Thermodynamics-informed neural networks for physically realistic mixed reality

Autor:	Hernández, Quercus, Badías, Alberto, Chinesta, Francisco, Cueto, Elías
Rok vydání:	2022
Předmět:	Computer Science - Graphics Computer Science - Artificial Intelligence Computer Science - Machine Learning Mathematics - Dynamical Systems
Druh dokumentu:	Working Paper
DOI:	10.1016/j.cma.2023.115912
Popis:	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. Comment: 11 pages, 7 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2210.13414 Zobrazit plný text záznamu View this record from Arxiv