# Physical Knowledge Enhanced Deep Neural Network for Sea Surface Temperature Prediction

arXiv: https://arxiv.org/abs/2304.09376 · HTML: https://mlsanity.com/paper/2304.09376/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 59 references that are in the map.
- First posted on arXiv 19 Apr 2023.

## Builds on (most cited first, 30 of 30)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [One pixel attack for fooling deep neural networks](https://mlsanity.com/paper/1710.08864/) (2017)
- [A Neural Algorithm of Artistic Style](https://mlsanity.com/paper/1508.06576/) (2015)
- [In-Domain GAN Inversion for Real Image Editing](https://mlsanity.com/paper/2004.00049/) (2020)
- [Autoencoding beyond pixels using a learned similarity metric](https://mlsanity.com/paper/1512.09300/) (2015)
- [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015)
- [Generative Visual Manipulation on the Natural Image Manifold](https://mlsanity.com/paper/1609.03552/) (2016)
- [GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification](https://mlsanity.com/paper/1803.01229/) (2018)
- [Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations](https://mlsanity.com/paper/1811.02033/) (2018)
- [Dataset Augmentation in Feature Space](https://mlsanity.com/paper/1702.05538/) (2017)
- [Return of the Devil in the Details: Delving Deep into Convolutional Nets](https://mlsanity.com/paper/1405.3531/) (2014)
- [Data Augmentation using Random Image Cropping and Patching for Deep CNNs](https://mlsanity.com/paper/1811.09030/) (2018)
- [Style Augmentation: Data Augmentation via Style Randomization](https://mlsanity.com/paper/1809.05375/) (2018)
- [Label Refinery: Improving ImageNet Classification through Label Progression](https://mlsanity.com/paper/1805.02641/) (2018)
- [Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks](https://mlsanity.com/paper/1509.06041/) (2015)
- [Improved Mixed-Example Data Augmentation](https://mlsanity.com/paper/1805.11272/) (2018)
- [Adversarial Framing for Image and Video Classification](https://mlsanity.com/paper/1812.04599/) (2018)
- [Physics-informed semantic inpainting: Application to geostatistical modeling](https://mlsanity.com/paper/1909.09459/) (2019)
- [Prediction of Sea Surface Temperature using Long Short-Term Memory](https://mlsanity.com/paper/1705.06861/) (2017)
- [Physics-informed GANs for Coastal Flood Visualization](https://mlsanity.com/paper/2010.08103/) (2020)
- [Learning More Robust Features with Adversarial Training](https://mlsanity.com/paper/1804.07757/) (2018)
- [Icing on the Cake: An Easy and Quick Post-Learnig Method You Can Try After Deep Learning](https://mlsanity.com/paper/1807.06540/) (2018)
- [Sea surface temperature prediction and reconstruction using patch-level neural network representations](https://mlsanity.com/paper/1806.00144/) (2018)

## Built on by (most cited first, 8 of 8)

- [Foundation Models for Weather and Climate Data Understanding: A Comprehensive Survey](https://mlsanity.com/paper/2312.03014/) (2023)
- [Deep Learning and Foundation Models for Weather Prediction: A Survey](https://mlsanity.com/paper/2501.06907/) (2025)
- [Frequency-Compensated Network for Daily Arctic Sea Ice Concentration Prediction](https://mlsanity.com/paper/2504.16745/) (2025)
- [OKG-LLM: Aligning Ocean Knowledge Graph with Observation Data via LLMs for Global Sea Surface Temperature Prediction](https://mlsanity.com/paper/2508.00933/) (2025)
- [Wavelet-Assisted Mamba for Satellite-Derived Sea Surface Temperature Super-Resolution](https://mlsanity.com/paper/2509.24334/) (2025)
- [CTP: A hybrid CNN-Transformer-PINN model for ocean front forecasting](https://mlsanity.com/paper/2505.10894/) (2025)
- [DeepSeasons: a Deep Learning scale-selecting approach to Seasonal Forecasts](https://mlsanity.com/paper/2509.10494/) (2025)
- [Dual-Branch State-Displacement Network for Sea Surface Temperature Super-Resolution](https://mlsanity.com/paper/2608.15423/) (2026)

## Abstract (arXiv)

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.