Temporally-consistent koopman autoencoders for forecasting dynamical systems

Absence of sufficiently high-quality data often poses a key challenge in data-driven modeling of high-dimensional spatio-temporal dynamical systems. Koopman Autoencoders (KAEs) harness the expressivity of deep neural networks (DNNs), the dimension reduction capabilities of autoencoders, and the spec...

Full description

Saved in:

Bibliographic Details
Published in:	Scientific reports Vol. 15; no. 1; pp. 22127 - 13
Main Authors:	Nayak, Indranil, Chakrabarti, Ananda, Kumar, Mrinal, Teixeira, Fernando L., Goswami, Debdipta
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 01.07.2025 Nature Publishing Group Nature Portfolio
Subjects:	639/166 639/166/987 639/166/988 Electrical and electronic engineering Engineering Humanities and Social Sciences Koopman Machine learning Mechanical engineering multidisciplinary Neural networks Science Science (multidisciplinary) Neural networks Machine learning Koopman
ISSN:	2045-2322, 2045-2322
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!