Abstract

The context of this paper is the simulation of parameter-dependent partial differential equations (PDEs). When the aim is to solve such PDEs for a large number of parameter values, Reduced Basis Methods (RBM) are often used to reduce computational costs of a classical high fidelity code based on Finite Element Method (FEM), Finite Volume (FVM) or Spectral methods. The efficient implementation of most of these RBM requires to modify this high fidelity code, which cannot be done, for example in an industrial context if the high fidelity code is only accessible as a "black-box" solver. The Non-Intrusive Reduced Basis (NIRB) method has been introduced in the context of finite elements as a good alternative to reduce the implementation costs of these parameter-dependent problems. The method is efficient in other contexts than the FEM one, like with finite volume schemes, which are more often used in an industrial environment. In this case, some adaptations need to be done as the degrees of freedom in FV methods have different meanings. At this time, error estimates have only been studied with FEM solvers. In this paper, we present a generalisation of the NIRB method to Finite Volume schemes and we show that estimates established for FEM solvers also hold in the FVM setting. We first prove our results for the hybrid-Mimetic Finite Difference method (hMFD), which is part the Hybrid Mixed Mimetic methods (HMM) family. Then, we explain how these results apply more generally to other FV schemes. Some of them are specified, such as the Two Point Flux Approximation (TPFA).

Highlights

  • This paper is concerned with the efficient simulation of parameter-dependent partial differential equations (PDEs), with a parameter varying in a given set G

  • The aim of this paper is to propose the adaptation of the Non-Intrusive Reduced Basis (NIRB) method to FV and to propose the numerical analysis able to recover the classical error estimate with Finite Volume (FV) schemes

  • We introduce the definition of Gradient Discretisation (GD) for Dirichlet boundary conditions (BCs) as in [19] and the GD scheme associated to our model problem

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Summary

Introduction

This paper is concerned with the efficient simulation of parameter-dependent partial differential equations (PDEs), with a parameter varying in a given set G. It may happen, for instance in the context of parameter optimization or real time simulations in an industrial context, that the same problem needs to be solved for several parameter values. For instance in the context of parameter optimization or real time simulations in an industrial context, that the same problem needs to be solved for several parameter values In such cases, different model order reductions (MOR) like the reduced basis methods have been proposed [22, 27]) based on Proper Orthogonal Decomposition (POD) or greedy selection of the reduced basis, the reduced basis elements being computed accurately enough through a high fidelity code In these approaches, the efficient implementation of the reduced method, leading to reductions in the computational time, requires to.

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