TensorMesh is a finite element method (FEM) library built natively on PyTorch. It is designed to solve partial differential equations (PDEs) with the ergonomics of modern deep learning frameworks — ...

To fulfill the 2 Core Courses, take two Core Courses from two different Core Areas. CSE Core Courses are classified into six areas: Introduction to CSE, Computational Mathematics, High Performance ...

We introduce an extension of the hopping method, typically used in quantum systems, to mechanical networks for constructing dynamical matrices. This innovative and efficient approach facilitates the ...

This set of tutorials are written at an introductory level for an engineering or physical sciences major. It is ideal for someone who has completed college level courses in linear algebra, calculus ...

However, the rise of Python in scientific computing has opened the doors to powerful, open-source tools for power system analysis. This guide provides a step-by-step walk-through on modeling, ...

Solving Ordinary Differential Equations (ODEs) lies at the core of modeling dynamic systems in engineering. From predicting chemical reactions to simulating mechanical oscillations, numerical ...

Identifying governing equations from observational data is crucial for understanding nonlinear physical systems but remains challenging due to the risk of overfitting. Here we introduce the Bi-Level ...

Physics-Informed Neural Networks (PINN) are neural networks encoding the problem governing equations, such as Partial Differential Equations (PDE), as a part of the neural network. PINNs have emerged ...

The calculation of derivatives is ubiquitous in science and engineering. In thermodynamics, in particular, state properties can be expressed as derivatives of thermodynamic potentials. The manual ...

Partial differential equations (PDEs) are ubiquitous in natural science and engineering problems. Traditional discrete methods for solving PDEs are usually time-consuming and labor-intensive due to ...