Bayesian-Optimized Physics-Informed Deep Autoencoders for Efficient Calibration of Geotechnical Constitutive Models: A NorSand Case Study

Abstract

AbstractRecent advancements in deep learning have revolutionized constitutive model calibration in geotechnical engineering by automatically identifying complex patterns in high-dimensional data, enhancing accuracy, and reducing reliance on subjective ...