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Generative adversarial networks (GAN) have recently been used for a designsynthesis of mechanical shapes. A GAN sometimes outputs physically unreasonableshapes. For example, when a GAN model is trained to output airfoil shapes thatindicate required aerodynamic performance, significant errors occur in theperformance values. This is because the GAN model only considers data but doesnot consider the aerodynamic equations that lie under the data. This paperproposes the physics-guided training of the GAN model to guide the model tolearn physical validity. Physical validity is computed using general-purposesoftware located outside the neural network model. Such general-purposesoftware cannot be used in physics-informed neural network frameworks, becausephysical equations must be implemented inside the neural network models.Additionally, a limitation of generative models is that the output data aresimilar to the training data and cannot generate completely new shapes.However, because the proposed model is guided by a physical model and does notuse a training dataset, it can generate completely new shapes. Numericalexperiments show that the proposed model drastically improves the accuracy.Moreover, the output shapes differ from those of the training dataset but stillsatisfy the physical validity, overcoming the limitations of existing GANmodels.

Physics-guided training of GAN to improve accuracy in airfoil design synthesis