Dissertation/Thesis Abstract

Seem: A Kernel-Based Fictitious Domain Method
by Agress, Daniel Joseph, Ph.D., University of California, Irvine, 2020, 132; 28089864
Abstract (Summary)

This thesis presents SEEM (Smooth Extension Embedding Method), a novel approach to the solution of boundary value problems within the framework of the fictitious domain method philosophy. The salient feature of the novel method is that it reduces the whole boundary value problem to a linear constraint for an appropriate optimization problem formulated in a larger, simpler set which contains the domain on which the boundary value problem is posed and which allows for the use of straightforward discretizations. It can also be viewed as a fully discrete meshfree method which uses a novel class of basis functions, thus building a bridge between fictitious domain and meshfree methods.

SEEM in essence computes a (discrete) extension of the solution to the boundary value problem by selecting it as a smooth element of the complete affine family of solutions of the original equations, which now yield an underdetermined problem for an unknown defined in the whole fictitious domain. The actual regularity of this extension is determined by that of the analytic solution and by the choice of objective functional. Numerical experiments are presented which demonstrate that the method can be stably used to efficiently solve boundary value problems on general geometries, and that it produces solutions of tunable (and high) accuracy. Divergence-free and time-dependent problems are considered as well.

Indexing (document details)
Advisor: Guidotti, Patrick Q.
Commitee: Chen, Long, Lowengrub, John S.
School: University of California, Irvine
Department: Mathematics - Ph.D.
School Location: United States -- California
Source: DAI-B 82/6(E), Dissertation Abstracts International
Subjects: Applied Mathematics
Keywords: Fictitious domain, Immersed boundary, Meshfree collocation, Numerical PDEs, Spectral methods
Publication Number: 28089864
ISBN: 9798557020770
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