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Physics-Informed Recurrent Architecture with Embedded Thermodynamic Dynamics for Robust Sequence Modeling

Authors:

Zafer Yigit, Håkan Forsberg, Masoud Daneshtalab

Research group:

Heterogeneous systems - hardware software co-design

Publication Type:

Conference/Workshop Paper

Venue:

34th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Abstract

Physics-informed machine learning has shown strong potential in improving generalisation under limited or noisy data, but most existing approaches treat physical priors only as soft regularisation terms on the loss. This work introduces a physics-structured recurrent architecture where thermodynamic differential equations are embedded directly into LSTM state updates. Adaptive physical parameters are learned through auxiliary multilayer perceptrons, forming a differentiable hybrid dynamical system that fuses physics priors with sequence learning. Experiments on industrial datasets show improved robustness under unseen fault conditions, outperforming conventional LSTMs and PINN-style models. The framework offers a scalable and generalizable approach to physics-aware recurrent modeling.

Bibtex

@inproceedings{Yigit7336,
author = {Zafer Yigit and H{\aa}kan Forsberg and Masoud Daneshtalab},
title = {Physics-Informed Recurrent Architecture with Embedded Thermodynamic Dynamics for Robust Sequence Modeling},
month = {April},
year = {2026},
booktitle = {34th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning},
url = {http://www.es.mdu.se/publications/7336-}
}