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Data Driven Computing

Citation

Kirchdoerfer, Trenton T. (2018) Data Driven Computing. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/Z9Z899MV. http://resolver.caltech.edu/CaltechTHESIS:09122017-092017294

Abstract

Data Driven Computing is a new field of computational analysis which uses provided data to directly produce predictive outcomes. This thesis first establishes definitions of Data-Driven solvers and working examples of static mechanics problems to demonstrate efficacy. Significant extensions are then explored to both accommodate noisy data sets and apply the deveoloped methods to dynamic problems within mechanics. Possible method improvements discuss incorporation of data quality metrics and adaptive data sampling, while new applications focus on multi-scale analysis and the need for public databases to support constitutive data collaboration.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:Data Driven Computing, big data, data science, constitutive modeling
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Aeronautics
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Ortiz, Michael
Thesis Committee:
  • Lapusta, Nadia (chair)
  • Asimaki, Domniki
  • Kochmann, Dennis M.
  • Ortiz, Michael
Defense Date:12 July 2017
Non-Caltech Author Email:imtrenton (AT) yahoo.com
Record Number:CaltechTHESIS:09122017-092017294
Persistent URL:http://resolver.caltech.edu/CaltechTHESIS:09122017-092017294
DOI:10.7907/Z9Z899MV
Related URLs:
URLURL TypeDescription
https://doi.org/10.1016/j.cma.2016.02.001DOIArticle adapted for Chapter 2
ORCID:
AuthorORCID
Kirchdoerfer, Trenton T.0000-0003-2290-1857
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:10431
Collection:CaltechTHESIS
Deposited By: Trenton Kirchdoerfer
Deposited On:04 Oct 2017 19:40
Last Modified:11 Oct 2017 15:53

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