Fatigue (Materials)

Data-Driven Approach to Modeling Creep-Fatigue Behavior Using Neural ODEs

Research conducted by Argonne National Laboratory has presented a data-driven machine learning approach to modeling one-dimensional stress-strain behavior under cyclic loading, utilizing experimental data from the nickel-based Alloy 617. The approach employs uniaxial creep-fatigue test data acquired under various loading histories and compares two distinct neural network-based ODE models. Financial

Fatigue (Materials)

Evidence-Enhanced Data-Model-Driven Approach for Predicting Fatigue Crack Propagation

Researchers from Zhejiang University have developed a novel approach for simulating fatigue crack propagation in steel structures, utilizing advanced surrogate modeling techniques and data-driven physical model updating. The study aims to provide reliable decision-making basis for the dynamic life management and maintenance of steel structures. The researchers proposed an evidence-enhanced