Virtual LANS Seminar

Seminar Title: Parsimonious, Structure Exploiting Neural Network Surrogates for Parameter-to-Output Maps Governed by PDEs
Speaker: Thomas O’Leary-Roseberry, PhD Student, Oden Institute for Computational Engineering & Sciences, University of Texas Austin

Date/Time: December 2, 2020 / 10:30 AM – 11:30 AM
Location: See meeting URL on the cels-seminars website (requires Argonne login)

Description: Outer-loop problems arising in scientific applications (such as optimization, uncertainty quantification and inverse problems) require repeated evaluation of computationally intensive numerical models, such as those arising from discretization and solution of ordinary and partial differential equations. The cost of these evaluations makes solution using the model prohibitive, and efficient accurate surrogates are a key to solving these problems in practice. In this talk I will investigate how informed subspaces of the input and output spaces can be used to construct parsimonious encoder-decoder neural networks that have weight dimensions that can be made independent of the discretization dimension. These compact representations require relatively few data to train and outperform conventional data-driven approaches which require large training data sets.

Please note that the meeting URL for this event can be seen on the cels-seminars website, which requires an Argonne login.