Physics-Based Machine Learning for Electromagnetic Inverse Problems

As a Peter O’Donnell Jr. Postdoctoral Fellow at the Institute for Computational Engineering and Sciences, University of Texas at Austin, my research with Omar Ghattas and Tan Bui focused on physics-based machine learning and inverse problems in electromagnetics and acoustoelastics, with applications in oil exploration.

I maintained a legacy C codebase with more than 1.5 million lines and performed large-scale runs on thousands of CPU cores at TACC.

Electromagnetic scattering on a spherical void with a total-field/scattered-field formulation and a perfectly matched layer cropped away.