Citation
Kirchdoerfer, Trenton Thomas (2018) Data Driven Computing. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/Z9Z899MV. https://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.)) | ||||||
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| 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) | ||||||
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| Group: | GALCIT | ||||||
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| Defense Date: | 12 July 2017 | ||||||
| Non-Caltech Author Email: | imtrenton (AT) yahoo.com | ||||||
| Record Number: | CaltechTHESIS:09122017-092017294 | ||||||
| Persistent URL: | https://resolver.caltech.edu/CaltechTHESIS:09122017-092017294 | ||||||
| DOI: | 10.7907/Z9Z899MV | ||||||
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| 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: | 28 Oct 2021 22:48 |
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