Design with feature-sensitive neural nets in the immersive environment
Z. Peter Szewczyk, John Wang, Z. Peter Szewczyk, John Wang · 38th Structures, Structural Dynamics, and Materials Conference · 1997
This paper makes an assessment of the utility of modeling of structural systems based on existing databases in which the knowledge about the system behavior has been collected. It is believed that such modeling will open a critical path for equipping offthe-shelf structural components with physics-based models needed for the next generation of the design environment enabling virtual prototyping. The focus of this paper resides with a class of feed-forward neural nets for such modeling, and backpropagation, radial basis and feature-sensitive nets are examined. It is shown that such nets provide not only a high ratio of data compression but also effective and efficient data storage and retrieval. In the numerical study, two problems of building physics-based models from databases are presented; namely sizing a solar panel of a generic satellite for the pointing accuracy, and visualization of nonlinear deformation of the upper skin of a wing box structure.