|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
||Variational System Identification of the Partial Differential Equations Governing Microstructure Evolution in Materials: Inference over Sparse and Spatially Unrelated Data
||Krishna Garikipati, Xun Huan, Zhenlin Wang
|On-Site Speaker (Planned)
Pattern formation in materials is mechanism-specific, and encoded by partial differential equations (PDEs). With the aim of discovering hidden physics, we have developed a variational approach to identifying such systems of PDEs in the face of noisy data at varying fidelities (Comp Meth App Mech Eng, 353:201, 2019 and 377:113706, 2021). We apply our methods to image data on microstructures in materials physics. PDEs are posed as initial and boundary value problems over combinations of time intervals and spatial domains whose evolution is either fixed or can be tracked. However, micrographs of pattern evolution in materials are over domains that are unrelated at different time instants, and come from different physical specimens. The temporal resolution can rarely capture the fastest time scales, and noise abounds. We exploit the variational framework to choose weighting functions and identify PDE operators from such dynamics. The framework is demonstrated on synthetic and real data.
||Machine Learning, Computational Materials Science & Engineering, Modeling and Simulation