Non-parametric data-driven approach to reliability-based topology optimization of trusses under uncertainty of material constitutive law

Autor: Yoshihiro KANNO

Publikováno v: Journal of Advanced Mechanical Design, Systems, and Manufacturing, Vol 18, Iss 5, Pp JAMDSM0064-JAMDSM0064 (2024)

The material behavior intrinsically possesses the aleatory uncertainty (i.e., the natural variability). Against uncertainty in a given data set of elastic material responses, this paper presents a data-driven approach to reliability-based truss topol

Externí odkaz: https://doaj.org/article/7626c75fd14d45caa69c8ea7f6f80d62

Three-dimensional data-driven magnetostatic field computation using real-world measurement data

Autor: Galetzka, Armin, Loukrezis, Dimitrios, De Gersem, Herbert

Publikováno v: COMPEL -The international journal for computation and mathematics in electrical and electronic engineering, 2021, Vol. 41, Issue 2, pp. 615-627.

Externí odkaz: http://www.emeraldinsight.com/doi/10.1108/COMPEL-06-2021-0219

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Alternating minimization for data-driven computational elasticity from experimental data: kernel method for learning constitutive manifold

Autor: Yoshihiro Kanno

Publikováno v: Theoretical and Applied Mechanics Letters, Vol 11, Iss 5, Pp 100289- (2021)

Data-driven computing in elasticity attempts to directly use experimental data on material, without constructing an empirical model of the constitutive relation, to predict an equilibrium state of a structure subjected to a specified external load. P

Externí odkaz: https://doaj.org/article/7811f04a4abb47539f5b12f02939f59b

A constraint-driven approach to food supply chain management

Autor: Sitek, Pawel, Wikarek, Jaroslaw, Nielsen, Peter

Publikováno v: Industrial Management & Data Systems, 2017, Vol. 117, Issue 9, pp. 2115-2138.

Externí odkaz: http://www.emeraldinsight.com/doi/10.1108/IMDS-10-2016-0465