There are 13 repositories under pde topic.
Source code for the Processing Core and Development Environment (PDE)
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.
Learning in infinite dimension with neural operators.
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
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
FEATool - "Physics Simulation Made Easy" (Fully Integrated FEA, FEniCS, OpenFOAM, SU2 Solver GUI & Multi-Physics Simulation Platform)
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
A framework for hydrodynamics explorations and prototyping
Castro (Compressible Astrophysics): An adaptive mesh, astrophysical compressible (radiation-, magneto-) hydrodynamics simulation code for massively parallel CPU and GPU architectures.
A Julia package to perform Bifurcation Analysis
Linear operators for discretizations of differential equations and scientific machine learning (SciML)
Generalized and Personalized
DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia
BOUT++: Plasma fluid finite-difference simulation code in curvilinear coordinate systems
🗿 dotfilery, configuration, environment settings, automation, etc. 🛖
Automatic Finite Difference PDE solving with Julia SciML
R Language Mode in Processing for Creative Coding, created by @gaocegege, maintained by @jeremydouglass
Universal modeling and simulation of fluid mechanics upon machine learning. From the Boltzmann equation, heading towards multiscale and multiphysics flows.
Solve non-linear HJB equations.
ETH course - Solving PDEs in parallel on GPUs
Simple one-dimensional examples of various hydrodynamics techniques
This repository is the official implementation of the paper Convolutional Neural Operators for robust and accurate learning of PDEs