Engineering design tasks often require synthesizing new designs that meet desired performance requirements. The conventional design process, which requires iterative optimization and performance evaluation, is slow and dependent on initial designs. Past work has used conditional generative adversarial networks (cGANs) to enable direct design synthesis for given target performances. However, most existing cGANs are restricted to categorical conditions. Recent work on Continuous conditional GAN (CcGAN) tries to address this problem, but still faces two challenges: 1) it performs poorly on non-uniform performance distributions, and 2) the generated designs may not cover the entire design space. We propose a new model, named Performance Conditioned Diverse Generative Adversarial Network (PcDGAN), which introduces a singular vicinal loss combined with a Determinantal Point Processes (DPP) based loss function to enhance diversity. PcDGAN uses a new self-reinforcing score called the Lambert Log Exponential Transition Score (LLETS) for improved conditioning. Experiments on synthetic problems and a real-world airfoil design problem demonstrate that PcDGAN outperforms state-of-the-art GAN models and improves the conditioning likelihood by 69% in an airfoil generation task and up to 78% in synthetic conditional generation tasks and achieves greater design space coverage. The proposed method enables efficient design synthesis and design space exploration with applications ranging from CAD model generation to metamaterial selection.
翻译:常规设计过程需要迭代优化和性能评估,但缓慢且取决于初步设计。过去的工作使用了一个名为“性能调控”的新型模型,用于对特定性能进行直接设计合成。然而,大多数现有的CGAN(CGAN)限于绝对条件。最近关于连续有条件的GAN(CcGAN)的工作试图解决这一问题,但仍然面临两个挑战:(1) 它在非统一性性性能分布上表现不佳,(2) 所产生的设计可能无法覆盖整个设计空间。我们提出了名为“性能调控”的多样化多动性能对反向网络(PcDGAN)的新模型,它引入了与基于“阻力点”进程(DPPP)的损失功能相结合的独一线性设计合成合成功能,以加强多样性。PcDGAN(CDGAN)最近的工作使用一个新的自我强化评分,称为“Lambol Loc Eveniential 过渡评分”(LETS),用于改进调控调。关于合成问题和真实世界的空气结构设计问题实验,显示PcDGDAN(DAAN)在设计模型中,在设计中,在设计中,以更精确的模型中,使GADADADADADM-ral-res型的模型中,在设计中,在设计中,在设计中,在设计中,使GMLM-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-S-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-