Fig. 1: Workflow of interleaved physics-based deep-learning (PBDL) framework for predicting history-dependent propagation of microstructurally small fatigue cracks. | npj Computational Materials

Fig. 1: Workflow of interleaved physics-based deep-learning (PBDL) framework for predicting history-dependent propagation of microstructurally small fatigue cracks.

From: An interleaved physics-based deep-learning framework as a new cycle jumping approach for microstructurally small fatigue crack growth simulations

Fig. 1

The framework combines a physics-based crystal plasticity model, which evaluates micromechanical fields in a cracked microstructure, with a deep-learning surrogate that predicts a sequence of crack-growth increments using fields from the current state of the physics-based model. Uncertainty metrics are used to trigger intermittent updates of the explicit crack representation in the physics-based model to maintain relevant micromechanical fields as input for the deep-learning predictions.

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