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相关论文: A Reduced basis stabilization for the unsteady Sto…

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In this work, we propose viable and efficient strategies for stabilized parametrized advection dominated problems, with random inputs. In particular, we investigate the combination of wRB (weighted reduced basis) method for stochastic…

数值分析 · 数学 2018-11-05 Davide Torlo , Francesco Ballarin , Gianluigi Rozza

This work focuses on steady and unsteady Navier-Stokes equations in a reduced order modeling framework. The methodology proposed is based on a Proper Orthogonal Decomposition within a levelset geometry description and the problems of…

数值分析 · 数学 2023-08-08 Efthymios N. Karatzas , Monica Nonino , Francesco Ballarin , Gianluigi Rozza

This work proposes novel techniques for the efficient numerical simulation of parameterized, unsteady partial differential equations. Projection-based reduced order models (ROMs) such as the reduced basis method employ a (Petrov-)Galerkin…

数值分析 · 数学 2023-12-05 Nicholas Mueller , Santiago Badia

In this paper, we develop and analyze a novel numerical scheme for the steady incompressible Navier-Stokes equations by the weak Galerkin methods. The divergence-preserving velocity reconstruction operator is employed in the discretization…

数值分析 · 数学 2020-11-24 Lin Mu

The present work focuses on the geometric parametrization and the reduced order modeling of the Stokes equation. We discuss the concept of a parametrized geometry and its application within a reduced order modeling technique. The full order…

数值分析 · 数学 2021-06-01 Nirav Vasant Shah , Martin Hess , Gianluigi Rozza

A priori analysis for a generalized local projection stabilized conforming finite element approximation of Darcy flow and Stokes problems is presented in this paper. A first-order conforming P1 finite element space is used to approximate…

数值分析 · 数学 2020-09-02 Deepika Garg , Sashikumaar Ganesan

In this work we consider a discontinuous Galerkin method for the discretization of the Stokes problem. We use $H(\textrm{div})$-conforming finite elements as they provide major benefits such as exact mass conservation and…

数值分析 · 数学 2016-12-06 Philip L. Lederer , Joachim Schöberl

This work proposes a method for model reduction of finite-volume models that guarantees the resulting reduced-order model is conservative, thereby preserving the structure intrinsic to finite-volume discretizations. The proposed…

数值分析 · 计算机科学 2018-07-04 Kevin Carlberg , Youngsoo Choi , Syuzanna Sargsyan

We present a Reduced Order Model (ROM) which exploits recent developments in Physics Informed Neural Networks (PINNs) for solving inverse problems for the Navier--Stokes equations (NSE). In the proposed approach, the presence of simulated…

流体动力学 · 物理学 2022-09-08 Saddam Hijazi , Melina Freitag , Niels Landwehr

We study the steady-state Navier-Stokes equations in the context of stochastic finite element discretizations. Specifically, we assume that the viscosity is a random field given in the form of a generalized polynomial chaos expansion. For…

数值分析 · 数学 2016-04-26 Bedřich Sousedík , Howard C. Elman

We propose a projection-based model order reduction method for the solution of parameter-dependent dynamical systems. The proposed method relies on the construction of time-dependent reduced spaces generated from evaluations of the solution…

数值分析 · 数学 2019-09-11 Marie Billaud-Friess , Anthony Nouy

Accurate and inexpensive Reduced Order Models (ROMs) for forecasting turbulent flows can facilitate rapid design iterations and thus prove critical for predictive control in engineering problems. Galerkin projection based Reduced Order…

流体动力学 · 物理学 2023-01-27 Surya Chakrabarti , Arvind T. Mohan , Datta V. Gaitonde , Daniel Livescu

In this work, we apply the space-time Galerkin reduced basis (ST-GRB) method to a reduced fluid-structure interaction model, for the numerical simulation of hemodynamics in arteries. In essence, ST-GRB extends the classical reduced basis…

数值分析 · 数学 2025-08-05 Riccardo Tenderini , Simone Deparis

We present a new stability and error analysis of fully discrete approximation schemes for the transient Stokes equation. For the spatial discretization, we consider a wide class of Galerkin finite element methods which includes both inf-sup…

数值分析 · 数学 2023-12-12 Alessandro Contri , Balázs Kovács , André Massing

Proper orthogonal decomposition (POD) stabilized methods for the Navier-Stokes equations are considered and analyzed. We consider two cases, the case in which the snapshots are based on a non inf-sup stable method and the case in which the…

数值分析 · 数学 2020-06-02 Julia Novo , Samuele Rubino

Numerical simulations are a valuable research and layout tool for fluid flow problems, yet repeated evaluations of parametrized problems, necessary to solve optimization problems, can be very costly. One option to speed up this process is…

流体动力学 · 物理学 2025-02-28 Marian Staggl , Wolfgang Sanz , Paul Pieringer

In this paper we consider fully discrete approximations with inf-sup stable mixed finite element methods in space to approximate the Navier-Stokes equations. A continuous downscaling data assimilation algorithm is analyzed in which…

数值分析 · 数学 2019-04-15 Bosco García-Archilla , Julia Novo

Jump penalty stabilisation techniques have been recently proposed for continuous and discontinuous high order Galerkin schemes [1,2,3]. The stabilisation relies on the gradient or solution discontinuity at element interfaces to incorporate…

流体动力学 · 物理学 2022-08-25 Jiaqing Kou , Oscar A. Marino , Esteban Ferrer

In this work, we analyze an unfitted discontinuous Galerkin discretization for the numerical solution of the Stokes system based on equal higher-order discontinuous velocities and pressures. This approach combines the best from both worlds,…

数值分析 · 数学 2022-04-06 Aikaterini Aretaki , Efthymios N. Karatzas , Georgios Katsouleas

In this paper we analyze a finite element method applied to a continuous downscaling data assimilation algorithm for the numerical approximation of the two and three dimensional Navier-Stokes equations corresponding to given measurements on…

数值分析 · 数学 2019-03-05 García-Archilla , Julia Novo , Edriss S. Titi