Generative Pre-Trained Physics-Informed Neural Networks Implementation

A Physics-Informed Neural Network for solving Burgers' equation.

Solve the 1D forced Burgers equation with high order finite elements and finite difference schemes.

The fast Finite Volume simulator with UQ support.

Python script solving the Burgers' equation (équation de Burgers) 1D by using FFT pseudo-spectral method.

Simple (and not-so-simple) CFD solvers written in Fortran with Python plotting routines

PySpectral is a Python package for solving the partial differential equation (PDE) of Burgers' equation in its deterministic and stochastic version.

Two solutions, written in MATLAB, for solving the viscous Burger's equation. They are both spectral methods: the first is a Fourier Galerkin method, and the second is Collocation on the Tchebyshev-Gauß-Lobatto points.

MATLAB functions and scripts for 1D Computational Fluid Dynamics

Runge-Kutta adaptive-step solvers for nonlinear PDEs. Solvers include both exponential time differencing and integrating factor methods.

Physics-informed neural networks (PINNs)

Codebase for Master's dissertation in Mathematics at Durham University. Topic: applying neural networks to differential equations. Grade: 85/100.

This report is developed with the purpose of giving the student a better understanding of what is turbulence modelling and its analysis. A brief introduction about the Burger's equation and the theories behind the models used later on the report. The study consist in the analysis of the Burger's equation in the Fourier space analysing the behavi…

Parallelization attempt to the “12 steps to Navier–Stokes” lessons using OpenMP/C

A simple Fortran code of DG+KXRCF Detector+WENO Limiter solving 2D Burgers Equation

Heliospheric Upwind eXtrapolation Technique (HUX)

Finite-difference solution to 1D viscid Burger's equation

Utilized PINNs to fit 1D curves and 2D Burgers' Equation

small examples of solving simple pde

偏微分方程式の数値計算サンプル．バグがまだ埋まってると思う．