Enforcing Deterministic Constraints on Generative Adversarial Networks for Emulating Physical Systems
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Generative adversarial networks (GANs) are 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 in turbulent flows). The latter has been studied in a companion paper (Wu et al. 2019. Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical systems. arxiv:1905.06841). In the present work, we enforce deterministic yet approximate 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 precisely conform to the underlying constraints, even though the constraints are only enforced approximately. More importantly, the imposed constraints significantly accelerate the convergence and improve the robustness in the training. These improvements are noteworthy, as the convergence and robustness are two well-known obstacles in the training of GANs.
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