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2018 | OriginalPaper | Chapter

Data-Driven Computing

Authors : Trenton Kirchdoerfer, Michael Ortiz

Published in: Advances in Computational Plasticity

Publisher: Springer International Publishing

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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.

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Title: Data-Driven Computing
Authors: Trenton Kirchdoerfer
Michael Ortiz
Publisher: Springer International Publishing
Book: Advances in Computational Plasticity
Print ISBN: 978-3-319-60884-6

Electronic ISBN: 978-3-319-60885-3

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-60885-3_8

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