A Physics-Informed Neural Network for solving Burgers' equation.
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Updated
Apr 14, 2024 - Jupyter Notebook
A Physics-Informed Neural Network for solving Burgers' equation.
small examples of solving simple pde
Course material I created for the tutorial "Mathematical Modelling in Climate Research" at the Freie Universität Berlin
Simple data assimilation studies
Assignment codes from my Non -linear PDE course in ICTS TIFR
Generative Pre-Trained Physics-Informed Neural Networks Implementation
Efficient quantum algorithm for dissipative nonlinear differential equations
Some of my own pseudo-spectral method codes
Utilized PINNs to fit 1D curves and 2D Burgers' Equation
Codebase for Master's dissertation in Mathematics at Durham University. Topic: applying neural networks to differential equations. Grade: 85/100.
Spectral Integration and Differentiation Algorithms. Includes FFTs, Chebyshev Transforms, and Hankel transforms. Exponential time differencing and integrating factor Runge-Kutta methods.
Runge-Kutta adaptive-step solvers for nonlinear PDEs. Solvers include both exponential time differencing and integrating factor methods.
Fitting 2D curves or Multi-variable partial Differential Equations
PySpectral is a Python package for solving the partial differential equation (PDE) of Burgers' equation in its deterministic and stochastic version.
Solving 1D Burger's equation using discontinuous Galerkin method
Solve the 1D forced Burgers equation with high order finite elements and finite difference schemes.
Codes to simulate & solve the Burgers equation using Fourier analysis
A simple Fortran code of DG+KXRCF Detector+WENO Limiter solving 2D Burgers Equation
Physics-informed neural networks (PINNs)
Simulation of Viscosity-Stratified Flow in MATLAB.
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