A hybrid partitioned deep learning methodology for moving interface and fluid-structure interaction
type: Item
instance of: scholarly article
title: A hybrid partitioned deep learning methodology for moving interface and fluid-structure interaction
author: Rachit Gupta
author: Rajeev K. Jaiman
published in: Computers and Fluids
publication date: 2022-01-26T00:00:00Z
full work available at URL: https://arxiv.org/abs/2102.09095
Mathematics Subject Classification ID: 76-XX
zbMATH DE Number: 7464443
zbMATH Keywords: fluid-structure interaction
zbMATH Keywords: deep learning-based reduced-order model
zbMATH Keywords: proper orthogonal decomposition
zbMATH Keywords: convolutional autoencoder
zbMATH Keywords: long short-term memory network
zbMATH Keywords: digital twin
describes a project that uses: DeepONet
describes a project that uses: DeepAdverserialRegulariser
describes a project that uses: Adam
describes a project that uses: AntisymmetricRNN
MaRDI profile type: Publication
OpenAlex ID: W3213650470
cites work: Self-induced flapping dynamics of a flexible inverted foil in a uniform flow
cites work: A variational flexible multibody formulation for partitioned fluid-structure interaction: application to bat-inspired drones and unmanned air-vehicles
cites work: Partitioned iterative and dynamic subgrid-scale methods for freely vibrating square-section structures at subcritical Reynolds number
cites work: A robust and accurate finite element framework for cavitating flows with moving fluid-structure interfaces
cites work: Q3321594
cites work: Q4265376
cites work: The immersed boundary method
cites work: A DLM/FD method for fluid/flexible-body interactions
cites work: A phase-field model for fluid-structure interaction
cites work: Q5270493
cites work: The Kolmogorov-Arnold representation theorem revisited
cites work: Approximation by superpositions of a sigmoidal function
cites work: Machine learning closures for model order reduction of thermal fluids
cites work: A paradigm for data-driven predictive modeling using field inversion and machine learning
cites work: Solving high-dimensional partial differential equations using deep learning
cites work: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
cites work: Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
cites work: Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks
cites work: Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem
cites work: Non-intrusive reduced order modeling of nonlinear problems using neural networks
cites work: Physics-informed machine learning for reduced-order modeling of nonlinear problems
cites work: Nonlinear Model Reduction via Discrete Empirical Interpolation
cites work: Decomposition of wake dynamics in fluid–structure interaction via low-dimensional models
cites work: POD and CVT-based reduced-order modeling of Navier-Stokes flows
cites work: A trivariate Clough-Tocher scheme for tetrahedral data
cites work: A Method for Interpolating Scattered Data Based Upon a Minimum Norm Network
cites work: A triangle-based \(C^ 1\) interpolation method
DOI: 10.1016/J.COMPFLUID.2021.105239
description: scientific article; zbMATH DE number 7464443