• français
    • English
    français
  • Login
Help
View Item 
  •   Home
  • Laboratoire Procédés et Ingénierie en Mécanique et Matériaux (PIMM)
  • View Item
  • Home
  • Laboratoire Procédés et Ingénierie en Mécanique et Matériaux (PIMM)
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Structure-preserving neural networks

Article dans une revue avec comité de lecture
Author
HERNÁNDEZ, Quercus
161327 Aragón Institute of Engineering Research [Zaragoza] [I3A]
BADÍAS, Alberto
161327 Aragón Institute of Engineering Research [Zaragoza] [I3A]
GONZÁLEZ, David
161327 Aragón Institute of Engineering Research [Zaragoza] [I3A]
ccCHINESTA SORIA, Francisco
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]
564849 ESI Group [ESI Group]
ccCUETO, Elias
161327 Aragón Institute of Engineering Research [Zaragoza] [I3A]

URI
http://hdl.handle.net/10985/19924
DOI
10.1016/j.jcp.2020.109950
Date
2021
Journal
Journal of Computational Physics

Abstract

We develop a method to learn physical systems from data that employs feedforward neural networks and whose predictions comply with the first and second principles of thermodynamics. The method employs a minimum amount of data by enforcing the metriplectic structure of dissipative Hamiltonian systems in the form of the so-called General Equation for the Non-Equilibrium Reversible-Irreversible Coupling, GENERIC (Öttinger and Grmela (1997) [36]). The method does not need to enforce any kind of balance equation, and thus no previous knowledge on the nature of the system is needed. Conservation of energy and dissipation of entropy in the prediction of previously unseen situations arise as a natural by-product of the structure of the method. Examples of the performance of the method are shown that comprise conservative as well as dissipative systems, discrete as well as continuous ones.

Files in this item

Name:
PIMM_JCP_2021_CHINESTA.pdf
Size:
787.0Kb
Format:
PDF
Description:
Article
Embargoed until:
2021-08-01
View/Open

Collections

  • Autres équipes
  • Laboratoire Procédés et Ingénierie en Mécanique et Matériaux (PIMM)

Related items

Showing items related by title, author, creator and subject.

  • Deep learning of thermodynamics-aware reduced-order models from data 
    Article dans une revue avec comité de lecture
    HERNANDEZ, Quercus; BADIAS, Alberto; GONZALEZ, David; ccCHINESTA SORIA, Francisco; ccCUETO, Elias (Elsevier, 2021)
    We present an algorithm to learn the relevant latent variables of a large-scale discretized physical system and predict its time evolution using thermodynamically-consistent deep neural networks. Our method relies on sparse ...
  • Structure-preserving neural networks 
    Article dans une revue avec comité de lecture
    HERNÁNDEZ, Quercus; BADÍAS, Alberto; GONZÁLEZ, David; ccCUETO, Elias; ccCHINESTA SORIA, Francisco (Elsevier, 2020)
    We develop a method to learn physical systems from data that employs feedforward neural networks and whose predictions comply with the first and second principles of thermodynamics. The method employs a minimum amount of ...
  • Port-metriplectic neural networks: thermodynamics-informed machine learning of complex physical systems 
    Article dans une revue avec comité de lecture
    ccHERNÁNDEZ, Quercus; ccBADIAS, Alberto; ccCHINESTA SORIA, Francisco; ccCUETO, Elias (2023)
    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 ...
  • Thermodynamics-informed neural networks for physically realistic mixed reality 
    Article dans une revue avec comité de lecture
    ccHERNÁNDEZ, Quercus; ccBADIAS, Alberto; ccCHINESTA SORIA, Francisco; ccCUETO, Elias (2023)
    The imminent impact of immersive technologies in society urges for active research in real-time and interactive physics simulation for virtual worlds to be realistic. In this context, realistic means to be compliant to the ...
  • MORPH-DSLAM: Model Order Reduction for Physics-Based Deformable SLAM 
    Article dans une revue avec comité de lecture
    ccBADIAS, Alberto; ALFARO, Icíar; GONZALEZ, David; ccCHINESTA SORIA, Francisco; ccCUETO, Elias (Institute of Electrical and Electronics Engineers (IEEE), 2022-11)
    We propose a new methodology to estimate the 3D displacement field of deformable objects from video sequences using standard monocular cameras. We solve in real time the complete (possibly visco-)hyperelasticity problem ...

Browse

All SAMCommunities & CollectionsAuthorsIssue DateCenter / InstitutionThis CollectionAuthorsIssue DateCenter / Institution

Newsletter

Latest newsletterPrevious newsletters

Statistics

Most Popular ItemsStatistics by CountryMost Popular Authors

ÉCOLE NATIONALE SUPERIEURE D'ARTS ET METIERS

  • Contact
  • Mentions légales

ÉCOLE NATIONALE SUPERIEURE D'ARTS ET METIERS

  • Contact
  • Mentions légales