5 citations · 11 across the 8 of their papers we have counts for
9 papers
Prediction of Extreme Events in Multiscale Simulations of Geophysical Turbulence using Reinforcement Learning
Yifei Guan, Lucas Amoudruz, Sergey Litvinov +4
Accurate subgrid-scale closures are essential for weather/climate models, where predicting extreme events is critical. Traditional closures have structural errors, e.g., producing…
Scalable, Cloud-Based Simulations of Blood Flow and Targeted Drug Delivery in Retinal Capillaries
Lucas Amoudruz, Sergey Litvinov, Riccardo Murri +4
We investigate the capabilities of cloud computing for large-scale,tightly-coupled simulations of biological fluids in complex geometries, traditionally performed in supercomputing…
Bayesian Inference for PDE-based Inverse Problems using the Optimization of a Discrete Loss
Lucas Amoudruz, Sergey Litvinov, Costas Papadimitriou +1
Inverse problems are crucial for many applications in science, engineering and medicine that involve data assimilation, design, and imaging. Their solution infers the parameters or…
Data-Driven Discovery of Interpretable Kalman Filter Variants through Large Language Models and Genetic Programming
Vasileios Saketos, Sebastian Kaltenbach, Sergey Litvinov +1
Algorithmic discovery has traditionally relied on human ingenuity and extensive experimentation. Here we investigate whether a prominent scientific computing algorithm, the Kalman…
Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement Learning
Paul Fischer, Sebastian Kaltenbach, Sergey Litvinov +2
The Lattice Boltzmann method (LBM) offers a powerful and versatile approach to simulating diverse hydrodynamic phenomena, spanning microfluidics to aerodynamics. The vast range of…
Inertial Focusing of Spherical Particles: The Effects of Rotational Motion
Dmitry Alexeev, Sergey Litvinov, Athena Economides +3
The identification of cells and particles based on their transport properties in microfluidic devices is crucial for numerous applications in biology and medicine. Neutrally buoyan…
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…
A Learnable Prior Improves Inverse Tumor Growth Modeling
Jonas Weidner, Ivan Ezhov, Michal Balcerak +11
Biophysical modeling, particularly involving partial differential equations (PDEs), offers significant potential for tailoring disease treatment protocols to individual patients. H…
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…