113 Packages since 2013
User Packages
-
FEniCS.jl84A scientific machine learning (SciML) wrapper for the FEniCS Finite Element library in the Julia programming language
-
EllipsisNotation.jl80Julia-based implementation of ellipsis array indexing notation `..`
-
PreallocationTools.jl78Tools for building non-allocating pre-cached functions in Julia, allowing for GC-free usage of automatic differentiation in complex codes
-
ModelingToolkitStandardLibrary.jl76A standard library of components to model the world and beyond
-
QuasiMonteCarlo.jl75Lightweight 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)
-
ExponentialUtilities.jl75Fast and differentiable implementations of matrix exponentials, Krylov exponential matrix-vector multiplications ("expmv"), KIOPS, ExpoKit functions, and more. All your exponential needs in SciML form.
-
EasyModelAnalysis.jl74High level functions for analyzing the output of simulations
-
ParameterizedFunctions.jl72A simple domain-specific language (DSL) for defining differential equations for use in scientific machine learning (SciML) and other applications
-
MultiScaleArrays.jl64A framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations
-
StructuralIdentifiability.jl63Fast and automatic structural identifiability software for ODE systems
-
HighDimPDE.jl60A Julia package that breaks down the curse of dimensionality in solving PDEs.
-
DiffEqUncertainty.jl59Fast uncertainty quantification for scientific machine learning (SciML) and differential equations
-
SciMLExpectations.jl59Fast uncertainty quantification for scientific machine learning (SciML) and differential equations
-
SparsityDetection.jl58Automatic detection of sparsity in pure Julia functions for sparsity-enabled scientific machine learning (SciML)
-
DiffEqNoiseProcess.jl55A library of noise processes for stochastic systems like stochastic differential equations (SDEs) and other systems that are present in scientific machine learning (SciML)
-
DiffEqCallbacks.jl52A library of useful callbacks for hybrid scientific machine learning (SciML) with augmented differential equation solvers
-
DiffEqParamEstim.jl51Easy scientific machine learning (SciML) parameter estimation with pre-built loss functions
-
CellMLToolkit.jl51CellMLToolkit.jl is a Julia library that connects CellML models to the Scientific Julia ecosystem.
-
Static.jl49Static types useful for dispatch and generated functions.
-
DelayDiffEq.jl46Delay differential equation (DDE) solvers in Julia for the SciML scientific machine learning ecosystem. Covers neutral and retarded delay differential equations, and differential-algebraic equations.
-
DiffEqPhysics.jl46A library for building differential equations arising from physical problems for physics-informed and scientific machine learning (SciML)
-
MinimallyDisruptiveCurves.jl43Finds relationships between the parameters of a mathematical model
-
DiffEqDevTools.jl43Benchmarking, testing, and development tools for differential equations and scientific machine learning (SciML)
-
DeepEquilibriumNetworks.jl41Implicit Layer Machine Learning via Deep Equilibrium Networks, O(1) backpropagation with accelerated convergence.
-
SimpleNonlinearSolve.jl39Fast and simple nonlinear solvers for the SciML common interface. Newton, Broyden, Bisection, Falsi, and more rootfinders on a standard interface.
-
DiffEqProblemLibrary.jl39A library of premade problems for examples and testing differential equation solvers and other SciML scientific machine learning tools
-
MuladdMacro.jl39This package contains a macro for converting expressions to use muladd calls and fused-multiply-add (FMA) operations for high-performance in the SciML scientific machine learning ecosystem
-
OperatorLearning.jl37No need to train, he's a smooth operator
-
HelicopterSciML.jl36Helicopter Scientific Machine Learning (SciML) Challenge Problem
-
SciMLWorkshop.jl34Workshop materials for training in scientific computing and scientific machine learning
-
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)
-
SBMLToolkit.jl31SBML differential equation and chemical reaction model (Gillespie simulations) for Julia's SciML ModelingToolkit
-
DASSL.jl31Solves stiff differential algebraic equations (DAE) using variable stepsize backwards finite difference formula (BDF) in the SciML scientific machine learning organization
-
GlobalSensitivity.jl31Robust, Fast, and Parallel Global Sensitivity Analysis (GSA) in Julia
-
SciMLOperators.jl30SciMLOperators.jl: Matrix-Free Operators for the SciML Scientific Machine Learning Common Interface in Julia
-
DifferenceEquations.jl28Solving difference equations with DifferenceEquations.jl and the SciML ecosystem.
-
ModelOrderReduction.jl27High-level model-order reduction to automate the acceleration of large-scale simulations
-
TruncatedStacktraces.jl25Simpler stacktraces for the Julia Programming Language
-
BoundaryValueDiffEq.jl25Boundary value problem (BVP) solvers for scientific machine learning (SciML)
-
PDESystemLibrary.jl24A library of systems of partial differential equations, as defined with ModelingToolkit.jl in Julia
Loading more...