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Physics informed neural network for dynamic stress prediction

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Abstract

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.

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Data Availability

The dataset and codes generated and/or analyzed during the current study are available at GitHub https://github.com/bolandih/2023_PINN_Stress

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Funding

This research was funded in part by the National Science Foundation grant CNS 1645783

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Authors and Affiliations

  1. Civil and Environmental Engineering, Michigan State University, Shaw Lane, East Lansing, 48824, MI, USA

    Hamed Bolandi, Xuyang Li & Nizar Lajnef

  2. Computer Science and Engineering, Michigan State University, Shaw Lane, East Lansing, 48824, MI, USA

    Hamed Bolandi, Gautam Sreekumar, Xuyang Li & Vishnu Naresh Boddeti

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  1. Hamed Bolandi
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  2. Gautam Sreekumar
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Correspondence to Hamed Bolandi.

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Bolandi, H., Sreekumar, G., Li, X. et al. Physics informed neural network for dynamic stress prediction. Appl Intell 53, 26313–26328 (2023). https://doi.org/10.1007/s10489-023-04923-8

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