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Thermodynamics-informed neural networks for physically realistic mixed reality

Authors :
Quercus Hernández
Alberto Badías
Francisco Chinesta
Elías Cueto
Source :
Computer Methods in Applied Mechanics and Engineering. 407:115912
Publication Year :
2023
Publisher :
Elsevier BV, 2023.

Abstract

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 laws of physics. In this paper we present a method for computing the dynamic response of (possibly non-linear and dissipative) deformable objects induced by real-time user interactions in mixed reality using deep learning. The graph-based architecture of the method ensures the thermodynamic consistency of the predictions, whereas the visualization pipeline allows a natural and realistic user experience. Two examples of virtual solids interacting with virtual or physical solids in mixed reality scenarios are provided to prove the performance of the method.<br />Comment: 11 pages, 7 figures

Details

ISSN :
00457825
Volume :
407
Database :
OpenAIRE
Journal :
Computer Methods in Applied Mechanics and Engineering
Accession number :
edsair.doi.dedup.....70e646d1598db218991223e4b0c64ce2
Full Text :
https://doi.org/10.1016/j.cma.2023.115912