MAE 298 SEMINAR: An Interleaved Physics-Based Deep-Learning Framework as a New Cycle Jumping Approach for Microstructurally Small Fatigue Crack Growth Simulations

Abstract: The early stages of fatigue-crack evolution can consume the majority of a structure's life in
high-cycle fatigue applications. Thus, predicting accurately the behavior of microstructurally small
fatigue cracks (MSCs) is essential for developing next-generation fatigue-resistant materials and for
realizing concepts like the airframe digital twin. While current simulation frameworks using crystal
plasticity constitutive models can resolve material deformation and micromechanical fields at the MSC
scale, running such simulations over realistic cycle counts remains computationally intractable. This work
introduces a novel cycle-jumping strategy that leverages deep learning and uncertainty quantification
(UQ) to accelerate 3D MSC propagation simulations. Bidirectional Long Short-Term Memory (BiLSTM)
networks are trained to predict local crack deflection and growth rate using 18,000 data sequences
extracted from 40 high-fidelity, physics-based simulations of MSC propagation. Recognizing that making
many successive forward predictions using the deep-learning framework can lead to unacceptable
uncertainty propagation, we propose an interleaved physics-based deep-learning (PBDL) framework that
combines the rapid predictive capabilities of deep learning with the accuracy of physics-based models. In
the proposed framework, UQ plays a key role in determining when to update the explicit crack surface in
the physics-based model with the deep-learning evolved crack surface, prior to resuming deep-learning
predictions using the updated physics-based model for input. The UQ-informed PBDL framework enables
the simulation of MSC growth over a realistic number of cycle counts while reining in model error and
uncertainty propagation. The work represents a significant advancement in fatigue modeling and offers a
template for other applications.

Bio: Ashley Spear is a Presidential Scholar at the University of Utah and an associate professor in
mechanical engineering. She directs the Multiscale Mechanics & Materials Laboratory, which specializes
in integrating physics-based modeling, data science and experiments to examine deformation, fatigue
and fracture in a wide range of materials. Spear received her B.S. in architectural engineering from the
University of Wyoming and Ph.D. in civil engineering from Cornell University. She is the recipient of
the Constance Tipper Medal from the International Congress on Fracture, the Young Investigator Award
from the Air Force Office of Scientific Research, the TMS Early Career Faculty Fellow Award and the
National Science Foundation CAREER award.