Temporally-consistent koopman autoencoders for forecasting dynamical systems

Temporally-consistent koopman autoencoders for forecasting dynamical systems

Abstract 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...

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Bibliographic Details
Main Authors: Indranil Nayak, Ananda Chakrabarti, Mrinal Kumar, Fernando L. Teixeira, Debdipta Goswami
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Koopman
Machine learning
Neural networks
Online Access:https://doi.org/10.1038/s41598-025-05222-7
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https://doi.org/10.1038/s41598-025-05222-7

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