doi:10.1016/j.cma.2023.115912engHernández, QuercusBadías, AlbertoChinesta, FranciscoCueto, ElíasThermodynamics-informed neural networks for physically realistic mixed realityART-2023-132991The 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.2023http://zaguan.unizar.es/record/12525810.1016/j.cma.2023.115912http://zaguan.unizar.es/record/125258oai:zaguan.unizar.es:125258info:eu-repo/grantAgreement/ES/MICINN-AEI/PID2020-113463RB-C31/AEI/10.13039/501100011033Computer Methods in Applied Mechanics and Engineering 407 (2023), 115912 [11 pp.]by-nc-ndhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccess