Data-driven prediction of unsteady flow over a circular cylinder using deep learning

By Sangseung Lee, Donghyun You. Journal of Fluid Mechanics, 2018

317 citations recorded by the source corpus.

Unsteady flow fields over a circular cylinder are used for training and then prediction using four different deep learning networks: generative adversarial networks with and without consideration of conservation laws; and convolutional neural networks with and without consideration of conservation laws. Flow fields at future occasions are predicted based on information on flow fields at previous occasions. Predictions of deep learning networks are made for flow fields at Reynolds numbers that were not used during training. Physical loss functions are proposed to explicitly provide information on conservation of mass and momentum to deep learning networks. An adversarial training is applied to extract features of flow dynamics in an unsupervised manner. Effects of the proposed physical loss functions and adversarial training on predicted results are analysed. Captured and missed flow physics from predictions are also analysed. Predicted flow fields using deep learning networks are in good agreement with flow fields computed by numerical simulations.

Subfields: Computational Mechanics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/bd1b4ff2fae9b761a9fade041aab1a5ac45067b7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

In Statistical and Nonlinear Physics

Citations in the corpus

Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows (2020, background): “…the accuracy of the spatio-temporal super resolution analysis, we likely need to prepare a well-designed architecture which can take physics into account in its structure, e.g., loss function (Lee & You 2019; Raissi et al. 2020) and choice for input and output attributes, i.e., feature engineering.”
Learning Mesh-Based Simulation with Graph Networks (2020, background): “Conversely, the accuracy of predictions can be increased by including specialized knowledge about the system modelled in the form of loss terms [39, 21], or by physics-informed feature normalization [36].”
Unsupervised deep learning for super-resolution reconstruction of turbulence (2020, background): “As a fundamental example, Lee & You (2019) studied the historical prediction of flow around a cylinder using generative adversarial networks (GAN).”