Mesh-Conv: convolution operator with mesh resolution independence for flow field modeling

DOI10.1016/j.jcp.2021.110896OpenAlexW4200565677WikidataQ113488881 ScholiaQ113488881MaRDI QIDQ2133572

Jiawei Hu, Wei-Wei Zhang

Publication date: 4 May 2022

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jcp.2021.110896

zbMATH Keywords

machine learningconvolutional neural networkflow field modelinglocal weights

Mathematics Subject Classification ID

Basic methods in fluid mechanics (76Mxx) Artificial intelligence (68Txx) Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65Mxx)

Related Items (1)

Airfoil-based convolutional autoencoder and long short-term memory neural network for predicting coherent structures evolution around an airfoil

Uses Software

• Tensor2Tensor
• U-Net
• PDE-Net
• DGM
• EfficientNet
• PhyGeoNet
• ESRGAN
• DeepONet

Cites Work

• A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems
• Hidden physics models: machine learning of nonlinear partial differential equations
• Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification
• DGM: a deep learning algorithm for solving partial differential equations
• PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain
• Data-driven discovery of PDEs in complex datasets
• Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
• Enforcing constraints for interpolation and extrapolation in generative adversarial networks
• PDE-Net 2.0: learning PDEs from data with a numeric-symbolic hybrid deep network
• A deep learning approach for efficiently and accurately evaluating the flow field of supercritical airfoils
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Prediction of aerodynamic flow fields using convolutional neural networks
• Machine Learning for Fluid Mechanics
• Prediction of turbulent heat transfer using convolutional neural networks
• Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows
• Data-driven prediction of unsteady flow over a circular cylinder using deep learning
• Deep learning in fluid dynamics

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