Enforcing Deterministic Constraints on Generative Adversarial Networks for Emulating Physical Systems
This work addresses the need for trustworthy GAN emulators in physical systems, offering a physics-based regularization method to overcome training challenges, though it is incremental as it builds on prior statistical constraint enforcement.
The authors tackled the problem of ensuring GAN-generated samples adhere to deterministic physical constraints, such as conservation laws, by incorporating these constraints into the generator's loss function. They demonstrated that this approach produced accurate samples and significantly accelerated training convergence and improved robustness in tasks like generating points on circles and divergence-free flow fields.
Generative adversarial networks (GANs) were initially proposed to generate images by learning from a large number of samples. Recently, GANs have been used to emulate complex physical systems such as turbulent flows. However, a critical question must be answered before GANs can be considered trusted emulators for physical systems: do GANs-generated samples conform to the various physical constraints? These include both deterministic constraints (e.g., conservation laws) and statistical constraints (e.g., energy spectrum of turbulent flows). The latter have been studied in a companion paper (Wu et al., Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical systems. Journal of Computational Physics. 406, 109209, 2020). In the present work, we enforce deterministic yet imprecise constraints on GANs by incorporating them into the loss function of the generator. We evaluate the performance of physics-constrained GANs on two representative tasks with geometrical constraints (generating points on circles) and differential constraints (generating divergence-free flow velocity fields), respectively. In both cases, the constrained GANs produced samples that conform to the underlying constraints rather accurately, even though the constraints are only enforced up to a specified interval. More importantly, the imposed constraints significantly accelerate the convergence and improve the robustness in the training, indicating that they serve as a physics-based regularization. These improvements are noteworthy, as the convergence and robustness are two well-known obstacles in the training of GANs.