Scientific Learning project on the monodomain equation

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

Generalized and Personalized

Incompressible Navier-Stokes solver

My personal development environment for Neovim

EIT-EBM

Learning in infinite dimension with neural operators.

🔍 finite element analysis for continuum mechanics of solid bodies

Physics-Informed Neural networks for Advanced modeling

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

BOUT++: Plasma fluid finite-difference simulation code in curvilinear coordinate systems

Automatic Finite Difference PDE solving with Julia SciML

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

FEM是一个基于 Python 实现的有限元方程求解程序，旨在通过有限元方法解决非齐次两点边值问题。它用于近似求解在给定域内的微分方程解。该项目主要利用numpy和scipy库，将域离散化为较小的单元，并使用适当的基函数构建 Ritz-Galerkin 方程。最后分别通过应用高斯消去法和雅可比迭代等数值技术，求解生成的线性方程组。

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

A Julia package to perform Bifurcation Analysis

A library for scientific machine learning and physics-informed learning

TCAD Semiconductor Device Simulator

My personal development environment (Neovim)