Bayesian operator learning for nonlocal damage mechanics and internal length scale identification
Mathematics and Mechanics of Solids
Published online on July 03, 2026
Abstract
Mathematics and Mechanics of Solids, Ahead of Print.
We develop a Bayesian operator-learning framework for nonlocal damage mechanics aimed at calibrating uncertain internal length scales and quantifying predictive uncertainty from limited observations. The deterministic forward model is based on an AT2-type ...
We develop a Bayesian operator-learning framework for nonlocal damage mechanics aimed at calibrating uncertain internal length scales and quantifying predictive uncertainty from limited observations. The deterministic forward model is based on an AT2-type ...