Petros Koumoutsakos


Last Name

Koumoutsakos

First Name

Petros

ORCID

0000-0001-8337-2122

Organisational unit

Search Results

Publications1 - 10 of 62
  • Independent Control and Path Planning of Microswimmers with a Uniform Magnetic Field
    Item type: Journal Article
    Amoudruz, Lucas; Koumoutsakos, Petros (2022)
    Advanced Intelligent Systems
    Artificial bacteria flagella (ABFs) are magnetic helical microswimmers that can be remotely controlled via a uniform, rotating magnetic field. Previous studies have used the heterogeneous response of microswimmers to external magnetic fields for achieving independent control. Herein, analytical and reinforcement learning control strategies for path planning to a target by multiple swimmers using a uniform magnetic field are introduced. The comparison of the two algorithms shows the superiority of reinforcement learning in achieving minimal travel time to a target. The results demonstrate, for the first time, the effective independent navigation of realistic microswimmers with a uniform magnetic field in a viscous flow field.
  • Vorticity flux control for a turbulent channel flow
    Item type: Journal Article
    Koumoutsakos, Petros (1999)
    Physics of Fluids
    A feedback control algorithm using wall only information has been applied in simulations of low Reynolds number (Reτ=180) turbulent channel flow. The present control scheme is based on the manipulation of the vorticity flux components, which can be obtained as a function of time by measuring the instantaneous pressure at the wall and calculating its gradient. The strength of the unsteady mass transpiration actuators can be derived explicitly by inverting a system of equations whose terms depend on the relative locations of the sensors and actuators. The results of the simulations indicate a large (up to 40\\%) drag reduction. Moreover it appears that using the present methodology open-loop control laws can be devised.
  • Evolutionary Optimization of Anguilliform Swimming
    Item type: Other Conference Item
    Kern, Stefan; Koumoutsakos, Petros (2006)
  • Pharmacokinetics of Anti-VEGF Agent Aflibercept in Cancer Predicted by Data-Driven, Molecular-Detailed Model
    Item type: Journal Article
    Finley, Stacey D.; Angelikopoulos, Panagiotis; Koumoutsakos, Petros; et al. (2015)
    CPT: Pharmacometrics and Systems Pharmacology
    Mathematical models can support the drug development process by predicting the pharmacokinetic (PK) properties of the drug and optimal dosing regimens. We have developed a pharmacokinetic model that includes a biochemical molecular interaction network linked to a whole-body compartment model. We applied the model to study the PK of the anti-vascular endothelial growth factor (VEGF) cancer therapeutic agent, aflibercept. Clinical data is used to infer model parameters using a Bayesian approach, enabling a quantitative estimation of the contributions of specific transport processes and molecular interactions of the drug that cannot be examined in other PK modeling, and insight into the mechanisms of aflibercept's antiangiogenic action. Additionally, we predict the plasma and tissue concentrations of unbound and VEGF-bound aflibercept. Thus, we present a computational framework that can serve as a valuable tool for drug development efforts.
  • Optimization of anguilliform swimming
    Item type: Other Conference Item
    Kern, Stefan; Koumoutsakos, Petros; Eschler, Kristina (2007)
    Physics of Fluids
  • Scientific multi-agent reinforcement learning for wall-models of turbulent flows
    Item type: Journal Article
    Bae, H. Jane; Koumoutsakos, Petros (2022)
    Nature Communications
    The predictive capabilities of turbulent flow simulations, critical for aerodynamic design and weather prediction, hinge on the choice of turbulence models. The abundance of data from experiments and simulations and the advent of machine learning have provided a boost to turbulence modeling efforts. However, simulations of turbulent flows remain hindered by the inability of heuristics and supervised learning to model the near-wall dynamics. We address this challenge by introducing scientific multi-agent reinforcement learning (SciMARL) for the discovery of wall models for large-eddy simulations (LES). In SciMARL, discretization points act also as cooperating agents that learn to supply the LES closure model. The agents self-learn using limited data and generalize to extreme Reynolds numbers and previously unseen geometries. The present simulations reduce by several orders of magnitude the computational cost over fully-resolved simulations while reproducing key flow quantities. We believe that SciMARL creates unprecedented capabilities for the simulation of turbulent flows.
  • Preface
    Item type: Other Journal Item
    Koumoutsakos, Petros (2008)
    Journal of Computational Physics
  • Feedback Control Algorithms for Turbulence Flow Control
    Item type: Other Conference Item
    Koumoutsakos, Petros (1999)
  • Automating turbulence modelling by multi-agent reinforcement learning
    Item type: Journal Article
    Novati, Guido; de Laroussilhe, Hugues Lascombes; Koumoutsakos, Petros (2021)
    Nature Machine Intelligence
    Turbulent flow models are critical for applications such as aircraft design, weather forecasting and climate prediction. Existing models are largely based on physical insight and engineering intuition. More recently, machine learning has been contributing to this endeavour with promising results. However, all efforts have focused on supervised learning, which is difficult to generalize beyond training data. Here we introduce multi-agent reinforcement learning as an automated discovery tool of turbulence models. We demonstrate the potential of this approach on large-eddy simulations of isotropic turbulence, using the recovery of statistical properties of direct numerical simulations as a reward. The closure model is a control policy enacted by cooperating agents, which detect critical spatio-temporal patterns in the flow field to estimate the unresolved subgrid-scale physics. Results obtained with multi-agent reinforcement learning algorithms based on experience replay compare favourably with established modelling approaches. Moreover, we show that the learned turbulence models generalize across grid sizes and flow conditions.
  • Energy Matching: Unifying Flow Matching and Energy-Based Models for Generative Modeling
    Item type: Working Paper
    Balcerak, Michal; Amiranashvili, Tamaz; Terpin, Antonio; et al. (2025)
    arXiv
    Current state-of-the-art generative models map noise to data distributions by matching flows or scores. A key limitation of these models is their inability to readily integrate available partial observations and additional priors. In contrast, energy-based models (EBMs) address this by incorporating corresponding scalar energy terms. Here, we propose Energy Matching, a framework that endows flow-based approaches with the flexibility of EBMs. Far from the data manifold, samples move from noise to data along irrotational, optimal transport paths. As they approach the data manifold, an entropic energy term guides the system into a Boltzmann equilibrium distribution, explicitly capturing the underlying likelihood structure of the data. We parameterize these dynamics with a single time-independent scalar field, which serves as both a powerful generator and a flexible prior for effective regularization of inverse problems. The present method substantially outperforms existing EBMs on CIFAR-10 and ImageNet generation in terms of fidelity, while retaining simulation-free training of transport-based approaches away from the data manifold. Furthermore, we leverage the flexibility of the method to introduce an interaction energy that supports the exploration of diverse modes, which we demonstrate in a controlled protein generation setting. This approach learns a scalar potential energy, without time conditioning, auxiliary generators, or additional networks, marking a significant departure from recent EBM methods. We believe this simplified yet rigorous formulation significantly advances EBMs capabilities and paves the way for their wider adoption in generative modeling in diverse domains.
Publications1 - 10 of 62