Stony Brook University Mechanical Engineering

Research Areas:

• Computational fluid dynamics

• Machine learning

• High-performance computing

• Respiratory and hemodynamic flows

• Nasal cavity surgery planning

• Urban flows

• Drag-efficient UAV path planning

• Flow control with reinforcement learning

Representative Research Grants:

• PI, German Research Foundation (Deutsche Forschungsgemeinschaft (DFG)), “High-fidelity CFD and ML-based optimization of renewable, flow-dependent urban energy systems", Since 2024.

• Participant, HANAMI (EU-Japan HPC project for bio-medical applications), 2024.

• Participant, interTwin (EU digital twin project), 2023-2024.

• Participant, CoE RAISE (Simulation-based engineering at exascale), 2023-2024. 

• Participant, AM-SIT (DFG Project on AI and medical simulation data), 2019-2023.

Major research achievements:

• 19 papers in international journals and 8 peer-reviewed conference papers

• Developer of the urbanFlowGen and urbanPathGen libraries and contributor to the HydroGym CFD–RL platform

• Principal applicant for multi-million CPU/GPU-hour allocations at Jülich Supercomputing Centre since 2019

• Invited Keynote Speaker at the HANAMI Symposium, Dec. 2025: “Synergizing CFD and Machine Learning: From Personalized Surgery to Smart Cities”

• Walter Benjamin Fellowship (DFG, Germany), 2024

• Best Paper Award, “Parallel reinforcement learning and gaussian process regression for improved physics-based nasal surgery planning”, PPAM Conference, 2024

• Korean Government Scholarship Program, 2015

• Chinese Government Scholarship Program, 2014

Representative publications:

• “The HydroGym Reinforcement Learning Platform for Fluid Dynamics”, with Christian Lagemann, Ricardo Vinuesa, Steve Brunton, and others, Nature (2026)

• “Drag-Aware UAV Path Planning in Unseen Urban Domains Using Graph Convolutional Neural Network-Based Flow Prediction”, H. Lee, R. Puri, R. Sarma, A. Lintermann, S. Lee, M. Rüttgers, Journal of Mechanical Science and Technology (2026)

• “Towards a widespread usage of computational fluid dynamics simulations for automated virtual nasal surgery planning”,  M. Rüttgers, M. Waldmann, F. Hübenthal, K. Vogt, M. Tsubokura, S. Lee, and A. Lintermann, Future Generation Computer Systems (2026)

• “Comparative analysis of the flow in a realistic human airway”, M. Rüttgers, J. Vorspohl, L. Mayolle, B. Johanning-Meiners, D. Krug, M. Klaas, M. Meinke, S. Lee, W. Schröder, and A. Lintermann, Physics of Fluids (2025)

• “Patient-specific lattice-boltzmann simulations with inflow conditions from magnetic resonance velocimetry measurements for analyzing cerebral aneurysms”, M. Rüttgers, M. Waldmann, S. Ito, C. Wüstenhagen, S. Grundmann, M. Brede, and A. Lintermann, Computers in Biology and Medicine (2025)

• “Automated surgery planning for an obstructed nose by combining computational fluid dynamics with reinforcement learning”, M. Rüttgers, M. Waldmann, K. Vogt, J. Ilgner, W. Schröder, and A. Lintermann, Computers in Biology and Medicine (2024)

• “A machine-learning-based method for automatizing lattice-boltzmann simulations of respiratory flows”, M. Rüttgers, M. Waldmann, W. Schröder, and A. Lintermann, Applied Intelligence (2022)

• “Prediction of typhoon track and intensity using a generative adversarial network with observational and meteorological data“, M. Rüttgers, S. Jeon, S. Lee, and D. You, IEEE Access (2022)

• “Prediction of a typhoon track using a generative adversarial network and satellite images”, M. Rüttgers, S. Lee, S. Jeon, and D. You, Scientific Reports (2019)

• “Large-eddy simulation of turbulent flow over the drivaer fastback vehicle model”, M. Rüttgers, J. Park, and D. You, Journal of Wind Engineering and Industrial Aerodynamics (2019)

Lecture:

• 2025, Numerical Analysis, Inha University

• 2025-2026, Turbulent Flows, Inha University