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Individualizing Glioma Radiotherapy Planning by Optimization of Data and Physics-Informed Discrete Loss
Michal Balcerak, Jonas Weidner, Petr Karnakov +8
Brain tumor growth is unique to each glioma patient and extends beyond what is visible in imaging scans, infiltrating surrounding brain tissue. Understanding these hidden patient-s…
Extreme Event Prediction with Multi-agent Reinforcement Learning-based Parametrization of Atmospheric and Oceanic Turbulence
Rambod Mojgani, Daniel Waelchli, Yifei Guan +2
Global climate models (GCMs) are the main tools for understanding and predicting climate change. However, due to limited numerical resolutions, these models suffer from major struc…
Interpretable learning of effective dynamics for multiscale systems
Emmanuel Menier, Sebastian Kaltenbach, Mouadh Yagoubi +2
The modeling and simulation of high-dimensional multiscale systems is a critical challenge across all areas of science and engineering. It is broadly believed that even with today'…
Drag Reduction in Flows Past 2D and 3D Circular Cylinders Through Deep Reinforcement Learning
Michail Chatzimanolakis, Pascal Weber, Petros Koumoutsakos
We investigate drag reduction mechanisms in flows past two- and three-dimensional cylinders controlled by surface actuators using deep reinforcement learning. We investigate 2D and…
An interpretable wildfire spreading model for real-time predictions
Konstantinos Vogiatzoglou, Costas Papadimitriou, Konstantinos Ampountolas +3
Forest fires pose a natural threat with devastating social, environmental, and economic implications. The rapid and highly uncertain rate of spread of wildfires necessitates a trus…
Discovering Individual Rewards in Collective Behavior through Inverse Multi-Agent Reinforcement Learning
Daniel Waelchli, Pascal Weber, Petros Koumoutsakos
The discovery of individual objectives in collective behavior of complex dynamical systems such as fish schools and bacteria colonies is a long-standing challenge. Inverse reinforc…
The Volume of Healthy Red Blood Cells is Optimal for Advective Oxygen Transport in Arterioles
Lucas Amoudruz, Athena Economides, Petros Koumoutsakos
Red blood cells (RBCs) are vital for transporting oxygen from the lungs to the body's tissues through the intricate circulatory system. They achieve this by binding and releasing o…
Adaptive learning of effective dynamics: Adaptive real-time, online modeling for complex systems
Ivica Kičić, Pantelis R. Vlachas, Georgios Arampatzis +3
Predictive simulations are essential for applications ranging from weather forecasting to material design. The veracity of these simulations hinges on their capacity to capture the…
The stress-free state of human erythrocytes: data driven inference of a transferable RBC model
Lucas Amoudruz, Athena Economides, Georgios Arampatzis +1
The stress-free state (SFS) of red blood cells (RBCs) is a fundamental reference configuration for the calibration of computational models, yet it remains unknown. Current experime…
Interpretable reduced-order modeling with time-scale separation
Sebastian Kaltenbach, Phaedon-Stelios Koutsourelakis, Petros Koumoutsakos
Partial Differential Equations (PDEs) with high dimensionality are commonly encountered in computational physics and engineering. However, finding solutions for these PDEs can be c…
Flow reconstruction by multiresolution optimization of a discrete loss with automatic differentiation
Petr Karnakov, Sergey Litvinov, Petros Koumoutsakos
We present a potent computational method for the solution of inverse problems in fluid mechanics. We consider inverse problems formulated in terms of a deterministic loss function…
Learning from Predictions: Fusing Training and Autoregressive Inference for Long-Term Spatiotemporal Forecasts
Pantelis R. Vlachas, Petros Koumoutsakos
Recurrent Neural Networks (RNNs) have become an integral part of modeling and forecasting frameworks in areas like natural language processing and high-dimensional dynamical system…