Abstract: Solving partial differential equations (PDEs) is omnipresent in scientific research and engineering and requires expensive numerical iteration for memory and computation. The primary ...

Abstract: Physics-informed neural networks (PINNs) offer a flexible framework for solving differential equations using physical constraints and data. This study focuses on second-order ...

This course aims to develop a computational view of stochastic differential equations (SDEs) for students who have an applied or engineering background, e.g., machine learning, signal processing, ...