Shi-Yuan Yang

ASSESSMENT OF FATIGUE CRACK PROPAGATION LIFE BASED ON PHYSICS-INFORMED NEURAL NETWORKS UNDER ENSEMBLE LEARNING IN HYDROGEN ENVIRONMENTS

This paper offers a physics-informed neural network (PINN) and ensemble learning framework that integrates physical and data-driven modeling using Bayesian model averaging (BMA) with a probabilistic neural network (PNN).

A dual-descent adaptive control method for reliability-based design optimization

Time-Variant Reliability Analysis via Copula Failure Correlation and Advanced Kriging

In practical engineering, mechanical structures are affected by time-dependent uncertainties and by correlated failure modes. This study proposes an enhanced time-variant reliability analysis framework by coupling a mixed Archimedean Copula model with an adaptive Kriging model driven by the maximum expected prediction...