3D Design Using Generative Adversarial Networks and Physics-Based Validation

Abstract

The authors present a Generative Adversarial Network (GAN) model that learns how to generate 3D models in their native format so that they can either be evaluated using complex simulation environments, or realized using methods such as additive manufacturing. Once initially trained, the GAN can create additional training data itself by generating new designs, evaluating them in a physics-based virtual environment, and adding the high performing ones to the training set. A case study involving a GAN model that is initially trained on 4045 3D aircraft models is used for demonstration, where the training data set updated with GAN-generated and evaluated designs, results in enhanced model generation, both in the geometric feasibility and performance of the designs. Z-tests on the performance scores of the generated aircraft models indicate a statistically significant improvement in the functionality of the generated models after three iterations of the training-evaluation process. In the case study, a number of techniques are explored to structure the generate-evaluate process in order to balance the need to generate feasible designs with the need for innovative designs.

The Role of Machine Learning for Flexibility and Real Options Analysis in Engineering Systems Design

“…The integration of all these applications, however, would necessarily require a large data set of system designs, both flexible and inflexible. This is not normally available for most design problems; however, it is something that could be developed over time if designs were made more accessible or even built artificially using techniques such as Generative Adversarial Networks (GANs) (Shu et al 2019). There are also a number of potentially interesting applications more closely related to the field of computational creativity.…”

Attribute-Aware Generative Design With Generative Adversarial Networks

“…Part of the reason is the that such datasets are are clean, well-organized, freely-available, and enormous. Nevertheless, Deep generative models have been recently adopted for design automation [66]- [68] with the goal of improving designers' performance through cocreation with AI. Specifically, GAN has shown tremendous success in a variety of generative design tasks, from topology optimization [69] to material design [70] and shape parametrization [68].…”

“…Nevertheless, Deep generative models have been recently adopted for design automation [66]- [68] with the goal of improving designers' performance through cocreation with AI. Specifically, GAN has shown tremendous success in a variety of generative design tasks, from topology optimization [69] to material design [70] and shape parametrization [68]. In line with Osborn's rules for brainstorming [71], these generative models have proven effective in increasing the quantity of ideas at the designer's disposal to inspire her exploration and avoid investing too heavily in few ideas.…”

“…Second, there is a lack of a standardized method of assessing the performance of the generated design concepts [69]. Few recent studies propose assessment mechanisms based on form-function relationships [68] (e.g., physics-based simulators); however, those mechanisms are domain-specific and applicable to a limited set of functional attributes (e.g., aerodynamic performance). Future research must build novel, verifiable GANbased generative design techniques capable of conditioning the design concepts on both visual and functional attributes.…”

A Pilot Study Investigating STEM Learners’ Ability to Decipher AI-generated Video

“…Advancements in Artificial intelligence (AI) methods such as Generative Neural Networks (GNNs), have resulted in the ability to generate hyper-realistic data including images, videos and text, data types that are commonly used to teach in both brick-and-mortar and virtual learning environments [10]. GNNs have resulted in remarkable breakthroughs such as the creation of artwork [11], generation of 3D engineering designs [12], and the generation of educational game levels [13]. These breakthroughs present both an opportunity and a challenge to online STEM learning.…”