Generative adversarial networks (GAN) have recently been used for a design
synthesis of mechanical shapes. A GAN sometimes outputs physically unreasonable
shapes. For example, when a GAN model is trained to output airfoil shapes that
indicate required aerodynamic performance, significant errors occur in the
performance values. This is because the GAN model only considers data but does
not consider the aerodynamic equations that lie under the data. This paper
proposes the physics-guided training of the GAN model to guide the model to
learn physical validity. Physical validity is computed using general-purpose
software located outside the neural network model. Such general-purpose
software cannot be used in physics-informed neural network frameworks, because
physical equations must be implemented inside the neural network models.
Additionally, a limitation of generative models is that the output data are
similar to the training data and cannot generate completely new shapes.
However, because the proposed model is guided by a physical model and does not
use a training dataset, it can generate completely new shapes. Numerical
experiments show that the proposed model drastically improves the accuracy.
Moreover, the output shapes differ from those of the training dataset but still
satisfy the physical validity, overcoming the limitations of existing GAN
models.

通过物理引导训练生成对抗网络（GAN）模型，在不使用训练数据集的情况下，生成全新的形状，并且改善了现有 GAN 模型的准确性和物理有效性。