Traditionally, numerical models have been deployed in oceanography studies to
simulate ocean dynamics by representing physical equations. However, many
factors pertaining to ocean dynamics seem to be ill-defined. We argue that
transferring physical knowledge from observed data could further improve the
accuracy of numerical models when predicting Sea Surface Temperature (SST).
Recently, the advances in earth observation technologies have yielded a
monumental growth of data. Consequently, it is imperative to explore ways in
which to improve and supplement numerical models utilizing the ever-increasing
amounts of historical observational data. To this end, we introduce a method
for SST prediction that transfers physical knowledge from historical
observations to numerical models. Specifically, we use a combination of an
encoder and a generative adversarial network (GAN) to capture physical
knowledge from the observed data. The numerical model data is then fed into the
pre-trained model to generate physics-enhanced data, which can then be used for
SST prediction. Experimental results demonstrate that the proposed method
considerably enhances SST prediction performance when compared to several
state-of-the-art baselines.

本文介绍了一种将历史观测数据中的物理知识转移至数值模型的方法，使用编码器和生成对抗网络的组合来捕捉观测数据中的物理知识，并将数值模型数据馈入预训练模型以生成物理增强数据进行 SST 预测，实验结果表明，该方法在 SST 预测性能方面相比几种最先进的基线方法有了明显的提升。

基于物理知识增强的深度神经网络预测海表面温度

Physical Knowledge Enhanced Deep Neural Network for Sea Surface  Temperature Prediction

The scientific and technological revolution of the Internet of Things has
begun in the area of oceanography. Historically, humans have observed the ocean
from an external viewpoint in order to study it. In recent years, however,
changes have occurred in the ocean, and laboratories have been built on the
seafloor. Approximately 70.8% of the Earth's surface is covered by oceans and
rivers. The Ocean of Things is expected to be important for disaster
prevention, ocean-resource exploration, and underwater environmental
monitoring. Unlike traditional wireless sensor networks, the Ocean Network has
its own unique features, such as low reliability and narrow bandwidth. These
features will be great challenges for the Ocean Network. Furthermore, the
integration of the Ocean Network with artificial intelligence has become a
topic of increasing interest for oceanology researchers. The Cognitive Ocean
Network (CONet) will become the mainstream of future ocean science and
engineering developments. In this article, we define the CONet. The
contributions of the paper are as follows: (1) a CONet architecture is proposed
and described in detail; (2) important and useful demonstration applications of
the CONet are proposed; and (3) future trends in CONet research are presented.

该文介绍了互联网物联网在海洋学领域的科技革命，提出了海洋认知网络（CONet），并探讨了 CONet 的架构、应用和未来研究方向。