Configuration Selection via Self-Supervised, Performance-Weighted Generative Neural Networks

Configuration selection is an important and often primary task of conceptual design teams within aerospace. Many other sub-tasks within the domain have heavily benefited and today even rely upon computer-aided design methods, but configuration selection has remained a largely abstract, informal process. In this paperwe develop a model of configuration selection as a NP-complete mixed-integer optimization problem to explicate the difficulties involved therein and introduce EDEN - the Experimental Design Engineering Network - as a class of generative neural networks whose inference may be used as a heuristic method for identifying promising design candidates. We demonstratemethods for embedding design candidates into a continuous latent space and building a synthetic, performance-weighted probability distribution over said space. By learning to reconstruct samples generated from this latent space as design candidates, EDENsurpasses previouswork in generative design of configurations by predictably generating high-quality design candidates rather than merely feasible configurations with no weighting toward optimality.