Reference : Convolution, aggregation and attention based deep neural networks for accelerating si...
Scientific journals : Article
Engineering, computing & technology : Multidisciplinary, general & others
Computational Sciences
http://hdl.handle.net/10993/52915
Convolution, aggregation and attention based deep neural networks for accelerating simulations in mechanics
English
Deshpande, Saurabh mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE) >]
Sosa, Raul Ian mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Physics and Materials Science (DPHYMS) >]
Bordas, Stéphane mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE) >]
Lengiewicz, Jakub mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE) >]
Mar-2023
Frontiers in Materials
Frontiers Media S.A.
Yes
International
2296-8016
Lausanne
Switzerland
[en] Surrogate Modeling ; Deep Learning ; CNN U-NET ; Graph U-Net ; Perceiver IO ; Finite Element Method
[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.
Researchers ; Professionals
http://hdl.handle.net/10993/52915
10.3389/fmats.2023.1128954
https://www.frontiersin.org/articles/10.3389/fmats.2023.1128954/full
H2020 ; 764644 - RAINBOW - Rapid Biomechanics Simulation for Personalized Clinical Design

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