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99 search results found
Differentialequations.jl
⭐
2,689
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.
Modelingtoolkit.jl
⭐
1,292
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
Neuralpde.jl
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864
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
Scimltutorials.jl
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698
Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
Optimization.jl
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625
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.
Symbolicregression.jl
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484
Distributed High-Performance Symbolic Regression in Julia
Ordinarydiffeq.jl
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479
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
Diffeqpy
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456
Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization
Catalyst.jl
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402
Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.
Datadrivendiffeq.jl
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393
Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization
Surrogates.jl
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302
Surrogate modeling and optimization for scientific machine learning (SciML)
Scimlsensitivity.jl
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291
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.
Diffeqbase.jl
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282
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
Scimlbenchmarks.jl
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279
Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
Diffeqoperators.jl
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279
Linear operators for discretizations of differential equations and scientific machine learning (SciML)
Diffeqgpu.jl
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261
GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem
Diffeqdocs.jl
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251
Documentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystem
Stochasticdiffeq.jl
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229
Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem
Linearsolve.jl
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211
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.
Sundials.jl
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200
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
Reservoircomputing.jl
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192
Reservoir computing utilities for scientific machine learning (SciML)
Integrals.jl
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191
A common interface for quadrature and numerical integration for the SciML scientific machine learning organization
Recursivearraytools.jl
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190
Tools for easily handling objects like arrays of arrays and deeper nestings in scientific machine learning (SciML) and other applications
Universal_differential_equations
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187
Repository for the Universal Differential Equations for Scientific Machine Learning paper, describing a computational basis for high performance SciML
Scimlstyle
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160
A style guide for stylish Julia developers
Nonlinearsolve.jl
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154
High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.
Methodoflines.jl
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144
Automatic Finite Difference PDE solving with Julia SciML
Arrayinterface.jl
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130
Designs for new Base array interface primitives, used widely through scientific machine learning (SciML) and other organizations
Jumpprocesses.jl
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127
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)
Diffeqr
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127
Solving differential equations in R using DifferentialEquations.jl and the SciML Scientific Machine Learning ecosystem
Nbodysimulator.jl
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122
A differentiable simulator for scientific machine learning (SciML) with N-body problems, including astrophysical and molecular dynamics
Latticeqcd.jl
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120
A native Julia code for lattice QCD with dynamical fermions in 4 dimension.
Diffeqbayes.jl
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119
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
Labelledarrays.jl
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114
Arrays which also have a label for each element for easy scientific machine learning (SciML)
Scimlbase.jl
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111
The Base interface of the SciML ecosystem
Kinetic.jl
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110
Universal modeling and simulation of fluid mechanics upon machine learning. From the Boltzmann equation, heading towards multiscale and multiphysics flows.
Ode.jl
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106
Assorted basic Ordinary Differential Equation solvers for scientific machine learning (SciML). Deprecated: Use DifferentialEquations.jl instead.
Quasimontecarlo.jl
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95
Lightweight 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)
Modelingtoolkitstandardlibrary.jl
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89
A standard library of components to model the world and beyond
Fenics.jl
⭐
86
A scientific machine learning (SciML) wrapper for the FEniCS Finite Element library in the Julia programming language
Exponentialutilities.jl
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85
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.
Diffeqcallbacks.jl
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77
A library of useful callbacks for hybrid scientific machine learning (SciML) with augmented differential equation solvers
Autooptimize.jl
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75
Automatic optimization and parallelization for Scientific Machine Learning (SciML)
Parameterizedfunctions.jl
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75
A simple domain-specific language (DSL) for defining differential equations for use in scientific machine learning (SciML) and other applications
Multiscalearrays.jl
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69
A framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations
Highdimpde.jl
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61
A Julia package for Deep Backwards Stochastic Differential Equation (Deep BSDE) and Feynman-Kac methods to solve high-dimensional PDEs without the curse of dimensionality
Diffeqnoiseprocess.jl
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61
A library of noise processes for stochastic systems like stochastic differential equations (SDEs) and other systems that are present in scientific machine learning (SciML)
Scimlexpectations.jl
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61
Fast uncertainty quantification for scientific machine learning (SciML) and differential equations
Simplenonlinearsolve.jl
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57
Fast and simple nonlinear solvers for the SciML common interface. Newton, Broyden, Bisection, Falsi, and more rootfinders on a standard interface.
Crnn
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57
Chemical Reaction Neural Network
Sparsitydetection.jl
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53
Automatic detection of sparsity in pure Julia functions for sparsity-enabled scientific machine learning (SciML)
Cellmltoolkit.jl
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53
CellMLToolkit.jl is a Julia library that connects CellML models to the Scientific Julia ecosystem.
Worlddynamics.jl
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53
An open-source framework written in Julia for global integrated assessment models.
Delaydiffeq.jl
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53
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.
Diffeqproblemlibrary.jl
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51
A library of premade problems for examples and testing differential equation solvers and other SciML scientific machine learning tools
Sophon.jl
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50
Efficient, Accurate, and Streamlined Training of Physics-Informed Neural Networks
Odinn.jl
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49
Global glacier model using Universal Differential Equations for climate-glacier interactions
Sciml.ai
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48
The SciML Scientific Machine Learning Software Organization Website
Diffeqphysics.jl
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47
A library for building differential equations arising from physical problems for physics-informed and scientific machine learning (SciML)
Diffeqdevtools.jl
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46
Benchmarking, testing, and development tools for differential equations and scientific machine learning (SciML)
Muladdmacro.jl
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45
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
Globalsensitivity.jl
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41
Robust, Fast, and Parallel Global Sensitivity Analysis (GSA) in Julia
Scimloperators.jl
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41
SciMLOperators.jl: Matrix-Free Operators for the SciML Scientific Machine Learning Common Interface in Julia
Boundaryvaluediffeq.jl
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39
Boundary value problem (BVP) solvers for scientific machine learning (SciML)
Helicoptersciml.jl
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37
Helicopter Scientific Machine Learning (SciML) Challenge Problem
Rootedtrees.jl
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36
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.jl
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35
SBML differential equation and chemical reaction model (Gillespie simulations) for Julia's SciML ModelingToolkit
Scimlworkshop.jl
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34
Workshop materials for training in scientific computing and scientific machine learning
Latentdiffeq.jl
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33
Latent Differential Equations models in Julia.
Dassl.jl
⭐
31
Solves stiff differential algebraic equations (DAE) using variable stepsize backwards finite difference formula (BDF) in the SciML scientific machine learning organization
Modelorderreduction.jl
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30
High-level model-order reduction to automate the acceleration of large-scale simulations
Autooffload.jl
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29
Automatic GPU, TPU, FPGA, Xeon Phi, Multithreaded, Distributed, etc. offloading for scientific machine learning (SciML) and differential equations
Diffeqonline
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26
It's Angular2 business in the front, and a Julia party in the back! It's scientific machine learning (SciML) for the web
Regneuralde.jl
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25
Official Implementation of "Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics" (ICML 2021)
Steadystatediffeq.jl
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25
Solvers for steady states in scientific machine learning (SciML)
Diffeqfinancial.jl
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23
Differential equation problem specifications and scientific machine learning for common financial models
Modelingtoolkitcourse
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22
A course on composable system modeling, differential-algebraic equations, acausal modeling, compilers for simulation, and building digital twins of real-world devices
Stochasticdelaydiffeq.jl
⭐
22
Stochastic delay differential equations (SDDE) solvers for the SciML scientific machine learning ecosystem
Simplediffeq.jl
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21
Simple differential equation solvers in native Julia for scientific machine learning (SciML)
Matlabdiffeq.jl
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21
Common interface bindings for the MATLAB ODE solvers via MATLAB.jl for the SciML Scientific Machine Learning ecosystem
Scipydiffeq.jl
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20
Wrappers for the SciPy differential equation solvers for the SciML Scientific Machine Learning organization
Scimltutorialsoutput
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18
Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
Poissonrandom.jl
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15
Fast Poisson Random Numbers in pure Julia for scientific machine learning (SciML)
Tensorflowdiffeq.jl
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14
Using TensorFlow for physics-informed neural networks for scientific machine learning (SciML)
Commonsolve.jl
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14
A common solve function for scientific machine learning (SciML) and beyond
Scimlbenchmarksoutput
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13
SciML-Bench Benchmarks for Scientific Machine Learning (SciML), Physics-Informed Machine Learning (PIML), and Scientific AI Performance
Daskr.jl
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12
Interface to DASKR, a differential algebraic system solver for the SciML scientific machine learning ecosystem
Dimensionalplotrecipes.jl
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12
High dimensional numbers and reductions recipes for data visualization of scientific machine learning (SciML)
Odeinterfacediffeq.jl
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9
Adds the common API onto ODEInterface classic Fortran methods for the SciML Scientific Machine Learning organization
Desolvediffeq.jl
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9
Wrappers for calling the R deSolve differential equation solvers from Julia for scientific machine learning (SciML)
Geometricintegratorsdiffeq.jl
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9
Wrappers for GeometricIntegrators.jl into the SciML common interface for scientific machine learning (SciML)
Gaugefields.jl
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8
Utilities of gauge fields
Chaoticdynamicalsystemlibrary.jl
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8
A collection of chaotic ODEs.
Diffeqdevdocs.jl
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8
Developer documentation for the SciML scientific machine learning ecosystem's differential equation solvers
Symbolicindexinginterface.jl
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8
A general interface for symbolic indexing of SciML objects used in conjunction with Domain-Specific Languages
Resettablestacks.jl
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7
A stack implementation with a reset! function which avoids garbage collection for scientific machine learning (SciML)
Dynamicoed.jl
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5
Optimal experimental design of ODE and DAE systems in julia
Fmisensitivity.jl
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5
Unfortunately, FMUs (fmi-standard.org) are not differentiable by design. To enable their full potential inside Julia, FMISensitivity.jl makes FMUs fully differentiable, regarding to: states and derivatives | inputs, outputs and other observable variables | parameters | event indicators | explicit time | state change sensitivity by event
Bridgediffeq.jl
⭐
5
A thin wrapper over Bridge.jl for the SciML scientific machine learning common interface, enabling new methods for neural stochastic differential equations (neural SDEs)
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