The analysis of engineering systems must often be conducted using complex, non-hierarchic, coupled, discipline-speci c methods. When the cost of performing these individual analyses is high, it is impractical to apply many current optimization methods to this type of system to achieve improved designs. Consequently, methods are being developed which attempt to reduce the cost of designing or optimizing non-hierarchic systems. This paper details the application of an extension of the Concurrent Subspace Optimization (CSSO) approach through the use of neural network based response surface mappings. The response surface mappings are used to allow the discipline designer to account for discipline coupling and the impact of design decisions on the system at the discipline level as well as for system level design coordination. The ability of this method to identify globally optimal designs is discussed using two example system design problems. Comparisons between this algorithm and full system optimization are made with regard to computational expense associated with obtaining optimal system designs.

Design space approximations have proven useful as a means of coordinating individual discipline design decisions in the multidisciplinary design of complex, coupled systems. Arti cial neural networks have been used to provide these parameterized response surface approximations. A method has been developed in which neural networks can be trained using both state and state sensitivity information. This allows for more compact network geometries and reduces the number of coupled system analyses required to develop useful design space approximations. This approach is applied to the Concurrent Subspace Optimization (CSSO) framework for a nonhierarchic test problem in which the sensitivity information is provided using the Global Sensitivity Equations (GSEs).

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The practical application of optimization methods to non-hierarchic, coupled, multidisciplinary systems has been hampered by the costs associated with the use of complex discipline-speci c analysis procedures. When the cost of performing these individual analyses is high, it is impractical to apply many current optimization methods to most practical systems in order to identify improved designs. This paper details an extended formulation and application of the Concurrent Subspace Optimization (CSSO) approach to this class of system design problems. In this application neural network based response surface mappings are used to allow the discipline designer to account for discipline coupling as well as for system level design coordination. The application is the design of a \hovercraft" and includes external con guration, structures, performance and propulsion disciplines.

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