Publications

Gaussian-Process-Driven Adaptive Sampling for Reduced-Order Modeling of Texture Effects in Polycrystalline Alpha-Ti

Tallman, Aaron E.; Stopka, Krzysztof S.; Swiler, Laura P.; Wang, Yan; Kalidindi, Surya R.; McDowell, David L.

Data-driven tools for finding structure–property (S–P) relations, such as the Materials Knowledge System (MKS) framework, can accelerate materials design, once the costly and technical calibration process has been completed. A three-model method is proposed to reduce the expense of S–P relation model calibration: (1) direct simulations are performed as per (2) a Gaussian process-based data collection model, to calibrate (3) an MKS homogenization model in an application to α-Ti. The new methods are compared favorably with expert texture selection on the performance of the so-calibrated MKS models. Benefits for the development of new and improved materials are discussed.