
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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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Soss.jl401Probabilistic programming via source rewriting
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SymbolicRegression.jl377Distributed High-Performance symbolic regression in Julia
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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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Trixi.jl363Trixi.jl: Adaptive high-order numerical simulations of hyperbolic PDEs in Julia
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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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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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DFTK.jl337Density-functional toolkit
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Modia.jl298Modeling and simulation of multidomain engineering systems
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Manifolds.jl297Manifolds.jl provides a library of manifolds aiming for an easy-to-use and fast implementation.
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DiffEqOperators.jl279Linear operators for discretizations of differential equations and scientific machine learning (SciML)
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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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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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DiffEqBase.jl243The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
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BifurcationKit.jl240A Julia package to perform Bifurcation Analysis
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ComponentArrays.jl231Arrays with arbitrarily nested named components.
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DiffEqGPU.jl202GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem
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StochasticDiffEq.jl200Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem
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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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ChaosTools.jl183Tools for the exploration of chaos and nonlinear dynamics
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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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Caesar.jl173Robust robotic localization and mapping, together with www.NavAbility.io. Reach out to info@navability.io for help.
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ReachabilityAnalysis.jl170Methods to compute sets of states reachable by dynamical systems
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Integrals.jl166A common interface for quadrature and numerical integration for the SciML scientific machine learning organization
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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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