论文标题
物理受限的生成对抗网络，用于3D湍流
Physics-Constrained Generative Adversarial Networks for 3D Turbulence
论文作者
论文摘要
生成的对抗网络（GAN）在机器学习（ML）社区中获得了广泛的好评，因为它们能够生成现实的2D图像。超出计算机视觉的问题，将ML更频繁地用于复杂问题。但是，当前的框架通常用作黑匣子，并且缺乏物理嵌入，从而导致强制执行约束和不可靠模型的能力差。在这项工作中，我们开发了可以严格强加的物理嵌入，在神经网络体系结构中被称为硬约束。我们通过将它们嵌入gan中来证明它们的3D湍流能力，特别是在不可压缩的流体湍流中执行质量保护约束。在此过程中，我们还探索和对比了在甘恩斯框架内施加物理限制的其他方法的效果，尤其是基于惩罚的物理学约束在文献中受欢迎的。通过使用具有物理信息的诊断和统计数据，我们评估了方法的优势和劣势，并证明了其可行性。
Generative Adversarial Networks (GANs) have received wide acclaim among the machine learning (ML) community for their ability to generate realistic 2D images. ML is being applied more often to complex problems beyond those of computer vision. However, current frameworks often serve as black boxes and lack physics embeddings, leading to poor ability in enforcing constraints and unreliable models. In this work, we develop physics embeddings that can be stringently imposed, referred to as hard constraints, in the neural network architecture. We demonstrate their capability for 3D turbulence by embedding them in GANs, particularly to enforce the mass conservation constraint in incompressible fluid turbulence. In doing so, we also explore and contrast the effects of other methods of imposing physics constraints within the GANs framework, especially penalty-based physics constraints popular in literature. By using physics-informed diagnostics and statistics, we evaluate the strengths and weaknesses of our approach and demonstrate its feasibility.
