Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published November 1, 2017 | public
Journal Article Open

Data Driven Computing with Noisy Material Data Sets


We formulate a Data Driven Computing paradigm, termed max-ent Data Driven Computing, that generalizes distance-minimizing Data Driven Computing and is robust with respect to outliers. Robustness is achieved by means of clustering analysis. Specifically, we assign data points a variable relevance depending on distance to the solution and on maximum-entropy estimation. The resulting scheme consists of the minimization of a suitably-defined free energy over phase space subject to compatibility and equilibrium constraints. Distance-minimizing Data Driven schemes are recovered in the limit of zero temperature. We present selected numerical tests that establish the convergence properties of the max-ent Data Driven solvers and solutions.

Additional Information

© 2017 Elsevier B.V. Received 3 March 2017, Revised 17 May 2017, Accepted 27 July 2017, Available online 24 August 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. We gratefully acknowledge helpful discussions with H. Owhadi and T. J. Sullivan.

Attached Files

Submitted - 1702.01574.pdf


Files (1.5 MB)
Name Size Download all
1.5 MB Preview Download

Additional details

August 21, 2023
August 21, 2023