Probabilistic Analytical Target Cascading: A Moment Matching Formulation for Multilevel Optimization Under Uncertainty

By Huibin Liu, Michael Kokkolaras, Panos Y. Papalambros, Harrison M. Kim. Design Automation Conference, 2006

131 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Safety, Risk, Reliability and Quality, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/abbae3bd5c34a181ab6749ee6e1a1cc1a3d61eae

How researchers see this paper

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

In Computational Theory and Mathematics

1. Andrew B. Lambe
2. Joaquim R. Martins
3. Gaetan K. W. Kenway
4. John T. Hwang
5. Martin P. Bendsøe

In Safety, Risk, Reliability and Quality

1. Joaquim R. Martins
2. Andrew B. Lambe

In Statistics and Probability

1. Andrew B. Lambe
2. Joaquim R. Martins
3. Gaetan K. W. Kenway
4. John T. Hwang
5. Martin P. Bendsøe

Citations in the corpus

• Multidisciplinary design optimization: A survey of architectures (2013, background): “Formulations exist that can handle integer variables [47] and probabilistic design problems [188, 189].”
• Key computational modeling issues in Integrated Computational Materials Engineering (2013, unknown): “Within PATC, uncertainty is modeled in termsof randomvariables anduncertainty propagation techniques are used to determine the impact of uncertainties at the bottom level on parameters at the top level.”