Mixed-integer programming formulation of a data-driven solver in computational elasticity
Author:
Funder
Japan Society for the Promotion of Science
Publisher
Springer Science and Business Media LLC
Subject
Control and Optimization
Link
http://link.springer.com/article/10.1007/s11590-019-01409-w/fulltext.html
Reference11 articles.
1. Ibañez, R., Abisset-Chavanne, E., Aguado, J.V., Gonzalez, D., Cueto, E., Chinesta, F.: A manifold learning approach to data-driven computational elasticity and inelasticity. Arch. Comput. Methods Eng. 25, 47–57 (2018)
2. Ibañez, R., Borzacchiello, D., Aguado, J.V., Abisset-Chavanne, E., Cueto, E., Ladeveze, P., Chinesta, F.: Data-driven non-linear elasticity: constitutive manifold construction and problem discretization. Comput. Mech. 60, 813–826 (2017)
3. IBM ILOG: IBM ILOG CPLEX Optimization Studio Documentation. http://www.ibm.com/support/knowledgecenter/ . Accessed July 2018
4. Kanno, Y.: Simple heuristic for data-driven computational elasticity with material data involving noise and outliers: a local robust regression approach. Jpn. J. Ind. Appl. Math. 35, 1085–1101 (2018)
5. Kanno, Y.: Data-driven computing in elasticity via kernel regression. Theor. Appl. Mech. Lett. 8, 361–365 (2018)
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