CaltechAUTHORS
  A Caltech Library Service

Data-Driven Computing

Kirchdoerfer, Trenton and Ortiz, Michael (2017) Data-Driven Computing. In: Advances in Computational Plasticity: A Book in Honour of D. Roger J. Owen. Computational Methods in Applied Sciences. No.46. Springer , Cham, Switzerland, pp. 165-183. ISBN 978-3-319-60884-6. http://resolver.caltech.edu/CaltechAUTHORS:20170912-142917371

Full text is not posted in this repository. Consult Related URLs below.

Use this Persistent URL to link to this item: http://resolver.caltech.edu/CaltechAUTHORS:20170912-142917371

Abstract

Data-Driven Computing is a new field of computational analysis which uses provided data to directly produce predictive outcomes. Recent works in this developing field have established important properties of Data-Driven solvers, accommodated noisy data sets and demonstrated both quasi-static and dynamic solutions within mechanics. This work reviews this initial progress and advances some of the many possible improvements and applications that might best advance the field. Possible method improvements discuss incorporation of data quality metrics, and adaptive data additions while new applications focus on multi-scale analysis and the need for public databases to support constitutive data collaboration.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1007/978-3-319-60885-3_8DOIArticle
https://link.springer.com/chapter/10.1007%2F978-3-319-60885-3_8PublisherArticle
Additional Information:© 2018 Springer International Publishing AG. First Online: 10 September 2017.
Group:GALCIT
Record Number:CaltechAUTHORS:20170912-142917371
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20170912-142917371
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:81377
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:12 Sep 2017 22:15
Last Modified:29 May 2018 16:31

Repository Staff Only: item control page