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UID:63@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20240718T093000
DTEND;TZID=Asia/Kolkata:20240718T103000
DTSTAMP:20240710T152647Z
URL:https://cds.iisc.ac.in/events/m-tech-research-thesis-colloquium-cdslea
 rning-multiple-initial-conditions-using-physics-informed-neural-networks-p
 inns/
SUMMARY:M.Tech Research Thesis {Colloquium}: CDS:"Learning Multiple Initial
  Conditions Using Physics-Informed Neural Networks (PINNs)"
DESCRIPTION:DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\nM.Tech Resarch T
 hesis Colloquium\n\n\n\nSpeaker : Mr. Mahesh Tom\nS.R. Number : 06-18-01-1
 0-22-21-1-20317\nTitle : "Learning Multiple Initial Conditions Using Physi
 cs-Informed Neural Networks (PINNs)"\nResearch Supervisor : Prof. Sashikum
 aar Ganesan\nDate &amp\; Time : July 18\, 2024 (Thursday)\, 9:30 AM\nVenue
  : # 102 CDS Seminar Hall\n\nABSTRACT\n\nPhysics-Informed Neural Networks 
 (PINNs) and their variants have emerged as tools for solving differential 
 equations in the past few years. Although several variants of PINNs have b
 een proposed for time-dependent partial differential equations (PDEs)\, th
 e majority of these physics-informed approaches are based on solving a pro
 blem for a single set of initial conditions. In this work\, we consider on
 e-dimensional time-dependent PDEs and focus on solving multiple initial co
 nditions (ICs) with a single network simultaneously. Trying to solve multi
 ple ICs in a single network presents certain challenges\, such as spectral
  bias\, that we address in our work. We also look at how our approach perf
 orms in the FastVPINNs framework to solve multiple ICs using Variational P
 hysics-Informed Neural Networks (VPINNs). The choice of activation functio
 ns is crucial in the performance of a network\; hence we also test the inf
 luence of various activation functions on FastVPINNs for some standard tes
 t cases. While training multiple ICs\, we also look at the impact of the n
 etwork parameters and how they contribute to each trained task via an abla
 tion study.\n\nOnce we have a fully trained model that works on multiple I
 Cs\, incorporating new ICs without having to retrain all the previous ICs 
 is a challenging task\, and a brute-force way of training all the ICs agai
 n is not always feasible. To this end\, we explore the usage of elastic we
 ight consolidation (EWC)\, a regularization technique that is used in cont
 inual learning\, and study its effect on PINNs for training new ICs.\n\n\n
 \nALL ARE WELCOME
CATEGORIES:Events,MTech Research Thesis Colloquium
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