Kirchdoerfer, T. and Ortiz, M. (2017) Data Driven Computing with Noisy Material Data Sets. Computer Methods in Applied Mechanics and Engineering, 326 . pp. 622-641. ISSN 0045-7825. doi:10.1016/j.cma.2017.07.039. https://resolver.caltech.edu/CaltechAUTHORS:20170612-102809775
![]() |
PDF
- Accepted Version
See Usage Policy. 1MB |
![]() |
PDF
- Submitted Version
See Usage Policy. 1MB |
Use this Persistent URL to link to this item: https://resolver.caltech.edu/CaltechAUTHORS:20170612-102809775
Abstract
We formulate a Data Driven Computing paradigm, termed max-ent Data Driven Computing, that generalizes distance-minimizing Data Driven Computing and is robust with respect to outliers. Robustness is achieved by means of clustering analysis. Specifically, we assign data points a variable relevance depending on distance to the solution and on maximum-entropy estimation. The resulting scheme consists of the minimization of a suitably-defined free energy over phase space subject to compatibility and equilibrium constraints. Distance-minimizing Data Driven schemes are recovered in the limit of zero temperature. We present selected numerical tests that establish the convergence properties of the max-ent Data Driven solvers and solutions.
Item Type: | Article | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Related URLs: |
| ||||||||||||
ORCID: |
| ||||||||||||
Additional Information: | © 2017 Elsevier B.V. Received 3 March 2017, Revised 17 May 2017, Accepted 27 July 2017, Available online 24 August 2017. The support of Caltech’s Center of Excellence on High-Rate Deformation Physics of Heterogeneous Materials, AFOSR Award FA9550-12-1-0091, is gratefully acknowledged. We gratefully acknowledge helpful discussions with H. Owhadi and T. J. Sullivan. | ||||||||||||
Group: | GALCIT | ||||||||||||
Funders: |
| ||||||||||||
Subject Keywords: | Data science; Big data; Approximation theory; Scientific computing | ||||||||||||
DOI: | 10.1016/j.cma.2017.07.039 | ||||||||||||
Record Number: | CaltechAUTHORS:20170612-102809775 | ||||||||||||
Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20170612-102809775 | ||||||||||||
Official Citation: | T. Kirchdoerfer, M. Ortiz, Data Driven Computing with noisy material data sets, In Computer Methods in Applied Mechanics and Engineering, Volume 326, 2017, Pages 622-641, ISSN 0045-7825, https://doi.org/10.1016/j.cma.2017.07.039. (http://www.sciencedirect.com/science/article/pii/S0045782517304012) | ||||||||||||
Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||||||||
ID Code: | 78100 | ||||||||||||
Collection: | CaltechAUTHORS | ||||||||||||
Deposited By: | Tony Diaz | ||||||||||||
Deposited On: | 12 Jun 2017 18:26 | ||||||||||||
Last Modified: | 15 Nov 2021 17:36 |
Repository Staff Only: item control page