Deep Learning Convective Flow Using Conditional Generative Adversarial Networks

Changlin Jiang; Amir Barati Farimani

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arXiv · 2005.06422

Deep Learning Convective Flow Using Conditional Generative Adversarial Networks

Changlin Jiang·Amir Barati Farimani

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Abstract

We developed a general deep learning framework, FluidGAN, capable of learning and predicting time-dependent convective flow coupled with energy transport. FluidGAN is thoroughly data-driven with high speed and accuracy and satisfies the physics of fluid without any prior knowledge of underlying fluid and energy transport physics. FluidGAN also learns the coupling between velocity, pressure, and temperature fields. Our framework helps understand deterministic multiphysics phenomena where the underlying physical model is complex or unknown.

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physics.flu-dyn cs.LG physics.comp-ph

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BibTeX RIS

Changlin Jiang, Amir Barati Farimani. 2020-05-13. Deep Learning Convective Flow Using Conditional Generative Adversarial Networks. https://arxiv.org/abs/2005.06422

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