The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/6929d89f-e8cb-463d-b490-3265132841f5

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

Dataset

• Oceanography
• Geophysics
• Physical Geography and Environmental Geoscience
• Earth Sciences

Coastal systems, Deep learning surrogate modeling, Dutch Wadden Sea, Lagrangian advection and dispersion, Lagrangian pollution modeling

CC BY 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Jeancarlo Manuel Fajardo-Urbina , Yang Lui , Sonja Georgievska , Ulf Grawe , Herman Clercx , Theo Gerkema , Matias Duran-Matute

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.

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

4TU.ResearchData

NetCDF

• https://github.com/JeancarloFU/paper_Efficient_Deep_Learning_Surrogate_Method_For_Lagrangian_Transport

- 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.