A paper co-authored by Wilson Gregory, a PhD candidate in the Whiting School of Engineering’s Department of Applied Mathematics and Statistics, received the 2024 Best Paper Award at the Machine Learning for the Physical Science workshop during the 38th Conference on Neural Information Processing Systems (NeurIPS), held December 10 through 15 in Vancouver.
“Robust Emulator for Compressible Navier-Stokes using Equivariant Geometric Convolutions“ was written by Gregory with Soledad Villar, assistant professor, and Kaze Wong, assistant research professor—both from the Department of Applied Mathematics and Statistics—and David Hogg, a professor of physics and data science at New York University.
The workshop highlights the distinctive features of the physical sciences, emphasizing current challenges and the bidirectional opportunities between machine learning and the physical sciences.