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

Physics Informed Neural Network for Dynamic Stress Prediction