Data-driven computing in elasticity via kernel regression
ABSTRACT: This paper presents a simple nonparametric regression approach to data-driven computing in elasticity. We apply the kernel regression to the material data set, and formulate a system of nonlinear equations solved to obtain a static equilibrium state of an elastic structure. Preliminary num...
Main Author: | Yoshihiro Kanno |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2018-12-01
|
Series: | Theoretical and Applied Mechanics Letters |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2095034918302071 |
Similar Items
-
Data-driven Transient Stability Assessment Model Considering Network Topology Changes via Mahalanobis Kernel Regression and Ensemble Learning
by: Xianzhuang Liu, et al.
Published: (2020-01-01) -
Dimensionality reduction enhances data-driven reliability-based design optimize
by: Yoshihiro KANNO
Published: (2020-01-01) -
Multiple Kernel Clustering via Local Regression Integration
by: DU Liang, REN Xin, ZHANG Hai-ying, ZHOU Peng
Published: (2021-08-01) -
Instrumental variable regression via kernel maximum moment loss
by: Zhang Rui, et al.
Published: (2023-04-01) -
Non‐concept density estimation via kernel regression for concept ranking in weakly labelled data
by: Liantao Wang, et al.
Published: (2018-02-01)