Non-intrusive surrogate modeling for parametrized time-dependent partial differential equations using convolutional autoencoders

This paper presents a novel non-intrusive surrogate modeling scheme based on deep learning for predictive modeling of complex systems, described by parametrized time-dependent partial differential equations. Specifically, the proposed method utilizes a convolutional autoencoder in conjunction with a...

Full description

Bibliographic Details

Published in:	Engineering applications of artificial intelligence Vol. 109; p. 104652
Main Authors:	Nikolopoulos, Stefanos, Kalogeris, Ioannis, Papadopoulos, Vissarion
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.03.2022
Subjects:	Convolutional autoencoders Deep learning Feed forward neural networks Monte Carlo simulation Surrogate modeling Deep learning Convolutional autoencoders Surrogate modeling Feed forward neural networks Monte Carlo simulation
ISSN:	0952-1976, 1873-6769
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!