Deep Learning Convective Flow Using Conditional Generative Adversarial Networks

• URL: http://arxiv.org/abs/2005.06422v2
• Date: Sun, 18 Jun 2023 20:43:47 GMT
• Title: Deep Learning Convective Flow Using Conditional Generative Adversarial Networks
• Authors: Changlin Jiang, Amir Barati Farimani
• Abstract summary: FluidGAN is capable of learning and predicting time-dependent convective flow coupled with energy transport. Our framework helps understand deterministic multiphysics phenomena where the underlying physical model is complex or unknown.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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