Partitioned, multilevel response surfaces for modeling complex systems

AIAA Journal ◽

10.2514/3.14491 ◽

2000 ◽

Vol 38 ◽

pp. 875-881

Author(s):

Patrick N. Koch ◽

Dimitri Mavris ◽

Farrokh Mistree

Keyword(s):

Complex Systems ◽

Response Surfaces

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Multi-level, partitioned response surfaces for modeling complex systems

10.2514/6.1998-4958 ◽

1998 ◽

Cited By ~ 8

Author(s):

Patrick Koch ◽

Dimitri Mavris ◽

Farrokh Mistree

Keyword(s):

Complex Systems ◽

Response Surfaces ◽

Multi Level

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On Using Kriging Models for Complex Design

Volume 5: 37th Design Automation Conference, Parts A and B ◽

10.1115/detc2011-48579 ◽

2011 ◽

Author(s):

Jay D. Martin

Keyword(s):

Complex Systems ◽

Robust Design ◽

Kriging Model ◽

Response Surfaces ◽

Optimal System ◽

System Level ◽

Design Teams ◽

Complex Design ◽

Level Performance ◽

Estimate System

The design of most modern systems requires the tight integration of multiple disciplines. In practice, these multiple disciplines are often optimized independently, given only fixed values or targets for their interactions with other disciplines. The result is a system that may not represent the optimal system-level design. It may also not be a robust design in the sense that small changes in each subsystem’s performance may have a large impact on the system-level performance. The use of kriging models to represent the response surfaces of subsystems that are then combined to estimate system-level performance can be used as a method to provide collaboration between design teams. The difficulty with this method is the creation of the models given potentially large number of dimensions or observations. This paper presents a method to reduce the dimensionality of the input space for kriging models used for designing of complex systems. The input dimensionality of the kriging model is reduced to only includes the most important factors needed for the prediction of the observed output. A result of using these reduced dimensionality models is the need to no longer force interpolation of all of the observations used to create the models.

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