eprintid: 17489
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/74/89
datestamp: 2023-12-19 03:23:52
lastmod: 2023-12-19 03:23:52
status_changed: 2023-12-19 03:08:08
type: article
metadata_visibility: show
creators_name: Yunus, R.B.
creators_name: Abdul Karim, S.A.
creators_name: Shafie, A.
creators_name: Izzatullah, M.
creators_name: Kherd, A.
creators_name: Hasan, M.K.
creators_name: Sulaiman, J.
title: An Overview on Deep Learning Techniques in Solving Partial Differential Equations
ispublished: pub
note: cited By 1
abstract: Despite great advances in solving partial differential equations (PDEs) using the numerical discretization, some high- dimensional problems with large number of parameters cannot be handled easily. Owing to the rapid growth of accessible data and computing expedients, recent developments in deep learning techniques for the solution of (PDEs) have yielded outstanding results on distinctive problems. In this chapter, we give an overview on diverse deep learning techniques namely; Physics-Informed Neural Networks (PINNs), Int-Deep, BiPDE-Net etc., which are all devised based on Deep Neural Networks (DNNs). We also discuss on several optimization methods to enrich the accuracy of the training and minimize training time. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
date: 2022
publisher: Springer Science and Business Media Deutschland GmbH
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140227070&doi=10.1007%2f978-3-031-04028-3_4&partnerID=40&md5=7c8311181d1d36aaa1de9bcc7ecb1843
id_number: 10.1007/978-3-031-04028-3₄
full_text_status: none
publication: Studies in Systems, Decision and Control
volume: 444
pagerange: 37-47
refereed: TRUE
issn: 21984182
citation:   Yunus, R.B. and Abdul Karim, S.A. and Shafie, A. and Izzatullah, M. and Kherd, A. and Hasan, M.K. and Sulaiman, J.  (2022) An Overview on Deep Learning Techniques in Solving Partial Differential Equations.  Studies in Systems, Decision and Control, 444.  pp. 37-47.  ISSN 21984182