Physics-guided generative adversarial network to learn physical models
This addresses the problem of ensuring physical consistency in DNN predictions for physics and mechanics applications, but it is incremental as it builds on existing physics-informed approaches.
The paper tackles the issue of deep neural networks (DNNs) not always satisfying physical equations by proposing a physics-guided generative adversarial network (PG-GAN) that uses physical equations to judge output consistency, and it was applied to a simple problem to assess potential usability.
This short note describes the concept of guided training of deep neural networks (DNNs) to learn physically reasonable solutions. DNNs are being widely used to predict phenomena in physics and mechanics. One of the issues of DNNs is that their output does not always satisfy physical equations. One approach to consider physical equations is adding a residual of equations into the loss function; this is called physics-informed neural network (PINN). One feature of PINNs is that the physical equations and corresponding residual must be implemented as part of a neural network model. In addition, the residual does not always converge to a small value. The proposed model is a physics-guided generative adversarial network (PG-GAN) that uses a GAN architecture in which physical equations are used to judge whether the neural network's output is consistent with physics. The proposed method was applied to a simple problem to assess its potential usability.