FlowGAN: A Conditional Generative Adversarial Network for Flow Prediction in Various Conditions
Published in ICTAI, 2020
Recommended citation: Donglin Chen, Xiang Gao, Chuanfu Xu, Shizhao Chen, Jianbin Fang, Zhenghua Wang, Zheng Wang. "FlowGAN: A Conditional Generative Adversarial Network for Flow Prediction in Various Conditions." ICTAI. 2020. http://jianbinfang.github.io/files/2020-09-03-ictai.pdf
Existing DL-based models have to be re-trained whenever the flow condition changes, which incurs significant training overhead for real-life scenarios with a wide range of flow conditions. This paper presents FLOWGAN, a novel conditional generative adversarial network for accurate prediction of flow fields in various conditions. FLOWGAN is designed to directly obtain the generation of solutions to flow fields in various conditions based on observations rather than re-training.
Recommended citation: Donglin Chen, Xiang Gao, Chuanfu Xu, Shizhao Chen, Jianbin Fang, Zhenghua Wang, Zheng Wang. (2020). “FlowGAN: A Conditional Generative Adversarial Network for Flow Prediction in Various Conditions.” ICTAI. 2020.