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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 ⭐ 864

Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation

Scimltutorials.jl ⭐ 698

Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.

Diffeqbase.jl ⭐ 282

The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems

Scimlbenchmarks.jl ⭐ 279

Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R

Diffeqoperators.jl ⭐ 279

Linear operators for discretizations of differential equations and scientific machine learning (SciML)

Neuraloperators.jl ⭐ 206

DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia

Physics-Informed Neural networks for Advanced modeling

Kinetic.jl ⭐ 110

Universal modeling and simulation of fluid mechanics upon machine learning. From the Boltzmann equation, heading towards multiscale and multiphysics flows.

Fenics.jl ⭐ 86

A scientific machine learning (SciML) wrapper for the FEniCS Finite Element library in the Julia programming language

Fractionaldiffeq.jl ⭐ 70

Solve Fractional Differential Equations using high performance numerical methods

Highdimpde.jl ⭐ 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

Gpt Pinn ⭐ 22

Generative Pre-Trained Physics-Informed Neural Networks Implementation

Curve Shortening Demo ⭐ 20

Visualize curve shortening flow in your browser.

MFM community development code

Leibniz is a python package which provide facilities to express learnable partial differential equations with PyTorch

Adapode.jl ⭐ 10

Adaptive P/ODE numerical solvers with Grassmann element TensorField assembly

Related Searches

Julia Differential Equations (97)

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