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Thermodynamics-informed neural networks for physically realistic mixed reality
- 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
- Subjects :
- FOS: Computer and information sciences
Computer Science - Machine Learning
Computer Science - Artificial Intelligence
Mechanical Engineering
Computational Mechanics
General Physics and Astronomy
Dynamical Systems (math.DS)
Graphics (cs.GR)
Machine Learning (cs.LG)
Computer Science Applications
Computer Science - Graphics
Artificial Intelligence (cs.AI)
Mechanics of Materials
FOS: Mathematics
Mathematics - Dynamical Systems
Subjects
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