ESE Fall Seminar – “Approximate symmetries in machine learning”

Glandt Forum, Singh Center for Nanotechnology 3205 Walnut Street, Philadelphia, PA, United States

In this talk, we explain different roles that symmetries and approximate symmetries can play in machine learning models. We define approximately equivariant graph neural networks and we show a bias-variance […]

PICS Colloquium: “MFEM: Accelerating Efficient Solution of PDEs at Exascale”

https://upenn.zoom.us/j/96715197752

Upcoming exascale architectures require rethinking of the numerical algorithms used in large-scale PDE-based applications. These architectures favor algorithms, such as high-order finite elements, that expose fine-grain parallelism and maximize the […]