Fourier neural operator based fluid-structure interaction for predicting the vesicle dynamics

DOI10.1016/J.PHYSD.2024.134145MaRDI QIDQ6554917

Meng Zhao, Jinqiao Duan, Ting Gao, Kai Liu, Wang Xiao

Publication date: 13 June 2024

Published in: Physica D (Search for Journal in Brave)

zbMATH Keywords

Navier-Stokes equationvesicle dynamicsFourier neural operatorfluid-structure interaction solver

Mathematics Subject Classification ID

Spectral methods applied to problems in fluid mechanics (76M22) Stokes and related (Oseen, etc.) flows (76D07)

Cites Work

• Title not available (Why is that?)
• A stochastic immersed boundary method for fluid-structure dynamics at microscopic length scales
• The Deep Ritz Method: a deep learning-based numerical algorithm for solving variational problems
• Meta-mgnet: meta multigrid networks for solving parameterized partial differential equations
• Efficient simulation of thermally fluctuating biopolymers immersed in fluids on 1-micron, 1-second scales
• Pearling, wrinkling, and buckling of vesicles in elongational flows
• Structure and bifurcation of pullback attractors in a non-autonomous Chafee-Infante equation
• Dynamics of a vesicle in general flow
• The immersed boundary method
• A Simple Mesh Generator in MATLAB
• Nonlinear simulations of vesicle wrinkling
• Neural-network-based fluid-structure interaction applied to vortex-induced vibration
• A novel artificial neural network-based interface coupling approach for partitioned fluid-structure interaction problems

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