Zobrazeno 1 - 10
of 181
pro vyhledávání: '"Litvinov, Sergey A."'
Autor:
Alexeev, Dmitry, Litvinov, Sergey, Economides, Athena, Amoudruz, Lucas, Toner, Mehmet, Koumoutsakos, Petros
The identification of cells and particles based on their transport properties in microfluidic devices is crucial for numerous applications in biology and medicine. Neutrally buoyant particles transported in microfluidic channels, migrate laterally to
Externí odkaz:
http://arxiv.org/abs/2408.09552
Biomedical applications such as targeted drug delivery, microsurgery or sensing rely on reaching precise areas within the body in a minimally invasive way. Artificial bacterial flagella (ABFs) have emerged as potential tools for this task by navigati
Externí odkaz:
http://arxiv.org/abs/2404.02171
Autor:
Weidner, Jonas, Ezhov, Ivan, Balcerak, Michal, Metz, Marie-Christin, Litvinov, Sergey, Kaltenbach, Sebastian, Feiner, Leonhard, Lux, Laurin, Kofler, Florian, Lipkova, Jana, Latz, Jonas, Rueckert, Daniel, Menze, Bjoern, Wiestler, Benedikt
Biophysical modeling, particularly involving partial differential equations (PDEs), offers significant potential for tailoring disease treatment protocols to individual patients. However, the inverse problem-solving aspect of these models presents a
Externí odkaz:
http://arxiv.org/abs/2403.04500
Autor:
Balcerak, Michal, Weidner, Jonas, Karnakov, Petr, Ezhov, Ivan, Litvinov, Sergey, Koumoutsakos, Petros, Zhang, Ray Zirui, Lowengrub, John S., Wiestler, Bene, Menze, Bjoern
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-specific progressions is essential for effective therapies. Current tre
Externí odkaz:
http://arxiv.org/abs/2312.05063
Publikováno v:
Eur. Phys. J. E 46, 59 (2023)
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 that can accommodate data and regularization terms. We introduce a mul
Externí odkaz:
http://arxiv.org/abs/2303.04679
Publikováno v:
PNAS Nexus, Volume 3, Issue 1, January 2024, pgae005
We introduce the Optimizing a Discrete Loss (ODIL) framework for the numerical solution of Partial Differential Equations (PDE) using machine learning tools. The framework formulates numerical methods as a minimization of discrete residuals that are
Externí odkaz:
http://arxiv.org/abs/2205.04611
Crashing ocean waves, cappuccino froths and microfluidic bubble crystals are examples of foamy flows. Foamy flows are critical in numerous natural and industrial processes and remain notoriously difficult to compute as they involve coupled, multiscal
Externí odkaz:
http://arxiv.org/abs/2103.01513
The transport and manipulation of particles and cells in microfluidic devices has become a core methodology in domains ranging from molecular biology to manufacturing and drug design. The rational design and operation of such devices can benefit from
Externí odkaz:
http://arxiv.org/abs/1911.04712
We present a particle method for estimating the curvature of interfaces in volume-of-fluid simulations of multiphase flows. The method is well suited for under-resolved interfaces, and it is shown to be more accurate than the parabolic fitting that i
Externí odkaz:
http://arxiv.org/abs/1906.00314
We preset a computational study of bending models for the curvature elasticity of lipid bilayer membranes that are relevant for simulations of vesicles and red blood cells. We compute bending energy and forces on triangulated meshes and evaluate and
Externí odkaz:
http://arxiv.org/abs/1905.00240