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Generative VS non-Generative Models in Engineering Shape Optimization
In this work, we perform a systematic comparison of the effectiveness and efficiency of generative and non-generative models in constructing design spaces for novel and efficient design exploration and shape optimization.
How does agency impact human-AI collaborative design space exploration? A case study on ship design with deep generative models
However, the SAEM and AEM produce better-performing designs.
ShipHullGAN: A generic parametric modeller for ship hull design using deep convolutional generative model
In this work, we introduce ShipHullGAN, a generic parametric modeller built using deep convolutional generative adversarial networks (GANs) for the versatile representation and generation of ship hulls.
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