# Deep Learning Convective Flow Using Conditional Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2005.06422 · HTML: https://mlsanity.com/paper/2005.06422/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2020.

## Built on by (most cited first, 4 of 4)

- [Surrogate Modeling of Melt Pool Thermal Field using Deep Learning](https://mlsanity.com/paper/2207.12259/) (2022)
- [Deep Learning Models on CPUs: A Methodology for Efficient Training](https://mlsanity.com/paper/2206.10034/) (2022)
- [MeshDQN: A Deep Reinforcement Learning Framework for Improving Meshes in Computational Fluid Dynamics](https://mlsanity.com/paper/2212.01428/) (2022)
- [FluxGAN: A Physics-Aware Generative Adversarial Network Model for Generating Microstructures That Maintain Target Heat Flux](https://mlsanity.com/paper/2310.04622/) (2023)

## 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.