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Data-Driven Computing

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Advances in Computational Plasticity

Part of the book series: Computational Methods in Applied Sciences ((COMPUTMETHODS,volume 46))

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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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  1. California Institute of Technology, 1200 E California Blvd, Pasadena, CA, 91125, USA

    Trenton Kirchdoerfer & Michael Ortiz

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  1. Trenton Kirchdoerfer
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  2. Michael Ortiz
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Correspondence to Michael Ortiz .

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Editors and Affiliations

  1. CIMNE, Universitat Politècnica de Catalunya, Barcelona, Spain

    Eugenio Oñate

  2. Swansea University, Swansea, United Kingdom

    Djordje Peric

  3. Swansea University, Swansea, United Kingdom

    Eduardo de Souza Neto

  4. CIMNE, Universitat Politècnica de Catalunya, Barcelona, Spain

    Michele Chiumenti

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Kirchdoerfer, T., Ortiz, M. (2018). Data-Driven Computing. In: Oñate, E., Peric, D., de Souza Neto, E., Chiumenti, M. (eds) Advances in Computational Plasticity. Computational Methods in Applied Sciences, vol 46. Springer, Cham. https://doi.org/10.1007/978-3-319-60885-3_8

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

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