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DTSTAMP:20260422T000712Z
LOCATION:501-502
DTSTART;TZID=America/Denver:20231113T171500
DTEND;TZID=America/Denver:20231113T173000
UID:submissions.supercomputing.org_SC23_sess440_ws_ai4s121@linklings.com
SUMMARY:Tencoder:  Tensor-Product Encoder-Decoder Architecture for Predict
 ing Solutions of PDEs with Variable Boundary Data
DESCRIPTION:Aditya Kashi (Oak Ridge National Laboratory (ORNL))\n\nIt is w
 idely hoped that artificial intelligence will boost data-driven surrogate 
 models in science and engineering. However, fundamental spatial aspects of
  AI surrogate models remain under-studied. We investigate the ability of n
 eural-network surrogate models to predict solutions to PDEs under variable
  boundary values. We do not wish to retrain the model when the boundary va
 lues change but to make them inputs to the model and infer the solution of
  the PDE under those boundary conditions. Such a capability is essential t
 o making AI-based surrogate models practically useful. While simple feed-f
 orward networks are used for one-dimensional (1D) Poisson equation, an enc
 oder-decoder architecture with a tensor-product layer is developed for the
  two-dimensional Poisson equation posed on a rectangular domain. We show t
 hat it is indeed possible to infer solutions to PDEs from variable boundar
 y data using neural networks in this relatively simple setting, and point 
 to future directions.\n\nTag: Artificial Intelligence/Machine Learning\n\n
 Registration Category: Workshop Reg Pass\n\nSession Chairs: Murali Emani (
 Argonne National Laboratory (ANL)); Gokcen Kestor (Barcelona Supercomputin
 g Center (BSC); University of California, Merced); and Dong Li (University
  of California, Merced)\n\n
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