PID-GAN: A GAN Framework based on a Physics-informed Discriminator for Uncertainty Quantification with Physics
PID-GAN: A GAN Framework based on a Physics-informed Discriminator for Uncertainty Quantification with Physics
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DOI：
10.1145/3447548.3467449
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发表时间：
2021-06
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影响因子：
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通讯作者：
Arka Daw;M. Maruf;A. Karpatne
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作者：
Arka Daw;M. Maruf;A. Karpatne
随着深度学习（DL）的应用不断渗透到关键的科学用例中，使用深度学习执行不确定性量化（UQ）的重要性变得比以往任何时候都更加紧迫。在科学应用中，用问题的物理知识来告知DL模型的学习，以产生物理上一致和广义的解决方案也很重要。这被称为基于物理的深度学习（PIDL）的新兴领域。我们考虑开发也可以执行UQ的PIDL公式的问题。为此，我们提出了一种新的物理信息GAN架构，称为PID-GAN，其中物理知识用于通知生成器和鉴别器模型的学习，充分利用未标记的数据实例。我们表明，与最先进的技术相比，我们提出的PID-GAN框架不会受到多个损耗项的发电机梯度不平衡的影响。我们还通过经验证明了我们提出的框架在各种案例研究中的有效性，这些案例研究涉及基于基准物理的偏微分方程以及不完美物理。本研究中使用的所有代码和数据集已在此链接上提供：https://github.com/arkadaw9/PID-GAN。
As applications of deep learning (DL) continue to seep into critical scientific use-cases, the importance of performing uncertainty quantification (UQ) with DL has become more pressing than ever before. In scientific applications, it is also important to inform the learning of DL models with knowledge of physics of the problem to produce physically consistent and generalized solutions. This is referred to as the emerging field of physics-informed deep learning (PIDL). We consider the problem of developing PIDL formulations that can also perform UQ. To this end, we propose a novel physics-informed GAN architecture, termed PID-GAN, where the knowledge of physics is used to inform the learning of both the generator and discriminator models, making ample use of unlabeled data instances. We show that our proposed PID-GAN framework does not suffer from imbalance of generator gradients from multiple loss terms as compared to state-of-the-art. We also empirically demonstrate the efficacy of our proposed framework on a variety of case studies involving benchmark physics-based PDEs as well as imperfect physics. All the code and datasets used in this study have been made available on this link: https://github.com/arkadaw9/PID-GAN.