Parametric Symbolic Regression for Discovering Unified Crack Growth Models from Diverse Experiments

What is it about?

The rapid expansion of experimental conditions and methodologies in the field of fatigue research has resulted in increasingly diverse and complex datasets. We demonstrate a data-driven framework that can directly extract a unified crack growth model from these extensive experimental datasets, yielding a model that is simple, well-generalized, and stable.

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