Debiao Meng

Assessment of Fatigue Crack Growth Rate Based on Physics-Informed Neural Networks in Hydrogen Environments

The safety of hydrogen storage and transportation systems is a fundamental component of clean energy, so the fatigue performance of metallic materials is very important. Nevertheless, the hydrogen embrittling effect caused by hydrogen environments mostly increases the rate at which fatigue cracks grow in the materials,...

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...