Deep Learning Convective Flow Using Conditional Generative Adversarial Networks
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 May 2020.
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Citing papers per year: 2022: 3; 2023: 1.
4 papers.
- 2022Surrogate Modeling of Melt Pool Thermal Field using Deep Learningcited by 8
- 2022Deep Learning Models on CPUs: A Methodology for Efficient Trainingcited by 1
- 2022MeshDQN: A Deep Reinforcement Learning Framework for Improving Meshes in Computational Fluid Dynamicscited by 1
- 2023FluxGAN: A Physics-Aware Generative Adversarial Network Model for Generating Microstructures That Maintain Target Heat Fluxcited by 0
- 2023FluxGAN: A Physics-Aware Generative Adversarial Network Model for Generating Microstructures That Maintain Target Heat Flux6 Oct 2023
- 2022MeshDQN: A Deep Reinforcement Learning Framework for Improving Meshes in Computational Fluid Dynamics2 Dec 2022
- 2022Surrogate Modeling of Melt Pool Thermal Field using Deep Learning25 Jul 2022
- 2022Deep Learning Models on CPUs: A Methodology for Efficient Training20 Jun 2022