| About this Abstract |
| Meeting |
TMS Specialty Congress 2025
|
| Symposium
|
Joint Sessions of AIM, ICME, & 3DMS
|
| Presentation Title |
Digital Twins for Accelerated Materials Innovation |
| Author(s) |
Surya R. Kalidindi |
| On-Site Speaker (Planned) |
Surya R. Kalidindi |
| Abstract Scope |
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 and fusion of insights extracted from inherently incomplete, uncertain, and disparate 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. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |