Yong-Zhen Zhang

A Dual-Physics-Informed Neural Network with Incremental Learning for Corrosion Fatigue Crack Growth Prediction in Aluminum Alloys

Aluminum alloys used in aircraft structures are susceptible to corrosion fatigue cracking under combined aggressive environments and cyclic loading, threatening structural integrity. Pure data-driven models often fail under distribution shifts, while single-physics-informed neural networks (PINNs) lack flexibility in c...