Optimizing Fourier Neural Operators for Accurate Ocean Dynamics Modeling through Multiobjective Hyperparameter Search

Keskeiset käsitteet

Leveraging advanced search algorithms for multiobjective optimization to streamline the development of Fourier neural operators tailored for accurate ocean modeling.

Tiivistelmä

The paper focuses on optimizing Fourier neural operators (FNOs) for ocean dynamics modeling through a multiobjective hyperparameter search approach. FNOs are a data-driven model capable of simulating complex ocean behaviors.

The key highlights and insights are:

1. Careful selection of model hyperparameters is crucial for the performance of deep learning models in ocean modeling, but manual tuning is infeasible due to the vast search space.

2. The authors leverage DeepHyper's advanced search algorithms for multiobjective optimization to efficiently explore hyperparameters associated with data preprocessing, FNO architecture, and training strategies.

3. In addition to the commonly used mean squared error (MSE) loss, the authors propose adopting the negative anomaly correlation coefficient (ACC) as an additional loss term to improve model performance and investigate the potential trade-off between the two.

4. The experimental results show that the optimal set of hyperparameters enhanced model performance in single timestepping forecasting and greatly exceeded the baseline configuration in the autoregressive rollout for long-horizon forecasting up to 30 days.

5. The authors demonstrate that there is no trade-off between minimizing MSE and maximizing ACC, and the simple addition of negative ACC can benefit ocean modeling using FNOs.

6. The proposed approach offers a scalable solution with improved precision for ocean dynamics forecasting using FNOs.

Tilastot

The dataset used in the experiment consisted of 100 simulations of salinity, temperature, zonal velocity, and meridional velocity at the ocean surface of an idealized baroclinic wind-driven ocean model.

Lainaukset

"Training an effective deep learning model to learn ocean processes involves careful choices of various hyperparameters."
"We propose adopting the negative anomaly correlation coefficient as the additional loss term to improve model performance and investigate the potential trade-off between the two terms."
"The experimental results show that the optimal set of hyperparameters enhanced model performance in single timestepping forecasting and greatly exceeded the baseline configuration in the autoregressive rollout for long-horizon forecasting up to 30 days."

Tärkeimmät oivallukset

Streamlining Ocean Dynamics Modeling with Fourier Neural Operators

by Yixuan Sun,O... klo arxiv.org 04-10-2024

https://arxiv.org/pdf/2404.05768.pdf

Syvällisempiä Kysymyksiä

How can the proposed approach be extended to other types of ocean models or climate modeling tasks

The proposed approach of using Fourier neural operators (FNOs) and hyperparameter optimization can be extended to other types of ocean models or climate modeling tasks by adapting the model architecture and hyperparameters to suit the specific requirements of the new model. For different ocean models or climate modeling tasks, the input data characteristics, the variables of interest, and the desired forecasting horizon may vary. Therefore, the hyperparameter search space needs to be customized accordingly. Additionally, the loss function can be tailored to incorporate domain-specific metrics or objectives that are relevant to the new model. By fine-tuning the hyperparameters and model architecture based on the specific characteristics of the new model, the approach can be effectively applied to a wide range of ocean models and climate modeling tasks.

What are the potential limitations or drawbacks of using the negative anomaly correlation coefficient as an additional loss term, and under what circumstances might it not be beneficial

The use of the negative anomaly correlation coefficient as an additional loss term may have some limitations or drawbacks in certain scenarios. One potential limitation is that the negative ACC may introduce additional complexity to the loss function, which could make the optimization process more challenging. In some cases, the negative ACC may prioritize capturing anomalies over the overall mean behavior of the system, leading to potential overfitting to the training data. Moreover, the negative ACC may not always align with the specific objectives of the modeling task, especially if the focus is on long-term trends or average behaviors rather than deviations from the mean. In such cases, the negative ACC may not provide significant benefits and could potentially hinder model performance. It is essential to carefully evaluate the trade-offs and implications of incorporating the negative ACC as an additional loss term based on the specific requirements and goals of the modeling task.

How can the hyperparameter search process be further optimized to reduce computational costs and improve efficiency, such as through the use of one-epoch or multifidelity approaches

To further optimize the hyperparameter search process and reduce computational costs, several strategies can be implemented. One approach is to utilize one-epoch approaches, where the model is trained for a single epoch to evaluate its performance quickly. This can help in rapidly assessing the effectiveness of different hyperparameter configurations without the need for extensive training. Additionally, multifidelity approaches can be employed, where lower-fidelity models are used to guide the search towards promising regions of the hyperparameter space before refining the search with higher-fidelity models. By incorporating these strategies, the hyperparameter search process can be made more efficient, reducing computational costs and accelerating the optimization process. Furthermore, techniques such as early stopping, adaptive learning rates, and dynamic batch sizes can be implemented to enhance the efficiency of the search process and improve the overall optimization outcomes.