Summary of Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: a Multiobjective Hyperparameter and Architecture Optimization Approach, by Yixuan Sun et al.
Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A Multiobjective Hyperparameter and Architecture Optimization Approach
by Yixuan Sun, Ololade Sowunmi, Romain Egele, Sri Hari Krishna Narayanan, Luke Van Roekel, Prasanna Balaprakash
First submitted to arxiv on: 7 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Atmospheric and Oceanic Physics (physics.ao-ph); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed research utilizes deep learning techniques to optimize the performance of Fourier neural operators (FNOs) for simulating complex ocean behaviors. The study leverages DeepHyper, a scalable hyperparameter optimization software, to streamline the development of neural networks tailored for ocean modeling. By carefully selecting and tuning hyperparameters associated with data preprocessing, FNO architecture-related parameters, and model training strategies using advanced search algorithms for multiobjective optimization, the research aims to obtain an optimal set of hyperparameters leading to the most performant model. Additionally, the study proposes adopting the negative anomaly correlation coefficient as an additional loss term to improve model performance. The experimental results show that the optimal set of hyperparameters enhanced model performance in single timestepping forecasting and greatly exceeded the baseline configuration in autoregressive rollout for long-horizon forecasting up to 30 days. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study aims to improve ocean modeling by optimizing deep learning models called Fourier neural operators (FNOs). FNOs are good at simulating complex ocean behaviors, but choosing the right settings and adjusting them is hard. The researchers use a special tool called DeepHyper that can quickly try out different settings and find the best ones for their job. They also suggest adding an extra way to measure how well the model does, which helps it learn even better. The results show that using these optimized settings makes the models much more accurate at forecasting what the ocean will do in the future. |
Keywords
» Artificial intelligence » Autoregressive » Deep learning » Hyperparameter » Optimization
