mlsanityData to 13 Oct 2026

Physics Informed Neural Network for Dynamic Stress Prediction

arXiv 2211.16190 ↗First posted 28 Nov 2022cs.LGcited by 6

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Abstract (arXiv)

Structural failures are often caused by catastrophic events such as earthquakes and winds. As a result, it is crucial to predict dynamic stress distributions during highly disruptive events in real time. Currently available high-fidelity methods, such as Finite Element Models (FEMs), suffer from their inherent high complexity. Therefore, to reduce computational cost while maintaining accuracy, a Physics Informed Neural Network (PINN), PINN-Stress model, is proposed to predict the entire sequence of stress distribution based on Finite Element simulations using a partial differential equation (PDE) solver. Using automatic differentiation, we embed a PDE into a deep neural network's loss function to incorporate information from measurements and PDEs. The PINN-Stress model can predict the sequence of stress distribution in almost real-time and can generalize better than the model without PINN.

Key facts. Cited by 6 papers in the map (data through 13 Oct 2026). Builds on 20 of its 63 references that are in the map. First posted on arXiv 28 Nov 2022.

Builds on

20 of 63 references.

References · 20

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8 more
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Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 1; 2026: 2.

6 papers.

  1. 2023Transfer learning for improved generalizability in causal physics-informed neural networks for beam simulationscited by 8
  2. 2023Physics-informed PointNet: On how many irregular geometries can it solve an inverse problem simultaneously? Application to linear elasticitycited by 7
  3. 2024Mechanics-Informed Autoencoder Enables Automated Detection and Localization of Unforeseen Structural Damagecited by 6
  4. 2025Physics-informed KAN PointNet: Deep learning for simultaneous solutions to inverse problems in incompressible flow on numerous irregular geometriescited by 3
  5. 2026Generative modeling of granular flow on inclined planes using conditional flow matchingcited by 0
  6. 2026Appearance-Preserving Refinement of Generated 3D Assets for Monochromatic Fabricationcited by 0