Web: http://arxiv.org/abs/2206.08523

June 23, 2022, 1:11 a.m. | Andrew Bolt, Carolyn Huston, Petra Kuhnert, Joel Janek Dabrowski, James Hilton, Conrad Sanderson

cs.LG updates on arXiv.org arxiv.org

Computational simulations of wildfire spread typically employ empirical
rate-of-spread calculations under various conditions (such as terrain, fuel
type, weather). Small perturbations in conditions can often lead to significant
changes in fire spread (such as speed and direction), necessitating a
computationally expensive large set of simulations to quantify uncertainty.
Model emulation seeks alternative representations of physical models using
machine learning, aiming to provide more efficient and/or simplified surrogate
models. We propose a dedicated spatio-temporal neural network based framework
for model emulation, …

arxiv forecasting lg models network neural neural network temporal

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