Generative Pre-Trained Physics-Informed Neural Networks Implementation

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

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

The fast Finite Volume simulator with UQ support.

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

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

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

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

Physics-informed neural networks (PINNs)

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

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

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

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

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 behaviour of the total energy as a function of the Reynolds Number [Re], the number of modes [N] and finally the effect of these two on the number of iterations of the model. Finally a comparison between LES [Large Eddy Simulation] and DNS [Direct Numerical Simulation] is made in order to analysis when the increase in computational cost is necessary.

Heliospheric Upwind eXtrapolation Technique (HUX)

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

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．

small examples of solving simple pde

Solving 1D Burger's equation using discontinuous Galerkin method

Efficient quantum algorithm for dissipative nonlinear differential equations

利用 Mindspore 和傅里叶神经算子（Fourier Neural Operator，FNO）求解一维伯格斯方程（1-d Burgers' equation）

Spectral Integration and Differentiation Algorithms. Includes FFTs, Chebyshev Transforms, and Hankel transforms. Exponential time differencing and integrating factor Runge-Kutta methods.

Evaluation of an analytical Volterra series solution to the Burgers equation

A bunch of solvers for some of the most common hyperbolic problems in C.

Codes to simulate & solve the Burgers equation using Fourier analysis

A Physics-Informed Neural Network (PINN) implementation in TensorFlow for solving the 2D steady-state Navier–Stokes and Burger's equations .

Fitting 2D curves or Multi-variable partial Differential Equations

Solution to Burger's Equation (inviscid), written in C, using Adams-Bashforth Methods. These methods include the one, two, and three step algorithms.