Article (Scientific journals)
Convolution, aggregation and attention based deep neural networks for accelerating simulations in mechanics
Deshpande, Saurabh; Sosa, Raul Ian; Bordas, Stéphane et al.
2023In Frontiers in Materials
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Keywords :
Surrogate Modeling; Deep Learning; CNN U-NET; Graph U-Net; Perceiver IO; Finite Element Method
Abstract :
[en] Deep learning surrogate models are being increasingly used in accelerating scientific simulations as a replacement for costly conventional numerical techniques. However, their use remains a significant challenge when dealing with real-world complex examples. In this work, we demonstrate three types of neural network architectures for efficient learning of highly non-linear deformations of solid bodies. The first two architectures are based on the recently proposed CNN U-NET and MAgNET (graph U-NET) frameworks which have shown promising performance for learning on mesh-based data. The third architecture is Perceiver IO, a very recent architecture that belongs to the family of attention-based neural networks–a class that has revolutionised diverse engineering fields and is still unexplored in computational mechanics. We study and compare the performance of all three networks on two benchmark examples, and show their capabilities to accurately predict the non-linear mechanical responses of soft bodies.
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Deshpande, Saurabh  ;  University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)
Sosa, Raul Ian ;  University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Physics and Materials Science (DPHYMS)
Bordas, Stéphane ;  University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)
Lengiewicz, Jakub ;  University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)
External co-authors :
no
Language :
English
Title :
Convolution, aggregation and attention based deep neural networks for accelerating simulations in mechanics
Publication date :
March 2023
Journal title :
Frontiers in Materials
ISSN :
2296-8016
Publisher :
Frontiers Media S.A., Lausanne, Switzerland
Peer reviewed :
Peer Reviewed verified by ORBi
Focus Area :
Computational Sciences
European Projects :
H2020 - 764644 - RAINBOW - Rapid Biomechanics Simulation for Personalized Clinical Design
Funders :
CE - Commission Européenne [BE]
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