Multiscale modelling and inverse problems

Published

Conference Paper

© Springer-Verlag Berlin Heidelberg 2012. The need to blend observational data and mathematical models arises in many applications and leads naturally to inverse problems. Parameters appearing in the model, such as constitutive tensors, initial conditions, boundary conditions, and forcing can be estimated on the basis of observed data. The resulting inverse problems are usually ill-posed and some form of regularization is required. These notes discuss parameter estimation in situations where the unknown parameters vary across multiple scales. We illustrate the main ideas using a simple model for groundwater flow. We will highlight various approaches to regularization for inverse problems, including Tikhonov and Bayesian methods.We illustrate three ideas that arise when considering inverse problems in the multiscale context. The first idea is that the choice of space or set in which to seek the solution to the inverse problem is intimately related to whether a homogenized or full multiscale solution is required. This is a choice of regularization. The second idea is that, if a homogenized solution to the inverse problem is what is desired, then this can be recovered from carefully designed observations of the full multiscale system. The third idea is that the theory of homogenization can be used to improve the estimation of homogenized coefficients from multiscale data.

Full Text

Duke Authors

Cited Authors

  • Nolen, J; Pavliotis, GA; Stuart, AM

Published Date

  • January 1, 2012

Published In

Volume / Issue

  • 83 /

Start / End Page

  • 1 - 34

International Standard Serial Number (ISSN)

  • 1439-7358

International Standard Book Number 13 (ISBN-13)

  • 9783642220609

Digital Object Identifier (DOI)

  • 10.1007/978-3-642-22061-6_1

Citation Source

  • Scopus