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: Long left ignored by the digital computing industry since its heyday in 1940’s, analog computing is today making a comeback as Moore’s Law slows down. Analog CMOS has power efficiency ...
Enhancing PINNs for Solving PDEs via Adaptive Collocation Point Movement and Adaptive Loss Weighting
Physics-Informed Neural Networks (PINNs) are an emerging method for solving partial differential equations (PDEs) and have been widely applied in the field of scientific computing. In this paper, we ...
This project applies Physics-informed Neural Networks (PINNs) to simulate heat propagation, comparing it with the traditional finite difference method. It demonstrates PINNs' capability in solving the ...
Abstract: Over the past few decades, risk-averse optimization has emerged as a crucial framework for decision-making under uncertainty in systems governed by partial differential equations (PDEs).
Some results have been hidden because they may be inaccessible to you
Show inaccessible results