Materials Data Science and Informatics (IAS-9)

Surrogate (or forward) models utilize simulation datasets or microscopy images to learn complex spatio-temporal relationships to scalar or tensorial values or fields. For example, predicting a 2D field of pressure values in a fluid where the Reynolds number is desired, or determining accumulated plastic strain from a grain microstructure. Our goal is to develop computationally efficient surrogate models that meet the accuracy requirements of scientific problems.

Related Publications:

• B. D. Nguyen, P. Potapenko, A. Demirci, K. Govind, S. Bompas, S. Sandfeld, Efficient surrogate models for materials science simulations: Machine learning-based prediction of microstructure properties, Machine Learning with Applications, Volume 16, 2024, 100544, ISSN 2666-8270, https://doi.org/10.1016/j.mlwa.2024.100544