Mesh motion in fluid-structure interaction with deep operator networks
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Abstract (arXiv)
A mesh motion model based on deep operator networks is presented. The model is trained on and evaluated against a biharmonic mesh motion model on a fluid-structure interaction benchmark problem and further evaluated in a setting where biharmonic mesh motion fails. The performance of the proposed mesh motion model is comparable to the biharmonic mesh motion on the test problems.
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References · 6
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2020Fourier Neural Operator for Parametric Partial Differential Equationscited by 2,908
- 2019DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operatorscited by 2,018
- 2022M2N: Mesh Movement Networks for PDE Solverscited by 16
- 2020Mesh deformation techniques in fluid-structure interaction: robustness, accumulated distortion and computational efficiencycited by 2
- 2020A continuous perspective on modeling of shape optimal design problemscited by 1
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