This work presents a deep neural network method for approximating the performance of generated design concepts. This deep learning meta-modeling approach minimizes the need for costly simulations that test for design concept feasibility by discovering the visual features of a design that correlated to good and bad performance. These form-function relationships are discovered by simply observing the pixels of images of many candidate designs and their corresponding performance in a simulation environment. As opposed to existing metamodeling techniques, this evaluation is agnostic to the simulation environment and applicable to any design space in which form and function are closely linked. A case study is presented in which 2D sketches of boats generated from a deep generative model are evaluated in a simulation environment based on their ability to travel through water without sinking as well as their speed of travel. It is shown through simulation that 57.5% of the designs, which are validated according to their form during the generation process, fail in their intended function. Additionally, the trained VNN is able to classify designs it has never seen before as successful or failing with an accuracy of 86.6% and an F1-Score of 0.806.

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