no code implementations • 28 Oct 2022 • John Burge, Matthew R. Bonanni, R. Lily Hu, Matthias Ihme
While deep learning approaches have demonstrated the ability to predict wildfire propagation over large time periods, time-resolved fire-spread predictions are needed for active fire management.
1 code implementation • 25 Jul 2022 • Wai Tong Chung, Ki Sung Jung, Jacqueline H. Chen, Matthias Ihme
To illustrate this point, we demonstrate that deep learning models, trained and tested on data from a petascale CFD simulation, are robust to errors introduced during lossy compression in a semantic segmentation problem.
1 code implementation • 4 Dec 2021 • Fantine Huot, R. Lily Hu, Nita Goyal, Tharun Sankar, Matthias Ihme, Yi-fan Chen
To demonstrate the usefulness of this data set, we implement a neural network that takes advantage of the spatial information of this data to predict wildfire spread.
no code implementations • 11 Mar 2021 • Wai Tong Chung, Aashwin Ananda Mishra, Matthias Ihme
Using this data, a priori analysis is performed on the Favre-filtered DNS data to examine the accuracy of physics-based and random forest SGS-models under these conditions.
no code implementations • 11 Dec 2020 • John Burge, Matthew Bonanni, Matthias Ihme, Lily Hu
Machine learning techniques provide a potential approach for developing such models.
no code implementations • 15 Oct 2020 • Fantine Huot, R. Lily Hu, Matthias Ihme, Qing Wang, John Burge, Tianjian Lu, Jason Hickey, Yi-fan Chen, John Anderson
Identifying regions that have high likelihood for wildfires is a key component of land and forestry management and disaster preparedness.
no code implementations • 8 Sep 2020 • Wai Tong Chung, Aashwin Ananda Mishra, Nikolaos Perakis, Matthias Ihme
In this investigation, we outline a data-assisted approach that employs random forest classifiers for local and dynamic combustion submodel assignment in turbulent-combustion simulations.