44 citations · 66 across the 6 of their papers we have counts for
6 papers
FireBench: A High-fidelity Ensemble Simulation Framework for Exploring Wildfire Behavior and Data-driven Modeling
Qing Wang, Matthias Ihme, Cenk Gazen +2
Background. Wildfire research uses ensemble methods to analyze fire behaviors and assess uncertainties. Nonetheless, current research methods are either confined to simple models o…
Neural Ideal Large Eddy Simulation: Modeling Turbulence with Neural Stochastic Differential Equations
Anudhyan Boral, Zhong Yi Wan, Leonardo Zepeda-Núñez +5
We introduce a data-driven learning framework that assimilates two powerful ideas: ideal large eddy simulation (LES) from turbulence closure modeling and neural stochastic differen…
Accelerating large-eddy simulations of clouds with Tensor Processing Units
Sheide Chammas, Qing Wang, Tapio Schneider +3
Clouds, especially low clouds, are crucial for regulating Earth's energy balance and mediating the response of the climate system to changes in greenhouse gas concentrations. Despi…
A High-resolution Large-eddy Simulation Framework for Wildland Fire Predictions using TensorFlow
Qing Wang, Matthias Ihme, Rod R. Linn +6
As the impact of wildfires has become increasingly more severe over the last decades, there is continued pressure for improvements in our ability to predict wildland fire behavior…
A TensorFlow Simulation Framework for Scientific Computing of Fluid Flows on Tensor Processing Units
Qing Wang, Matthias Ihme, Yi-Fan Chen +1
A computational fluid dynamics (CFD) simulation framework for fluid-flow prediction is developed on the Tensor Processing Unit (TPU) platform. The TPU architecture is featured with…
Deep Learning Models for Predicting Wildfires from Historical Remote-Sensing Data
Fantine Huot, R. Lily Hu, Matthias Ihme +6
Identifying regions that have high likelihood for wildfires is a key component of land and forestry management and disaster preparedness. We create a data set by aggregating nearly…