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DTSTAMP:20230831T095746Z
LOCATION:Davos
DTSTART;TZID=Europe/Stockholm:20230627T102700
DTEND;TZID=Europe/Stockholm:20230627T102800
UID:submissions.pasc-conference.org_PASC23_sess110_pos115@linklings.com
SUMMARY:P55 - Novel Geometric Deep Learning Surrogate Framework for Non-Li
 near Finite Element Simulations
DESCRIPTION:Poster\n\nSaurabh Deshpande (University of Luxembourg); Jakub 
 Lengiewicz (University of Luxembourg, IPPT PAN); and Stéphane Bordas (Univ
 ersity of Luxembourg)\n\nConventional numerical methods are computationall
 y expensive in simulating non-linear phenomena arising in mechanics. In th
 is aspect, deep learning (DL) techniques are being increasingly used for a
 ccelerating simulations in mechanics. However, existing DL methods perform
  inefficiently as the size and complexity of the problem increases. In thi
 s work we propose a novel geometric deep learning surrogate framework, whi
 ch can efficiently find non-linear mappings between mesh-based datasets. I
 n particular, we propose two novel neural network layers, Multichannel Agg
 regation (MAg) layer, and the graph pooling layer, which are combined to c
 onstitute a robust graph U-Net architecture. Our framework can efficiently
  tackle problems involving complex fine meshes and scales efficiently to l
 arge dimensional inputs. We validate the performance of our framework by l
 earning on numerically generated non-linear finite element datasets and by
  comparing the performance to state-of-the-art convolutional neural networ
 k frameworks. In particular, we show that the proposed GDL framework is ab
 le to accurately predict the nonlinear deformations of irregular soft bodi
 es in real-time.\n\nSession Chair: Jibonananda Sanyal (National Renewable 
 Energy Laboratory)
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