|About this Abstract
||2019 TMS Annual Meeting & Exhibition
||Computational Approaches for Big Data, Artificial Intelligence and Uncertainty Quantification in Computational Materials Science
||GB Property Localization: Inference and Uncertainty Quantification of GB Structure-property Models from Indirect Polycrystal Measurements
||Christian Kurniawan, David T Fullwood, Eric R Homer, Oliver Johnson
|On-Site Speaker (Planned)
The space of all possible grain boundary (GB) characters is large and complex, making the development of constitutive models by testing individual bicrystals impractical both experimentally and computationally. In this talk, we present GB Property Localization: a high-throughput method for inferring GB structure-property models from measurements (and/or simulations) made on polycrystals. We describe the probabilistic framework on which GB Property Localization is built, and how it naturally facilitates Uncertainty Quantification (UQ) for the resulting constitutive models.
||Planned: Supplemental Proceedings volume