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AuthorStephen, Trent
AuthorRenno, Jamil
AuthorSassi, Sadok
AuthorMohamed, M. Shadi
Available date2023-11-30T08:25:51Z
Publication Date2023-04-15
Publication NameComputers & Mathematics with Applications
Identifierhttp://dx.doi.org/10.1016/j.camwa.2022.11.024
CitationTrent, S., Renno, J., Sassi, S., & Mohamed, M. S. (2023). Using image processing techniques in computational mechanics. Computers & Mathematics with Applications, 136, 1-24.
ISSN0898-1221
URIhttps://www.sciencedirect.com/science/article/pii/S089812212200493X
URIhttp://hdl.handle.net/10576/49846
AbstractThe implementation methods of finite element analysis (FEA) have remained essentially unchanged since the inception of FEA in the 1960s. Alterations of any of the input or design parameters to the FEA model can potentially nullify the previous results and subsequent additional simulations will be required. This is particularly relevant for situations that require active monitoring where telemetry is to be passed to remote systems capable of carrying out FEA computations. In this paper, we train an artificial neural network that was originally developed for image processing to emulate FEA. Conventionally generated FEA results are transformed into image pairs where the load, material and geometric properties are assigned different colour channels. These images are used to train a conditional Generative Adversarial Network (cGAN). The subsequent “trained” cGAN can generate predictions for arbitrary inputs which correspond to the domain of input on which the developed cGAN was trained. Three numerical experiments were conducted resulting in three separate cGANs trained to infer (a) deflections from forces, (b) stresses from deflections and (c) stresses from forces. After a moderate training regime of 200 epochs each, the outputs of the trained networks are shown to be in reasonable agreement to the ground truth with mean errors in the range of 5-10%. The contribution of this work lies in transforming FEA results into images which enables the usage of cGANs to solve a computational mechanics problem. The implementation herein allows for near real-time computations which highlights the potential of the proposed methodology in applications where simulation results are required in a timely manner such as predictive control, interactive virtual environment, etc. All the codes used in this research will be openly available at Qatar University's Institutional Repository1; the data used in this work will be available upon request from the corresponding author.
SponsorFinancial support provided by Qatar National Research Fund through the National Priorities Research Program under grant number NPRP 11S-1220-170112 and Qatar University Internal Grant QUCG-CENG-19/20-6 .
Languageen
PublisherElsevier
SubjectConditional Generative Adversarial Network
Applied mechanics
Image processing
Computational mechanics
Finite element method
Real-time predictions
TitleUsing image processing techniques in computational mechanics
TypeArticle
Pagination1-24
Volume Number136
ESSN1873-7668
dc.accessType Full Text


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