Reference : Convolution, aggregation and attention based deep neural networks for accelerating si...
E-prints/Working papers : First made available on ORBilu
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) >]
Nov-2022
No
[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 nonlinear
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
H2020 ; 764644 - RAINBOW - Rapid Biomechanics Simulation for Personalized Clinical Design

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