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In this work we aim to mimic the human ability to acquire the intuition toestimate the performance of a design from visual inspection and experiencealone. We study the ability of convolutional neural networks to predict staticand dynamic properties of cantilever beams directly from their rawcross-section images. Using pixels as the only input, the resulting modelslearn to predict beam properties such as volume maximum deflection andeigenfrequencies with 4.54% and 1.43% Mean Average Percentage Error (MAPE)respectively, compared to the Finite Element Analysis (FEA) approach. Trainingthese models doesn't require prior knowledge of theory or relevant geometricproperties, but rather relies solely on simulated or empirical data, therebymaking predictions based on "experience" as opposed to theoretical knowledge.Since this approach is over 1000 times faster than FEA, it can be adopted tocreate surrogate models that could speed up the preliminary optimizationstudies where numerous consecutive evaluations of similar geometries arerequired. We suggest that this modeling approach would aid in addressingchallenging optimization problems involving complex structures and physicalphenomena for which theoretical models are unavailable.

Visual design intuition: Predicting dynamic properties of beams from raw cross-section images