HAL (France)
open access
A probabilistic approach to wildfire spread prediction using a denoising diffusion surrogate model
We propose a stochastic framework for wildfire spread prediction using deep generative diffusion models with ensemble sampling. In contrast to traditional deterministic approaches that struggle to capture the inherent uncertainty and variability of wildfire dynamics, our method generates pro
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