This project was performed to practice a common approach to using the Spectral Method with the fourier Galerkin approach to solving PDE's. Specifically, we'll be ...

This repository encompasses Python code and Jupyter notebooks exemplifying the application of Physics-Informed Neural Networks (PINNs) and Lagaris' approach to address Partial Differential Equations ...

Partial differential equations (PDEs) are required for modeling dynamic systems in science and engineering, but solving them accurately, especially for initial value problems, remains challenging.

Machine Learning ML offers significant potential for accelerating the solution of partial differential equations (PDEs), a critical area in computational physics. The aim is to generate accurate PDE ...

Abstract: Modelica is a new object-oriented multi-domain modeling and simulation language and used for solving large, complex, and heterogeneous physical systems with differential-algebraic equations ...

Abstract: In this article, we present a continuation method, which transforms spatially distributed ordinary differential equation (ODE) systems into a continuous partial differential equation (PDE).

Partial differential equations can describe everything from planetary motion to plate tectonics, but they’re notoriously hard to solve. Unless you’re a physicist or an engineer, there really isn’t ...