Big data in experimental mechanics and model order reduction: today's challenges and tomorrow's opportunities

DOI10.1007/s11831-017-9234-3zbMath1390.74197OpenAlexW2741574008WikidataQ113322986 ScholiaQ113322986MaRDI QIDQ1639599

Olivier Allix, François Hild, Jan Neggers, Stéphane G. Roux

Publication date: 13 June 2018

Published in: Archives of Computational Methods in Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11831-017-9234-3

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Image analysis in multivariate analysis (62H35) Experimental work for problems pertaining to mechanics of deformable solids (74-05) Applications of statistics to physics (62P35) Stochastic and other probabilistic methods applied to problems in solid mechanics (74S60)

Related Items (6)

On a Physics-Compatible Approach for Data-Driven Computational Mechanics ⋮ Calibration of elastoplastic constitutive model parameters from full‐field data with automatic differentiation‐based sensitivities ⋮ Automated discovery of generalized standard material models with EUCLID ⋮ Enhancing phenomenological yield functions with data: challenges and opportunities ⋮ Comparing field data using Alpert multi-wavelets ⋮ mCRE-based parameter identification from full-field measurements: consistent framework, integrated version, and extension to nonlinear material behaviors

Uses Software

• DDDAS

Cites Work

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