Concurrent Subspace Optimization using gradient-enhanced neural network approximations
R. Sellar, Stephen M. Batill · 6th Symposium on Multidisciplinary Analysis and Optimization · 1996
Design space approximations have proven useful as a means of coordinating individual discipline design decisions in the multidisciplinary design of complex, coupled systems. Artificial 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). I.