An adaptive algorithm for an elliptic optimization problem, and stochastic-deterministic coupling: a mathematical framework
| dc.contributor.author | Lee, Sheldon, author | |
| dc.contributor.author | Estep, Donald, advisor | |
| dc.contributor.author | Tavener, Simon, advisor | |
| dc.date.accessioned | 2024-03-13T19:53:57Z | |
| dc.date.available | 2024-03-13T19:53:57Z | |
| dc.date.issued | 2008 | |
| dc.description.abstract | This dissertation consists of two parts. In the first part, we study optimization of a quantity of interest of a solution of an elliptic problem, with respect to parameters in the data using a gradient search algorithm. We use the generalized Green's function as an efficient way to compute the gradient. We analyze the effect of numerical error on a gradient search, and develop an efficient way to control these errors using a posteriori error analysis. Specifically, we devise an adaptive algorithm to refine and unrefine the finite element mesh at each step in the descent search algorithm. We give basic examples and apply this technique to a model of a healing wound. In the second part, we construct a mathematical framework for coupling atomistic models with continuum models. We first study the case of coupling two deterministic diffusive regions with a common interface. We construct a fixed point map by repeatedly solving the problems, while passing the flux in one direction and the concentration in the other direction. We examine criteria for the fixed point iteration to converge, and offer remedies such as reversing the direction of the coupling, or relaxation, for the case it does not. We then study the one dimensional case where the particles undergo a random walk on a lattice, next to a continuum region. As the atomistic region is random, this technique yields a fixed point iteration of distributions. We run numerical tests to study the long term behavior of such an iteration, and compare the results with the deterministic case. We also discuss a probability transition matrix approach, in which we assume that the boundary conditions at each iterations follow a Markov chain. | |
| dc.format.medium | born digital | |
| dc.format.medium | doctoral dissertations | |
| dc.identifier | ETDF_Lee_2008_3332756.pdf | |
| dc.identifier.uri | https://hdl.handle.net/10217/237835 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2000-2019 | |
| dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
| dc.rights.license | Per the terms of a contractual agreement, all use of this item is limited to the non-commercial use of Colorado State University and its authorized users. | |
| dc.subject | elliptic problems | |
| dc.subject | stochastic-deterministic coupling | |
| dc.subject | mathematics | |
| dc.title | An adaptive algorithm for an elliptic optimization problem, and stochastic-deterministic coupling: a mathematical framework | |
| dc.type | Text | |
| dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
| thesis.degree.discipline | Mathematics | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy (Ph.D.) |
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