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Found 8 papers, 2 papers with code
A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport
DST3D-UNet-SR is composed of two sequential modules: the temporal module (TM), which predicts the transient evolution of a plume in complex terrain from low-resolution temporal data, and the spatial refinement module (SRM), which subsequently enhances the spatial resolution of the TM predictions.
Recurrent Convolutional Deep Neural Networks for Modeling Time-Resolved Wildfire Spread Behavior
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.
The Bearable Lightness of Big Data: Towards Massive Public Datasets in Scientific Machine Learning
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.
Next Day Wildfire Spread: A Machine Learning Data Set to Predict Wildfire Spreading from Remote-Sensing Data
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.
Ranked #5 on
Flood extent forecasting
on Global Flood forecasting
Interpretable Data-driven Methods for Subgrid-scale Closure in LES for Transcritical LOX/GCH4 Combustion
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.
Convolutional LSTM Neural Networks for Modeling Wildland Fire Dynamics
Machine learning techniques provide a potential approach for developing such models.
Deep Learning Models for Predicting Wildfires from Historical Remote-Sensing Data
Identifying regions that have high likelihood for wildfires is a key component of land and forestry management and disaster preparedness.
Data-assisted combustion simulations with dynamic submodel assignment using random forests
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.
