## 2 Literature Review  
Idea generation, which starts with problem identification in the early design phase, is the process of creating new, creative, and useful solutions [8; 9]. Using deep learning methods in this process reduces design costs and enhances innovation [10; 11; 12]. In the literature, the number of studies involving idea and concept generation and 3D model generation based on deep learning models is rapidly increasing [13; 14]. In one of these studies, [PERSON] et al. proposed an approach to imitating human design strategies using deep learning in the idea generation process [15]. This image-processing-based approach aims to predict the next design step in problems where rules and strategy are ambiguous. [PERSON] and [PERSON] [16] introduced a modified super-resolution generative adversarial network (SRGAN) model incorporating a physics-informed loss function to boost multiphase turbulent fluid flow simulations. This model shows that both traditional and physics-informed models outperform standard sampling methods, but the additional complexity of the physics-informed approach does not significantly increase accuracy. In another generative design study, [PERSON] et al. [17] developed the DesignGAN model inspired by biological objects. This model generates new biologically inspired images by combining target object and biological object images. [PERSON] and [PERSON] created the design-attribute GAN (DAGAN) model using a new loss function and a different discriminative loss mechanism. The DAGAN model allows for modifying only the desired design features, thus enabling efficient fashion product creation [10; 18]. In another study, [PERSON] et al. [19] presented the Generative Adversarial Network-based Design Under Uncertainty Framework (GAN-DUF), for efficiently modeling and quantifying geometric uncertainties in engineering designs. This framework has effectively learned design variability without assuming specific distributions, demonstrating improved design optimization and robustness in real-world applications. On the other hand, generative models can create designs with balanced engineering properties [20; 21; 22; 23]. [PERSON] et al. [20] integrated topology optimization into boundary equilibrium GANs (BEGANs) to obtain aesthetic product designs with high engineering performance. The training data required for the success of the GAN models were obtained by topology optimization to increase the diversity and quality of the training set. The framework, which consists of exploration and evaluation steps, aims to achieve aesthetic and feasible product designs. In a similar study, [PERSON] et al. [24] propose a deep-learning-based framework that automatically generates 3D CAD models at the conceptual design stage and evaluates their engineering performance. This framework allows the engineering performance of the generated 3D concepts to be predicted at the early design stage. In another engineering design study, [PERSON] and [PERSON] (2020) presented a generative model for generating real-world designs bounded by one or more parts. This model generates part designs individually through a low-dimensional representation based on specific master parts. The model was verified on various design examples. [PERSON] et al. (2020) presented a method that uses GANs to simplify 3D CAD models of mechanical parts using a training dataset. This method effectively simplified the models while preserving essential features for the target domain. In another study, [PERSON] and [PERSON] (2020) developed a model called Multi-Objective Performance Augmented Diverse Generative Adversarial Network (MO-PaDGAN) to solve multi-objective optimization problems in engineering design. This model has shown a 180% better performance than other methods in real-world applications thanks to adding a special loss function to the GAN to increase design diversity and quality. [PERSON] et al.