On the feasibility of foundational models for the simulation of physical phenomena
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Abstract (arXiv)
We explore the feasibility of foundation models for the simulation of physical phenomena, with emphasis on continuum (solid and fluid) mechanics. Although so-called learned simulators have shown some success when applied to specific tasks, it remains to be studied to what extent they are able to undergo severe changes in domain shape, boundary conditions and/or constitutive laws and still provide robust (i.e., hallucination-free) and accurate results. In this paper we perform an exhaustive study of these features, put ourselves in the worst-case scenario and study their resistance to such strong changes in their domain of application.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Oct 2024.
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Citing papers per year: 2024: 1; 2025: 3; 2026: 3.
7 papers.
- 2026MeshGraphNet-Transformer: Scalable Mesh-based Learned Simulation for Solid Mechanicscited by 10
- 2025On the under-reaching phenomenon in message-passing neural PDE solvers: revisiting the CFL conditioncited by 4
- 2025Bridging Data and Physics: A Graph Neural Network-Based Hybrid Twin Frameworkcited by 2
- 2026Neural-Schwarz Tiling for Geometry-Universal PDE Solving at Scalecited by 2
- 2024Thermodynamics-informed graph neural networks for real-time simulation of digital human twinscited by 1
- 2025Application of Reduced-Order Models for Temporal Multiscale Representations in the Prediction of Dynamical Systemscited by 1
- 2026Variational Graph Neural Networks for Uncertainty Quantification in Inverse Problemscited by 0
- 2026Neural-Schwarz Tiling for Geometry-Universal PDE Solving at Scale12 May 2026
- 2026Variational Graph Neural Networks for Uncertainty Quantification in Inverse Problems31 Mar 2026
- 2026MeshGraphNet-Transformer: Scalable Mesh-based Learned Simulation for Solid Mechanics30 Jan 2026
- 2025Bridging Data and Physics: A Graph Neural Network-Based Hybrid Twin Framework12 Dec 2025
- 2025Application of Reduced-Order Models for Temporal Multiscale Representations in the Prediction of Dynamical Systems21 Oct 2025
- 2025On the under-reaching phenomenon in message-passing neural PDE solvers: revisiting the CFL condition9 Jul 2025
- 2024Thermodynamics-informed graph neural networks for real-time simulation of digital human twins16 Dec 2024