Thermodynamics-informed neural networks for physically realistic mixed reality
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.
Cite
@article{arxiv.2210.13414,
title = {Thermodynamics-informed neural networks for physically realistic mixed reality},
author = {Quercus Hernández and Alberto Badías and Francisco Chinesta and Elías Cueto},
journal= {arXiv preprint arXiv:2210.13414},
year = {2023}
}
Comments
11 pages, 7 figures