GlobalSensitivity.jl
GlobalSensitivity.jl: Performant and Parallel Global Sensitivity Analysis with Julia - Published in JOSS (2022)
Kinetic.jl
Kinetic.jl: A portable finite volume toolbox for scientific and neural computing - Published in JOSS (2021)
WorldDynamics.jl
WorldDynamics.jl: A Julia Package for Developing and Simulating Integrated Assessment Models - Published in JOSS (2024)
DynamicOED.jl
DynamicOED.jl: A Julia package for solving optimum experimental design problems - Published in JOSS (2024)
BracketingNonlinearSolve
High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.
Catalyst
Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.
Sundials
Julia 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
ModelingToolkit
An 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
DelayDiffEq
Delay differential equation (DDE) solvers in Julia for the SciML scientific machine learning ecosystem. Covers neutral and retarded delay differential equations, and differential-algebraic equations.
NeuralPDE
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
SciMLBenchmarks
Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
ImplicitDiscreteSolve
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
RootedTrees
A 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
SBML differential equation and chemical reaction model (Gillespie simulations) for Julia's SciML ModelingToolkit
JumpProcesses
Build and simulate jump equations like Gillespie simulations and jump diffusions with constant and state-dependent rates and mix with differential equations and scientific machine learning (SciML)
scimlbook
Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)
DiffEqBase
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
https://github.com/sciml/surrogates.jl
Surrogate modeling and optimization for scientific machine learning (SciML)
RecursiveArrayTools
Tools for easily handling objects like arrays of arrays and deeper nestings in scientific machine learning (SciML) and other applications
ArrayInterface
Designs for new Base array interface primitives, used widely through scientific machine learning (SciML) and other organizations
StochasticDiffEq
Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem
BoundaryValueDiffEq
Boundary value problem (BVP) solvers for scientific machine learning (SciML)
SciMLSensitivity
A 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.
DifferentialEquations
Multi-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.
DiffEqNoiseProcess
A library of noise processes for stochastic systems like stochastic differential equations (SDEs) and other systems that are present in scientific machine learning (SciML)
https://github.com/SciML/Optimization.jl
Mathematical 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.
Integrals
A common interface for quadrature and numerical integration for the SciML scientific machine learning organization
MuladdMacro
This 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
LabelledArrays
Arrays which also have a label for each element for easy scientific machine learning (SciML)
SteadyStateDiffEq
Solvers for steady states in scientific machine learning (SciML)
PoissonRandom
Fast Poisson Random Numbers in pure Julia for scientific machine learning (SciML)
resettablestacks.jl
A stack implementation with a reset! function which avoids garbage collection for scientific machine learning (SciML)
SciMLTutorials
Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
ParameterizedFunctions
A simple domain-specific language (DSL) for defining differential equations for use in scientific machine learning (SciML) and other applications
diffeqpy
Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization
ReservoirComputing
Reservoir computing utilities for scientific machine learning (SciML)
MultiScaleArrays
A framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations
DiffEqBayes
Extension functionality which uses Stan.jl, DynamicHMC.jl, and Turing.jl to estimate the parameters to differential equations and perform Bayesian probabilistic scientific machine learning
DiffEqPhysics
A library for building differential equations arising from physical problems for physics-informed and scientific machine learning (SciML)
DiffEqDevTools
Benchmarking, testing, and development tools for differential equations and scientific machine learning (SciML)
SimpleDiffEq
Simple differential equation solvers in native Julia for scientific machine learning (SciML)
DiffEqFinancial
Differential equation problem specifications and scientific machine learning for common financial models
DimensionalPlotRecipes
High dimensional numbers and reductions recipes for data visualization of scientific machine learning (SciML)
FEniCS
A scientific machine learning (SciML) wrapper for the FEniCS Finite Element library in the Julia programming language
SciMLExpectations
Fast uncertainty quantification for scientific machine learning (SciML) and differential equations
exponentialutilities.jl
Fast and differentiable implementations of matrix exponentials, Krylov exponential matrix-vector multiplications ("expmv"), KIOPS, ExpoKit functions, and more. All your exponential needs in SciML form.
DASKR
Interface to DASKR, a differential algebraic system solver for the SciML scientific machine learning ecosystem
StochasticDelayDiffEq
Stochastic delay differential equations (SDDE) solvers for the SciML scientific machine learning ecosystem
DASSL
Solves stiff differential algebraic equations (DAE) using variable stepsize backwards finite difference formula (BDF) in the SciML scientific machine learning organization
MATLABDiffEq
Common interface bindings for the MATLAB ODE solvers via MATLAB.jl for the SciML Scientific Machine Learning ecosystem
diffeqdevdocs.jl
Developer documentation for the SciML scientific machine learning ecosystem's differential equation solvers
geometricintegratorsdiffeq.jl
Wrappers for GeometricIntegrators.jl into the SciML common interface for scientific machine learning (SciML)
sophon.jl
Efficient, Accurate, and Streamlined Training of Physics-Informed Neural Networks
https://github.com/akio-tomiya/latticeqcd.jl
A native Julia code for lattice QCD with dynamical fermions in 4 dimension.
scimlbenchmarksoutput
SciML-Bench Benchmarks for Scientific Machine Learning (SciML), Physics-Informed Machine Learning (PIML), and Scientific AI Performance
DiffEqCallbacks
A library of useful callbacks for hybrid scientific machine learning (SciML) with augmented differential equation solvers
BVProblemLibrary
A library of premade problems for examples and testing differential equation solvers and other SciML scientific machine learning tools
diffeqdocs.jl
Documentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystem
NBodySimulator
A differentiable simulator for scientific machine learning (SciML) with N-body problems, including astrophysical and molecular dynamics
ODEInterfaceDiffEq
Adds the common API onto ODEInterface classic Fortran methods for the SciML Scientific Machine Learning organization
diffeqonline
It's Angular2 business in the front, and a Julia party in the back! It's scientific machine learning (SciML) for the web
pderoadmap
A repository for the discussion of PDE tooling for scientific machine learning (SciML) and physics-informed machine learning
diffeqonlineserver
Backend for DiffEqOnline, a webapp for scientific machine learning (SciML)
diffeqdevmaterials
Various developer materials, like PDFs, notes, derivations, etc. for differential equations and scientific machine learning (SciML)
scimltutorialsoutput
Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
https://github.com/avik-pal/regneuralde.jl
Official Implementation of "Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics" (ICML 2021)
BridgeDiffEq
A thin wrapper over Bridge.jl for the SciML scientific machine learning common interface, enabling new methods for neural stochastic differential equations (neural SDEs)
SciMLWorkshop
Workshop materials for training in scientific computing and scientific machine learning