Invited talk on GNNs and Bayesian DL in computational science

Abstract

I presented my approach to UQ and ML when dealing with large-volumes of high-dimensional data that contain some structure. I briefly presented the supervised learning work on crack localization with arbitrarily positioned strain-sensors and parts of the work on building supervised/semi-supervised graph-structured latent variable models with the relational VAE. Moreover, I presented some results that appear in my PhD work using trained RVAEs for selecting optimal sub-sets of turbines for monitoring.

Date
Sep 1, 2021 2:00 PM — 3:00 PM
Event
Invited talk to ETH-Computational Science and Engineering lab
Location
ETH Zurich
Charilaos Mylonas
Charilaos Mylonas
Doctoral Researcher

Computational scientist with strong interest in deep learning