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Data-Driven Computing in Dynamics

Kirchdoerfer, T. and Ortiz, M. (2018) Data-Driven Computing in Dynamics. International Journal for Numerical Methods in Engineering, 113 (11). pp. 1697-1710. ISSN 0029-5981. doi:10.1002/nme.5716.

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We formulate extensions to Data Driven Computing for both distance minimizing and entropy maximizing schemes to incorporate time integration. Previous works focused on formulating both types of solvers in the presence of static equilibrium constraints. Here formulations assign data points a variable relevance depending on distance to the solution and on maximum-entropy weighting, with distance minimizing schemes discussed as a special case. The resulting schemes consist of the minimization of a suitably-defined free energy over phase space subject to compatibility and a time-discretized momentum conservation constraint. We present selected numerical tests that establish the convergence properties of both types of Data Driven solvers and solutions.

Item Type:Article
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URLURL TypeDescription Paper
Ortiz, M.0000-0001-5877-4824
Additional Information:© 2017 John Wiley & Sons, Inc. Accepted manuscript online: 31 October 2017; Manuscript Revised: 11 October 2017; Manuscript Accepted: 11 October 2017; Manuscript Received: 12 June 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.
Funding AgencyGrant Number
Air Force Office of Scientific Research (AFOSR)FA9550-12-1-0091
Subject Keywords:Data Science, dynamics, noisy data, model-free computing
Issue or Number:11
Record Number:CaltechAUTHORS:20171101-121627090
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Official Citation:Kirchdoerfer T, Ortiz M. Data-driven computing in dynamics. Int J Numer Meth Engng. 2018;113:1697–1710.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:82834
Deposited By: Tony Diaz
Deposited On:01 Nov 2017 19:37
Last Modified:15 Nov 2021 19:53

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