Physics-guided training of GAN to improve accuracy in airfoil design
  synthesis
Kazunari Wada Katsuyuki Suzuki Kazuo Yonekura
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.