Hybrid and Surrogate Models

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.