# Mesh motion in fluid-structure interaction with deep operator networks

arXiv: https://arxiv.org/abs/2402.00774 · HTML: https://mlsanity.com/paper/2402.00774/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 1 Feb 2024.

## Builds on (most cited first, 6 of 6)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Fourier Neural Operator for Parametric Partial Differential Equations](https://mlsanity.com/paper/2010.08895/) (2020)
- [DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators](https://mlsanity.com/paper/1910.03193/) (2019)
- [M2N: Mesh Movement Networks for PDE Solvers](https://mlsanity.com/paper/2204.11188/) (2022)
- [Mesh deformation techniques in fluid-structure interaction: robustness, accumulated distortion and computational efficiency](https://mlsanity.com/paper/2006.14051/) (2020)
- [A continuous perspective on modeling of shape optimal design problems](https://mlsanity.com/paper/2004.06942/) (2020)

## 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.