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Data-driven Modelling of Nonlinear Spatio-temporal Fluid Flows Using a Deep Convolutional Generative Adversarial Network by Cheng, M.; Fang, F.; Pain, C.C.; Navon, I.M. is a Engineering article available to read on EtoBox.

What is Data-driven Modelling of Nonlinear Spatio-temporal Fluid Flows Using a Deep Convolutional Generative Adversarial Network about?

Deep learning techniques for fluid flow modelling have gained significant attention in recent years. Advanced deep learning techniques achieve great progress in rapidly predicting fluid flows without prior knowledge of the underlying physical relationships. However, most of existing researches focused mainly on either sequence learning or spatial learning, rarely on both spatial and temporal dynamics of fluid flows (Reichstein et al., 2019). In this work, an Artificial Intelligence (AI) fluid model based on a general deep convolutional generative adversarial network (DCGAN) has been developed for predicting spatio-temporal flow distributions. In deep convolutional networks, the high-dimensional flows can be converted into the lowdimensional "latent" representations. The complex features of flow dynamics can be captured by the adversarial networks. The above DCGAN fluid model enables us to provide reasonable predictive accuracy of flow fields while maintaining a high computational efficiency. The performance of the DCGAN is illustrated for two test cases of Hokkaido tsunami with different incoming waves along the coastal line. It is demonstrated that the results from the DCGAN are c

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Author

Cheng, M.; Fang, F.; Pain, C.C.; Navon, I.M.

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0045-7825)

Published

2020

Language

EN

Field

Engineering (Physical Sciences)