2021/10/30 by Mansura Habiba, Barak A. Pearlmutter, Habiba, Mansura +1
Physics and Astronomy · Engineering · Computer Science · #Model Reduction and Neural Networks #Advanced Data Processing Techniques #Computational Physics and Python Applications
paper · pdf · doi:10.48550/arxiv.2111.00343
Recent work in deep learning focuses on solving physical systems in the Ordinary Differential Equation or Partial Differential Equation. This current work proposed a variant of Convolutional Neural Networks (CNNs) that can learn the hidden dynamics of a physical system using ordinary differential equation (ODEs) systems (ODEs) and Partial Differential Equation systems (PDEs). Instead of considering the physical system such as image, time -series as a system of multiple layers, this new technique can model a system in the form of Differential Equation (DEs). The proposed method has been assessed by solving several steady-state PDEs on irregular domains, including heat equations, Navier-Stokes equations.