Dataset underlying the publication: "Efficient deep learning surrogate method for predicting the transport of particle patches in coastal environments"

DOI:10.4121/6929d89f-e8cb-463d-b490-3265132841f5.v1
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DOI: 10.4121/6929d89f-e8cb-463d-b490-3265132841f5

Datacite citation style

Fajardo-Urbina, Jeancarlo Manuel; Lui, Yang; Georgievska, Sonja; Grawe, Ulf; Herman Clercx et. al. (2024): Dataset underlying the publication: "Efficient deep learning surrogate method for predicting the transport of particle patches in coastal environments". Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/6929d89f-e8cb-463d-b490-3265132841f5.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

The data provided in this repository can be used to run the surrogate and optimal prediction experiments described in the manuscript "Efficient deep learning surrogate method for predicting the transport of particle patches in coastal environments". This paper introduces a revolutionary tool for forecasting the spread of tracers or pollutants in our oceans. We have developed a unique surrogate modeling method that combines the power of deep learning with physical oceanographic understanding. This translates to accurate forecasts that achieve at least two orders of magnitude faster than traditional systems – once the deep learning model is trained. In our paper, the experiment "surrogate prediction" is used to assess the performance of our current deep learning approach, whereas the experiment "optimal prediction" shows what can be achieved if a perfect deep learning prediction is obtained. A small sample of the data is also stored in the GitHub repository (https://github.com/JeancarloFU/paper_Efficient_Deep_Learning_Surrogate_Method_For_Lagrangian_Transport). Here, scripts, and notebooks (based on Python v3.8) used to run the surrogate and optimal prediction experiments described in the manuscript are archived.

History

  • 2024-11-08 first online, published, posted

Publisher

4TU.ResearchData

Format

NetCDF

Organizations

- Fluids and Flows group, Department of Applied Physics, Eindhoven University of Technology.
- Leibniz Institute for Baltic Sea Research Warnemunde, Rostock, Germany.
- Department of Estuarine and Delta Systems, NIOZ Royal Netherlands Institute for Sea Research.
- Netherlands eScience Center, Amsterdam, Netherlands.

DATA

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