Parametric Learning of Time-Advancement Operators for Unstable Flame Evolution

arXiv:2402.10238v1 Announce Type: new
Abstract: This study investigates the application of machine learning, specifically Fourier Neural Operator (FNO) and Convolutional Neural Network (CNN), to learn time-advancement operators for parametric partial differential equations (PDEs). Our focus is on extending existing operator learning methods to handle additional inputs representing PDE parameters. The goal is to create a unified learning approach that accurately predicts short-term solutions and provides robust long-term statistics under diverse parameter conditions, facilitating computational cost savings and accelerating development in …

abstract advancement application arxiv cnn convolutional neural network cs.lg differential evolution focus fourier inputs learn machine machine learning network neural network operators parameters parametric study type