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Port-metriplectic neural networks: thermodynamics-informed machine learning of complex physical systems

Article dans une revue avec comité de lecture
Author
ccHERNÁNDEZ, Quercus
ccBADIAS, Alberto
ccCHINESTA SORIA, Francisco
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]
ccCUETO, Elias

URI
http://hdl.handle.net/10985/24724
DOI
10.1007/s00466-023-02296-w
Date
2023
Journal
Comput Mech

Abstract

We develop inductive biases for the machine learning of complex physical systems based on the port-Hamiltonian formalism. To satisfy by construction the principles of thermodynamics in the learned physics (conservation of energy, non-negative entropy production), we modify accordingly the port-Hamiltonian formalism so as to achieve a port-metriplectic one. We show that the constructed networks are able to learn the physics of complex systems by parts, thus alleviating the burden associated to the experimental characterization and posterior learning process of this kind of systems. Predictions can be done, however, at the scale of the complete system. Examples are shown on the performance of the proposed technique.

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