CHEN, Junfeng

Professor CHEN, Junfeng is an Assistant Professor in the Department of Mathematics at The Hong Kong University of Science and Technology. He received his Bachelor degrees from Tsinghua University in 2016, followed by a Diplôme d'Ingénieur and a PhD from Mines Paris – Université PSL. From 2022 to 2025, he was a postdoctoral researcher at Southern University of Science and Technology. His research interests include reliable, efficient, and robust deep-learning methods for computational science and engineering; data-driven flow modeling; multiscale and chaotic dynamics; dimension reduction and reduced-order modeling. Selected publications include works in SIAM Review, Proceedings of Machine Learning Research, Journal of Computational Physics, Neural Computing and Applications, and Physics of Fluids.

Junfeng Chen is an Assistant Professor at HKUST, specializing in scientific machine learning. His research focuses on applying deep learning techniques to solve complex problems in fluid dynamics and operator learning. Chen's work includes developing graph neural networks for laminar flow prediction, U-net architectures for fast flow simulation, and deep learning frameworks for modeling unknown equations. His publications explore methods for incompressible laminar flow reconstruction, uncertainty estimation, and the challenges of conditional-mean barriers in scientific machine learning surrogates. His research bridges computational science and artificial intelligence to enhance predictive modeling in physical systems.