| Abstract Scope |
This presentation will describe the foundational AI/ML (artificial intelligence/machine learning) concepts and frameworks needed to formulate and continuously update the digital twin of a selected material system. The central challenge comes from the need to establish reliable models for predicting the effective functional response of the heterogeneous material system. This task demands a rigorous statistical treatment (i.e., uncertainty reduction, quantification and propagation through a network of human-interpretable models) and fusion of insights extracted from inherently incomplete (i.e., limited available information), uncertain, and disparate (due to diverse sources of data gathered at different times and fidelities, such as physical experiments, numerical simulations, and domain expertise) data used in calibrating the multiscale material model. This presentation will illustrate how a suitably designed Bayesian framework combined with emergent AI/ML toolsets can uniquely address this challenge. |