Session: 12-08-01: Data-Enabled Predictive Modeling, Scientific Machine Learning, and Uncertainty Quantification in Computational Mechanics

Paper Number: 147018

147018 - Digital Twins for Accelerated Materials Innovation 

This presentation will expound the challenges involved in the generation of digital twins (DT) as valuable tools for supporting innovation and providing informed decision support for the optimization of material properties and/or performance of advanced heterogeneous material systems. This presentation will describe the foundational AI/ML (artificial intelligence/machine learning) concepts and frameworks needed to formulate and continuously update the DT of a selected material system. The central challenge comes from the need to establish reliable models for predicting the effective (macroscale) functional response of the heterogeneous material system, which is expected to exhibit highly complex, stochastic, nonlinear behavior. 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 with examples how a suitably designed Bayesian framework combined with emergent AI/ML toolsets can uniquely address this challenge. Specifically, we will demonstrate the important roles of (i) emergent AI/ML toolsets for Bayesian inference (e.g., multi-output Gaussian process regression, generative models), (ii) high-throughput strategies for designing and employing non-standard experiments, and (iii) a SaaS platform for enabling highly efficient collaboration and knowledge sharing between distributed teams/participants in realizing the goals described above.

Presenting Author: Surya Kalidindi Georgia Tech

Presenting Author Biography: Surya R. Kalidindi is a Regents Professor and Rae S. and Frank H. Neely Chair Professor in the George W. Woodruff School of Mechanical Engineering with joint appointments in the School of Computational Science and Engineering and the School of Materials Science and Engineering at Georgia Institute of Technology, Georgia, USA. Surya’s research efforts have made seminal contributions to the fields of crystal plasticity, microstructure design, high-throughput mechanical characterization and materials informatics. Surya has been elected a Fellow of ASM International, TMS, and ASME. He has also been recognized with the Alexander von Humboldt Research Award, the Vannever Bush Faculty Fellow, and the Khan International Award. His research currently has a h-index of 93 (as per Google Scholar).

Authors:

Surya Kalidindi Georgia Tech