Fluid Flow Modelling Using Physics-Informed Convolutional Neural Network in Parametrised Domains-Reference-Cited by-同舟云学术

Fluid Flow Modelling Using Physics-Informed Convolutional Neural Network in Parametrised Domains

Published:2023-01-02 Issue:1 Volume:37 Page:67-81
ISSN:1061-8562
Container-title:International Journal of Computational Fluid Dynamics
language:en
Short-container-title:International Journal of Computational Fluid Dynamics

Author:

Bublík Ondřej¹^ORCID,Heidler Václav¹,Pecka Aleš¹,Vimmr Jan¹²

Affiliation:

1. NTIS - New Technology for the Information Society, Faculty of Applied Sciences, University of West Bohemia, Pilsen, Czech Republic

2. Department of Mechanics, Faculty of Applied Sciences, University of West Bohemia, Pilsen, Czech Republic

Funder

Grant Agency of the Czech Republic

Publisher

Informa UK Limited

Subject

Mechanical Engineering,Mechanics of Materials,Energy Engineering and Power Technology,Condensed Matter Physics,Aerospace Engineering,Computational Mechanics

Link

https://www.tandfonline.com/doi/pdf/10.1080/10618562.2023.2260763

Reference26 articles.

1. Abadi M. P. Barham J. Chen Z. Chen A. Davis J. Dean M. Devin et al. 2016. Tensorflow: A System for Large-Scale Machine Learning . ArXiv abs/1605.08695.

2. Predicting high-fidelity multiphysics data from low-fidelity fluid flow and transport solvers using physics-informed neural networks

3. Prediction of aerodynamic flow fields using convolutional neural networks

4. Flow-Field Prediction in Periodic Domains Using a Convolution Neural Network with Hypernetwork Parametrization

5. Neural-network-based fluid–structure interaction applied to vortex-induced vibration

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