# OceanNet: A principled neural operator-based digital twin for regional oceans

arXiv: https://arxiv.org/abs/2310.00813 · HTML: https://mlsanity.com/paper/2310.00813/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 1 Oct 2023.

## Builds on (most cited first, 8 of 8)

- [Fourier Neural Operator for Parametric Partial Differential Equations](https://mlsanity.com/paper/2010.08895/) (2020)
- [FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators](https://mlsanity.com/paper/2202.11214/) (2022)
- [Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks](https://mlsanity.com/paper/1901.06523/) (2019)
- [GraphCast: Learning skillful medium-range global weather forecasting](https://mlsanity.com/paper/2212.12794/) (2022)
- [Improving data-driven global weather prediction using deep convolutional neural networks on a cubed sphere](https://mlsanity.com/paper/2003.11927/) (2020)
- [Analog forecasting of extreme-causing weather patterns using deep learning](https://mlsanity.com/paper/1907.11617/) (2019)
- [Challenges of learning multi-scale dynamics with AI weather models: Implications for stability and one solution](https://mlsanity.com/paper/2304.07029/) (2023)
- [Learning continuous models for continuous physics](https://mlsanity.com/paper/2202.08494/) (2022)

## Built on by (most cited first, 24 of 24)

- [LUCIE: A Lightweight Uncoupled ClImate Emulator with long-term stability and physical consistency for O(1000)-member ensembles](https://mlsanity.com/paper/2405.16297/) (2024)
- [Coupled Ocean-Atmosphere Dynamics in a Machine Learning Earth System Model](https://mlsanity.com/paper/2406.08632/) (2024)
- [Binned Spectral Power Loss for Improved Prediction of Chaotic Systems](https://mlsanity.com/paper/2502.00472/) (2025)
- [Evaluation of Tropical Cyclone Track and Intensity Forecasts from Artificial Intelligence Weather Prediction (AIWP) Models](https://mlsanity.com/paper/2409.06735/) (2024)
- [Simultaneous emulation and downscaling with physically-consistent deep learning-based regional ocean emulators](https://mlsanity.com/paper/2501.05058/) (2025)
- [Geometry-Informed Neural Operator Transformer](https://mlsanity.com/paper/2504.19452/) (2025)
- [Structure-Preserving Digital Twins via Conditional Neural Whitney Forms](https://mlsanity.com/paper/2508.06981/) (2025)
- [Probing forced responses and causality in data-driven climate emulators: conceptual limitations and the role of reduced-order models](https://mlsanity.com/paper/2506.22552/) (2025)
- [Gradient-free online learning of subgrid-scale dynamics with neural emulators](https://mlsanity.com/paper/2310.19385/) (2023)
- [Lazy Diffusion: Mitigating spectral collapse in generative diffusion-based stable autoregressive emulation of turbulent flows](https://mlsanity.com/paper/2512.09572/) (2025)
- [Digital Twin AI: Opportunities and Challenges from Large Language Models to World Models](https://mlsanity.com/paper/2601.01321/) (2026)
- [Improved deep learning of chaotic dynamical systems with multistep penalty losses](https://mlsanity.com/paper/2410.05572/) (2024)
- [Deep Learning Weather Models for Subregional Ocean Forecasting: A Case Study on the Canary Current Upwelling System](https://mlsanity.com/paper/2505.24429/) (2025)
- [Fourier analysis of the physics of transfer learning for data-driven subgrid-scale models of ocean turbulence](https://mlsanity.com/paper/2504.15487/) (2025)
- [Efficient probabilistic surrogate modeling techniques for partially-observed large-scale dynamical systems](https://mlsanity.com/paper/2511.04641/) (2025)
- [SWR-Viz: AI-assisted Interactive Visual Analytics Framework for Ship Weather Routing](https://mlsanity.com/paper/2511.15182/) (2025)
- [Skillful Global Ocean Emulation and the Role of Correlation-Aware Loss](https://mlsanity.com/paper/2604.18727/) (2026)
- [KunPeng: A Global Ocean Environmental Model](https://mlsanity.com/paper/2504.04766/) (2025)
- [Ensembles of Neural Surrogates for Parametric Sensitivity in Ocean Modeling](https://mlsanity.com/paper/2508.16489/) (2025)
- [Principled Operator Learning in Ocean Dynamics: The Role of Temporal Structure](https://mlsanity.com/paper/2510.09792/) (2025)
- [High-resolution probabilistic estimation of three-dimensional regional ocean dynamics from sparse surface observations](https://mlsanity.com/paper/2604.02850/) (2026)
- [Toward Artificial Intelligence Enabled Earth System Coupling](https://mlsanity.com/paper/2604.03289/) (2026)
- [Eigenanalysis framework for autoregressive neural emulators of multi-scale chaotic dynamics](https://mlsanity.com/paper/2608.16084/) (2026)
- [Neptune: An AI model for Global Ocean Subseasonal Prediction](https://mlsanity.com/paper/2609.08606/) (2026)

## Abstract (arXiv)

While data-driven approaches demonstrate great potential in atmospheric modeling and weather forecasting, ocean modeling poses distinct challenges due to complex bathymetry, land, vertical structure, and flow non-linearity. This study introduces OceanNet, a principled neural operator-based digital twin for ocean circulation. OceanNet uses a Fourier neural operator and predictor-evaluate-corrector integration scheme to mitigate autoregressive error growth and enhance stability over extended time scales. A spectral regularizer counteracts spectral bias at smaller scales. OceanNet is applied to the northwest Atlantic Ocean western boundary current (the Gulf Stream), focusing on the task of seasonal prediction for Loop Current eddies and the Gulf Stream meander. Trained using historical sea surface height (SSH) data, OceanNet demonstrates competitive forecast skill by outperforming SSH predictions by an uncoupled, state-of-the-art dynamical ocean model forecast, reducing computation by 500,000 times. These accomplishments demonstrate the potential of physics-inspired deep neural operators as cost-effective alternatives to high-resolution numerical ocean models.