Data-Driven Fluid Engineering Lab.

Welcome to DDFE!

Welcome to the Data-driven fluid engineering Lab (DDFE)! Our lab is dedicated to exploring the intersection of artificial intelligence and fluid engineering to create a cleaner and more sustainable future. Our team of researchers is focused on tackling some of the most pressing challenges facing our world today, including wind power harvesting, urban flow prediction, and flooding prediction.

At DDFE, we employ cutting-edge technologies such as Explainable AIs to develop new methods for discovering drag inducing/reducing surfaces. By using big meteorological data, we are also developing AIs for fast wind prediction and more efficient wind power harvesting.

We pride ourselves on taking a unique approach to our research, utilizing data-driven methods to gain insight into fluid dynamics and uncover new solutions to complex engineering problems. Our team is dedicated to driving innovation and making a positive impact in the world through our work.

What we Study

Data-driven discovery of drag inducing/reducing rough surfaces

AI-assisted industrial simulations, optimizations, and designs

Meteorological & Urban flow prediction

AI-Assisted UAM Control & Guidance

Recent News

New Publication in ICHMT!

We developed a neural network-based thermal model for Korea’s first lunar orbiter, Danuri, enabling real-time temperature prediction from the ground. By combining active and transfer learning, our model achieves high accuracy with drastically reduced computation time—serving as a digital twin for spacecraft thermal monitoring. This joint work with POSTECH and KARI pushes the frontier of AI-driven virtual metrology for space missions. Check our work at : [link]

Prof. Romit Maulik visited DDFE!

Happy to host Prof. Romit Maulik (Penn State Univ.) for an invited talk and engaging discussion at Inha University!

Launch of JK-FLOW (Japan-Korea Fluid Mechanics Online Workshop)

We are launching JK-FLOW (Japan-Korea Fluid Mechanics Online Workshop), an online seminar series on a wide range of topics in fluid mechanics. Please check JK-Flow website for participating!

Prof. Kai Fukami visited DDFE!

Happy to host Prof. Kai Fukami (Tohoku Univ.) for an invited talk and engaging discussion at Inha University!

Workshop for AI for Fluid Dynamics Systems

DDFE hosted Prof. Kai Fukami (Tohoku Univ., Generalized Super-Resolution Analysis with Machine Learning of Turbulence), Prof. Heechang Lim (Pusan National Univ., Artificial Intelligence-Driven Patterning and Reconstruction of Turbulent Flow Structures), and Dr. Mario Rüttgers (Planing Urban Wind Turbines by Means of Machine Learning and Computational Fluid Dynamics) for a workshop of AI for fluid dynamics systems (Feb. 13).

DDFE attended KSME Scientific ML Workshop

Our group members attended KSME's Scientific Machine Learning Workshop in Seoul, South Korea.

New Publication in POF!

New publication in Physics of Fluids (POF)! We developed a CNN that predicts drag forces on rough surfaces using only topography data through an international collaboration (INHA, KTH (Sweden), and KIT (Germany)). Our model reveals key patterns linked to drag, offering insights into fluid dynamics without needing complex parameters. Check out how we're advancing data-driven drag prediction (Link)!

Collaborators

Feel free to contact us if you have questions.

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