7 papers
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…
Optimal navigation of magnetic artificial microswimmers in blood capillaries with deep reinforcement learning
Lucas Amoudruz, Sergey Litvinov, Petros Koumoutsakos
Biomedical applications such as targeted drug delivery, microsurgery, and sensing rely on reaching precise areas within the body in a minimally invasive way. Artificial bacterial f…
Closure Discovery for Coarse-Grained Partial Differential Equations Using Grid-based Reinforcement Learning
Jan-Philipp von Bassewitz, Sebastian Kaltenbach, Petros Koumoutsakos
Reliable predictions of critical phenomena, such as weather, wildfires and epidemics often rely on models described by Partial Differential Equations (PDEs). However, simulations t…
Deconstructing Recurrence, Attention, and Gating: Investigating the transferability of Transformers and Gated Recurrent Neural Networks in forecasting of dynamical systems
Hunter S. Heidenreich, Pantelis R. Vlachas, Petros Koumoutsakos
Machine learning architectures, including transformers and recurrent neural networks (RNNs) have revolutionized forecasting in applications ranging from text processing to extreme…
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…
Generative Learning of the Solution of Parametric Partial Differential Equations Using Guided Diffusion Models and Virtual Observations
Han Gao, Sebastian Kaltenbach, Petros Koumoutsakos
We introduce a generative learning framework to model high-dimensional parametric systems using gradient guidance and virtual observations. We consider systems described by Partial…