Petros Koumoutsakos

1. Machine Learning for Fluid Mechanics
   2019/09/12 by Steven L. Brunton, Bernd R. Noack, Petros Koumoutsakos · 98 citations
   Physics and Astronomy · Computer Science · Materials Science · #Model Reduction and Neural Networks #Generative Adversarial Networks and Image Synthesis #Machine Learning in Materials Science
2. Efficient collective swimming by harnessing vortices through deep reinforcement learning
   2018/02/07 by Siddhartha Verma, Guido Novati, Petros Koumoutsakos · 39 citations
   Engineering · Physics and Astronomy · Computer Science · #Biomimetic flight and propulsion mechanisms #Micro and Nano Robotics #Reinforcement Learning in Robotics
3. Multiscale Simulations of Complex Systems by Learning their Effective Dynamics
   2020/06/24 by Pantelis R. Vlachas, Georgios Arampatzis, Vlachas, Pantelis R. +5 · 8 citations
   Computer Science · #Chaotic Dynamics (nlin.CD) #Computational Physics (physics.comp-ph) #Computational Physics and Python Applications #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG)
4. Remember and Forget for Experience Replay
   2018/07/16 by Guido Novati, Petros Koumoutsakos, Novati, Guido +1 · 6 citations
   Computer Science · Engineering · Decision Sciences · #Reinforcement Learning in Robotics #Smart Grid Energy Management #Advanced Bandit Algorithms Research
5. Energy Matching: Unifying Flow Matching and Energy-Based Models for Generative Modeling
   2025/04/14 by Michał Balcerak, Michal Balcerak, Tamaz Amiranashvili +14 · 1 voice · 8 citations
   Computer Science · Physics and Astronomy · Mathematics · #Generative Adversarial Networks and Image Synthesis #Model Reduction and Neural Networks #Tensor decomposition and applications
6. Generative Learning of the Solution of Parametric Partial Differential Equations Using Guided Diffusion Models and Virtual Observations
   2024/07/31 by Han Gao, Sebastian Kaltenbach, Gao, Han +3 · 4 citations
   Physics and Astronomy · #Computational Physics (physics.comp-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Fluid Dynamics (physics.flu-dyn) #Machine Learning (cs.LG) #Model Reduction and Neural Networks
7. Physics-Regularized Multi-Modal Image Assimilation for Brain Tumor Localization
   2024/09/30 by Michał Balcerak, Tamaz Amiranashvili, Balcerak, Michal +17 · 4 citations
   Computer Science · Medicine · #Medical Image Segmentation Techniques #Medical Imaging Techniques and Applications #Advanced MRI Techniques and Applications
8. Individualizing Glioma Radiotherapy Planning by Optimization of Data and Physics-Informed Discrete Loss
   2023/12/08 by Michał Balcerak, Jonas Weidner, Balcerak, Michal +17 · 3 citations
   Mathematics · #Mathematical Biology Tumor Growth
9. Interpretable learning of effective dynamics for multiscale systems
   2023/09/11 by Emmanuel Menier, Sebastian Kaltenbach, Menier, Emmanuel +7 · 2 citations
   Physics and Astronomy · #Computational Physics (physics.comp-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks
10. Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows
    2025/02/11 by Han Gao, Sebastian Kaltenbach, Gao, Han +3 · 2 citations
    Computer Science · #Neural Networks and Applications #Data Stream Mining Techniques #Time Series Analysis and Forecasting
11. Learning on predictions: Fusing training and autoregressive inference for long-term spatiotemporal forecasts
    2024/09/16 by Pantelis R. Vlachas, P.R. Vlachas, Petros Koumoutsakos +1 · 1 citation
    Earth and Planetary Sciences · Computer Science · Environmental Science · #Meteorological Phenomena and Simulations #Data Analysis with R #Hydrological Forecasting Using AI
12. Bayesian Inference for PDE-based Inverse Problems using the Optimization of a Discrete Loss
    2025/10/17 by Lucas Amoudruz, Amoudruz, Lucas, Sergey Litvinov +5 · 2 citations
    Mathematics · #Computational Physics (physics.comp-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Markov Chains and Monte Carlo Methods #Mathematical Biology Tumor Growth #Methodology (stat.ME) #Numerical methods in inverse problems
13. Optimal, Data-Driven Wall Models for Efficient Large Eddy Simulations of Metastable von Kármán Flows
    2026/07/27 by Quentin Malé, Lucas Amoudruz, Daniel Bulgarini +4
    #physics.flu-dyn