  Structural components are typically exposed to dynamic loading, such as
earthquakes, wind, and explosions. Structural engineers should be able to
conduct real-time analysis in the aftermath or during extreme disaster events
requiring immediate corrections to avoid fatal failures. 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 and are
computationally prohibitive. Therefore, to reduce computational cost while
preserving accuracy, a deep learning model, Neuro-DynaStress, is proposed to
predict the entire sequence of stress distribution based on finite element
simulations using a partial differential equation (PDE) solver. The model was
designed and trained to use the geometry, boundary conditions and sequence of
loads as input and predict the sequences of high-resolution stress contours.
The performance of the proposed framework is compared to finite element
simulations using a PDE solver.
