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Erschienen in:
Comparison of Phase-Field Models of Fracture Coupled with Plasticity Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Data-Driven Computing

verfasst von : Trenton Kirchdoerfer, Michael Ortiz

Erschienen in: Advances in Computational Plasticity

Verlag: 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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Vorheriges Kapitel Performance Comparison of Nodally Integrated Galerkin Meshfree Methods and Nodally Collocated Strong Form Meshfree Methods
Nächstes Kapitel Elasto-Plastic Response of Thick Plates Built in Functionally Graded Material Using the Third Order Plate Theory
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Metadaten
Titel
Data-Driven Computing
verfasst von
Trenton Kirchdoerfer
Michael Ortiz
Copyright-Jahr
2018
Buch
Advances in Computational Plasticity

Print ISBN: 978-3-319-60884-6

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

Copyright-Jahr: 2018

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

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