Petros Koumoutsakos

DeepAI AI Chat

Featured Co-authors

Leonidas Guibas
102 publications
Bjoern Menze
61 publications
Caroline Uhler
49 publications
Benedikt Wiestler
41 publications
Marc Schoenauer
41 publications
Koen Van Leemput
26 publications
Rupesh Kumar Srivastava
18 publications
Phaedon-Stelios Koutsourelakis
18 publications
Jana Lipkova
17 publications
Wonmin Byeon
16 publications
Themistoklis P. Sapsis
14 publications

research

∙ 09/11/2023

Interpretable learning of effective dynamics for multiscale systems

The modeling and simulation of high-dimensional multiscale systems is a ...

0 Emmanuel Menier, et al. ∙

research

∙ 05/17/2023

Discovering Individual Rewards in Collective Behavior through Inverse Multi-Agent Reinforcement Learning

The discovery of individual objectives in collective behavior of complex...

0 Daniel Waelchli, et al. ∙

research

∙ 04/04/2023

Adaptive learning of effective dynamics: Adaptive real-time, online modeling for complex systems

Predictive simulations are essential for applications ranging from weath...

0 Ivica Kičić, et al. ∙

research

∙ 03/08/2023

Flow reconstruction by multiresolution optimization of a discrete loss with automatic differentiation

We present a potent computational method for the solution of inverse pro...

0 Petr Karnakov, et al. ∙

research

∙ 03/06/2023

The stress-free state of human erythrocytes: data driven inference of a transferable RBC model

The stress-free state (SFS) of red blood cells (RBCs) is a fundamental r...

0 Lucas Amoudruz, et al. ∙

research

∙ 03/03/2023

Interpretable reduced-order modeling with time-scale separation

Partial Differential Equations (PDEs) with high dimensionality are commo...

0 Sebastian Kaltenbach, et al. ∙

research

∙ 02/22/2023

Learning from Predictions: Fusing Training and Autoregressive Inference for Long-Term Spatiotemporal Forecasts

Recurrent Neural Networks (RNNs) have become an integral part of modelin...

0 Pantelis R. Vlachas, et al. ∙

research

∙ 05/10/2022

Optimizing a DIscrete Loss (ODIL) to solve forward and inverse problems for partial differential equations using machine learning tools

We introduce the Optimizing a Discrete Loss (ODIL) framework for the num...

0 Petr Karnakov, et al. ∙

research

∙ 03/24/2022

Remember and Forget Experience Replay for Multi-Agent Reinforcement Learning

We present the extension of the Remember and Forget for Experience Repla...

0 Pascal Weber, et al. ∙

research

∙ 06/21/2021

Scientific multi-agent reinforcement learning for wall-models of turbulent flows

The predictive capabilities of turbulent flow simulations, critical for ...

0 H. Jane Bae, et al. ∙

research

∙ 05/03/2021

Learning swimming escape patterns under energy constraints

Swimming organisms can escape their predators by creating and harnessing...

0 Ioannis Mandralis, et al. ∙

research

∙ 03/02/2021

Computing foaming flows across scales: from breaking waves to microfluidics

Crashing ocean waves, cappuccino froths and microfluidic bubble crystals...

0 Petr Karnakov, et al. ∙

research

∙ 02/21/2021

Learning Efficient Navigation in Vortical Flow Fields

Efficient point-to-point navigation in the presence of a background flow...

7 Peter Gunnarson, et al. ∙

research

∙ 02/17/2021

Accelerated Simulations of Molecular Systems through Learning of their Effective Dynamics

Simulations are vital for understanding and predicting the evolution of ...

0 Pantelis R. Vlachas, et al. ∙

research

∙ 08/24/2020

Improved Memories Learning

We propose Improved Memories Learning (IMeL), a novel algorithm that tur...

0 Francesco Varoli, et al. ∙

research

∙ 06/24/2020

Learning the Effective Dynamics of Complex Multiscale Systems

Simulations of complex multiscale systems are essential for science and ...

0 Pantelis R. Vlachas, et al. ∙

research

∙ 05/27/2020

Korali: a High-Performance Computing Framework for Stochastic Optimization and Bayesian Uncertainty Quantification

We present a modular, open-source, high-performance computing framework ...

0 Sergio M. Martin, et al. ∙

research

∙ 05/18/2020

Automating Turbulence Modeling by Multi-Agent Reinforcement Learning

The modeling of turbulent flows is critical to scientific and engineerin...

0 Guido Novati, et al. ∙

research

∙ 11/12/2019

Mirheo: High-Performance Mesoscale Simulations for Microfluidics

The transport and manipulation of particles and cells in microfluidic de...

0 Dmitry Alexeev, et al. ∙

research

∙ 10/22/2019

Optimal sensing for fish school identification

Fish schooling implies an awareness of the swimmers for their companions...

0 Pascal Weber, et al. ∙

research

∙ 10/09/2019

Forecasting of Spatio-temporal Chaotic Dynamics with Recurrent Neural Networks: a comparative study of Reservoir Computing and Backpropagation Algorithms

How effective are Recurrent Neural Networks (RNNs) in forecasting the sp...

0 Pantelis R. Vlachas, et al. ∙

research

∙ 05/27/2019

Machine Learning for Fluid Mechanics

The field of fluid mechanics is rapidly advancing, driven by unprecedent...

0 Steven Brunton, et al. ∙

research

∙ 05/01/2019

Bending models of lipid bilayer membranes: spontaneous curvature and area-difference elasticity

We preset a computational study of bending models for the curvature elas...

0 Xin Bian, et al. ∙

research

∙ 07/16/2018

Remember and Forget for Experience Replay

Experience replay (ER) is crucial for attaining high data-efficiency in ...

0 Guido Novati, et al. ∙

research

∙ 07/07/2018

Deep-Reinforcement-Learning for Gliding and Perching Bodies

Controlled gliding is one of the most energetically efficient modes of t...

0 Guido Novati, et al. ∙

research

∙ 07/02/2018

Personalized Radiotherapy Design for Glioblastoma: Integrating Mathematical Tumor Models, Multimodal Scans and Bayesian Inference

Glioblastoma is a highly invasive brain tumor, whose cells infiltrate su...

0 Jana Lipkova, et al. ∙

research

∙ 07/02/2018

Personalized Radiotherapy Design for Glioblastoma Using Mathematical Models, Multimodal Scans and Bayesian Inference

Glioblastoma is a highly invasive brain tumor, whose cells infiltrate su...

0 Jana Lipkova, et al. ∙

research

∙ 07/02/2018

Personalized Radiotherapy Planning for Glioma Using Multimodal Bayesian Model Calibration

Existing radiotherapy (RT) plans for brain tumors derive from population...

0 Jana Lipkova, et al. ∙

research

∙ 02/21/2018

Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long-Short Term Memory Networks

We introduce a data-driven forecasting method for high dimensional, chao...

0 Pantelis R. Vlachas, et al. ∙

research

∙ 02/07/2018

Efficient collective swimming by harnessing vortices through deep reinforcement learning

Fish in schooling formations navigate complex flow-fields replete with m...

0 Siddhartha Verma, et al. ∙

research

∙ 10/23/2017

Fully Context-Aware Video Prediction

This paper proposes a new neural network design for unsupervised learnin...

0 Wonmin Byeon, et al. ∙

research

∙ 10/22/2017

Bayesian uncertainty quantification for epidemic spread on networks

While there exist a number of mathematical approaches to modeling the sp...

0 Zhizhong Chen, et al. ∙

research

∙ 05/11/2017

Optimal fidelity multi-level Monte Carlo for quantification of uncertainty in simulations of cloud cavitation collapse

We quantify uncertainties in the location and magnitude of extreme press...

0 Jonas Šukys, et al. ∙

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