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  • PhD Defense - Sungyong Seo

    Fri, Jun 18, 2021 @ 01:00 PM - 02:30 PM

    Computer Science

    University Calendar

    PhD Candidate: Sungyong Seo

    Committee: Prof. Yan Liu (chair), Prof. Xiang Ren, Prof. Antonio Ortega

    Date: June 18th, 2021
    Time: 1:00-2:30pm
    Zoom Link: https://usc.zoom.us/j/7346393285

    Title: Physics-aware Graph Networks for Spatiotemporal Physical Systems

    While deep neural networks have been successful over a number of applications, it is still challenging to achieve a robust model for physical systems since data-driven learning does not explicitly consider physical knowledge, which should be beneficial for modeling. To leverage domain knowledge for robust learning, I propose various novel methods to incorporate physical knowledge for modeling spatiotemporal observations from physical systems. First, in my talk, I quantify data quality inspired by physical properties of fluids to identify abnormal observations and improve forecasting performance. The second work proposes a regularizer to explicitly impose partial differential equations (PDEs) associated with physical laws to provide an inductive bias in the latent space. The third method focuses on the approximation of spatial derivatives, which are one of the fundamental components of spatiotemporal PDEs. Then, I demonstrate a meta-learning framework to prove that the physics-related quantity is beneficial for fast-adaptation of learnable models on few observations. Finally, I propose spatiotemporal modeling via physics-aware causality, which leverages additional causal information described in PDEs for physical systems. All methods share a common goal: how to integrate physical knowledge with graph networks to model sensor-based physical systems by providing a strong inductive bias.

    WebCast Link: Link: https://usc.zoom.us/j/7346393285

    Audiences: Everyone Is Invited

    Contact: Lizsl De Leon


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