Title Information
Title
Data-Driven Parallel Scientific Computing: Multi-Fidelity Information Fusion Algorithms and Applications to Physical and Biological Systems
Name: Personal
Name Part
Perdikaris, Paris
Role
Role Term: Text
creator
Origin Information
Copyright Date
2015
Physical Description
Extent
xxi, 236 p.
digitalOrigin
born digital
Note
Thesis (Ph.D. -- Brown University (2015)
Name: Personal
Name Part
Karniadakis, George
Role
Role Term: Text
Director
Name: Personal
Name Part
Royset, Johannes
Role
Role Term: Text
Reader
Name: Personal
Name Part
Venturi, Daniele
Role
Role Term: Text
Reader
Name: Corporate
Name Part
Brown University. Applied Mathematics
Role
Role Term: Text
sponsor
Type of Resource
text
Genre (aat)
theses
Abstract
This thesis takes aim at two directions. The first direction involves setting the foundations for a new type of data-driven scientific computing, essentially creating a platform for blending multiple information sources of variable fidelity, e.g., experimental data, high-fidelity numerical simulations, expert opinion, etc., towards creating tractable paths to analyzing the response of complex physical and biological systems. Standing on the verge of traditional scientific computing and contemporary statistical learning techniques, we elaborate on a novel multi-fidelity information fusion framework that allows for the seamless integration of surrogate-based optimization and uncertainty quantification, and enables the development of efficient algorithms for data assimilation, design optimization, model inversion, and beyond. The second direction is focused on addressing open questions in the modeling of the human circulatory system, especially the interplay between blood flow and biomechanics in the brain Despite great growth in computing power and algorithmic sophistication, in-silico modeling of blood flow and arterial mechanics is still limited to truncated arterial domains, a fact that introduces the need for reduced-order models and parametric representations to account for the neglected mesoscale dynamics. The implicit need of such simplified representations gives rise to a series of open questions in cardiovascular mathematics, three of which will be at the focal point of our attention. To this end, we will introduce robust fractional-order constitutive laws for arterial biomechanics, we will address the closure problem for hemodynamic simulations in truncated arterial domains by coupling terminal outlet vessels to nonlinear 1D blood flow models in fractal arterial trees, and we will propose a model inversion technique via multi-fidelity surrogates that enables the efficient solution of parameter calibration problems.
Subject
Topic
Multi-fidelity modeling
Subject
Topic
High performance scientific computing
Subject
Topic
Design optimization
Subject
Topic
Uncertainty quantification
Subject
Topic
Arterial biomechanics
Subject
Topic
Inverse problems
Subject (FAST) (authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/1004795")
Topic
Machine learning
Subject (FAST) (authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/1892965")
Topic
Big data
Subject (FAST) (authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/834725")
Topic
Blood flow
Record Information
Record Content Source (marcorg)
RPB
Record Creation Date (encoding="iso8601")
20150601
Language
Language Term: Code (ISO639-2B)
eng
Language Term: Text
English
Identifier: DOI
10.7301/Z03N21S1