Construction and application of an ellipsoidal convex model using a semi-definite programming formulation from measured data

By Zhan Kang, Wenbo Zhang. 2016

88 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Computational Theory and Mathematics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/26ba2ffa84c2289dfa3a4af8bcfb20b83c8dc9de

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. Zhiliang Huang
2. Jiang Chao
3. Bingjie Ni
4. Akihiro Takezawa
5. Zhan Kang
6. Lin Cheng
7. Jikai Liu
8. Albert C. To

In Computational Theory and Mathematics

1. Bingjie Ni
2. Zhiliang Huang
3. Jiang Chao
4. Zhan Kang
5. Akihiro Takezawa
6. Jikai Liu
7. Krishnan Suresh
8. Andrew T. Gaynor

In Statistics and Probability

1. Zhiliang Huang
2. Jiang Chao
3. Bingjie Ni
4. Akihiro Takezawa
5. Krishnan Suresh
6. Zhan Kang
7. Albert C. To
8. Jikai Liu

Citations in the corpus

• Current and future trends in topology optimization for additive manufacturing (2018, background): “When sufficient samples are not available to permit a precise probability model of the uncertain inputs, non-probabilistic convex models (Kang and Zhang 2016) providing smooth bound descriptions of the uncertainties become attractive for acquiring reliable topology optimization solutions (Luo et al.”
• Discussions on non-probabilistic convex modelling for uncertain problems (2017, methodology): “For the same purpose, an equivalent semi-definite programming (SDP) formulation was given to determine the minimum-volume ellipsoid model, based on which a reliability-based topology optimization method was further developed [17].”