From the 1 of 14 linked papers with an AI index.
14 papers
Tensor-Based Reduced-Order Modeling for Optimization-Based Inverse Problems
Sahidul Islam, Andreas Mang, Maxim Olshanskii
The paper introduces a tensor-train based reduced-order modeling framework that directly approximates the parameter-to-observation map for optimization-based inverse problems, enab…
Parametric Reduced Order Models for the Generalized Kuramoto--Sivashinsky Equations
Md Rezwan Bin Mizan, Maxim Olshanskii, Ilya Timofeyev
The paper studies parametric Reduced Order Models (ROMs) for the Kuramoto--Sivashinsky (KS) and generalized Kuramoto--Sivashinsky (gKS) equations. We consider several POD and POD-D…
Reduced-Order Modeling of Parameterized Visco-Plastic Shallow Flows
Md Rezwan Bin Mizan, Ilya Timofeyev, Maxim Olshanskii
We propose a non-intrusive reduced-order modeling framework for parametrized visco-plastic free-surface flows governed by a shallow-water formulation of Herschel--Bulkley fluids. T…
Tensorial Reduced-Order Models for Parametric Coupled Reaction-Diffusion Systems: Application to Brain Tumor Growth Modeling
Asikul Islam, Md Rezwan Bin Mizan, Maxim Olshanskii +1
We construct efficient surrogate models for parametric forward operators arising in brain tumor growth simulations, governed by coupled semilinear parabolic reaction-diffusion syst…
A modified Brinkman penalization fictitious domain method for the unsteady Navier-Stokes equations
Zhanybek Baitulenov, Maxim Olshanskii, Almas Temirbekov +2
This paper investigates a modification of the fictitious domain method with continuation in the lower-order coefficients for the unsteady Navier-Stokes equations governing the moti…
An unfitted divergence-free higher order finite element method for the Stokes problem
Michael Neilan, Maxim Olshanskii, Henry von Wahl
The paper develops and analyzes a higher-order unfitted finite element method for the incompressible Stokes equations, which yields a strongly divergence-free velocity field up to…