Neural Networks for Models of Forward and Inverse Problems in Fire Spread

This dataset contains fire spread simulations generated with a physics-driven and data-driven model. The data-driven model is trained with a TensorFlow applied to data generated with the physics-driven model described in the paper titled "A simple model for wildland fire vortex–sink interactions". The data are separated into

Data and Resources

Additional Info

Field Value
Last Updated July 4, 2025, 09:33 (UTC)
Created July 4, 2025, 09:33 (UTC)
Source https://wfsi-data.org/view/doi%3A10.60594/W4B88N
award The Role of Vorticity and Fuel Moisture on the Near-Field Plume Structure and Ember Dynamics
creators Bryan Quaife, bquaife@fsu.edu, Florida State University, Department of Scientific Computing, https://orcid.org/0000-0002-9186-8926 | Xin Tong, Florida State University
doi doi:10.60594/W4B88N
encoding utf8
funder U. S. Department of Defense (DoD), Strategic Environmental Research and Development Program (SERDP), http://dx.doi.org/10.13039/100013316
harvest_object_id 568e87f0-a0ac-4f0d-a9ff-b8d3500a5f71
harvest_source_id a2637971-af12-457f-ae4a-831d2202a539
harvest_source_title WIFIRE Commons
maintainor Bryan Quaife, bquaife@fsu.edu
method Generate training and testing data for the first arrival time. In this dataset, we used the method described in: Quaife, B., & Speer, K. (2021). A simple model for wildland fire vortex–sink interactions. Atmosphere, 12(8), 1014, https://www.mdpi.com/2073-4433/12/8/1014.
project Funding Award RC20-1298: The Role of Vorticity and Fuel Moisture on the Near-Field Plume Structure and Ember Dynamics
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temporal {"endTime": "2024-08-31", "startTime": "2023-01-01"}