mlsanityData to 7 Oct 2026

Thermodynamics-informed neural networks for physically realistic mixed reality

arXiv 2210.13414 ↗First posted 24 Oct 2022cs.GR cs.AI cs.LGcited by 9

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Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 22 of its 51 references that are in the map. First posted on arXiv 24 Oct 2022.

Builds on

22 of 51 references.

References · 22

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10 more
  1. 2019Hamiltonian Graph Networks with ODE Integratorscited by 107
  2. 2021Physics-informed neural networks for solving Reynolds-averaged Navierunicodex2013Stokes equationscited by 68
  3. 2022CROM: Continuous Reduced-Order Modeling of PDEs Using Implicit Neural Representationscited by 56
  4. 2020Structure-preserving neural networkscited by 54
  5. 2022Thermodynamics-informed graph neural networkscited by 31
  6. 2018LookinGood: Enhancing Performance Capture with Real-time Neural Re-Renderingcited by 22
  7. 2022Physics-informed deep-learning applications to experimental fluid mechanicscited by 21
  8. 2020UNOC: Understanding Occlusion for Embodied Presence in Virtual Realitycited by 7
  9. 2019Augmented Reality Applied to LEGO Construction: AR-based Building Instructions with High Accuracy & Precision and Realistic Object-Hand Occlusionscited by 6
  10. 2021DeepPhysics: a physics aware deep learning framework for real-time simulationcited by 6

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 5; 2025: 3.

9 papers.

  1. 2023Deep Learning in Deterministic Computational Mechanicscited by 21
  2. 2024P2C2Net: PDE-Preserved Coarse Correction Network for efficient prediction of spatiotemporal dynamicscited by 13
  3. 2024Physics-informed Deep Learning for Muscle Force Prediction with Unlabeled sEMG Signalscited by 4
  4. 2024Conservation-informed Graph Learning for Spatiotemporal Dynamics Predictioncited by 4
  5. 2025On the under-reaching phenomenon in message-passing neural PDE solvers: revisiting the CFL conditioncited by 4
  6. 2024A hybrid numerical methodology coupling Reduced Order Modeling and Graph Neural Networks for non-parametric geometries: applications to structural dynamics problemscited by 3
  7. 2025MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulationcited by 3
  8. 2024Thermodynamics-informed graph neural networks for real-time simulation of digital human twinscited by 1
  9. 2025Learnable-Differentiable Finite Volume Solver for Accelerated Simulation of Flowscited by 1