A Probabilistic Approach to Wildfire Spread Prediction Using a Denoising Diffusion Surrogate Model

ArXi:2507.00761v2 Announce Type: replace Thanks to recent advances in generative AI, computers can now simulate realistic and complex natural processes. We apply this capability to predict how wildfires spread, a task made difficult by the unpredictable nature of fire and the variety of environmental conditions it depends on. In this study, We present the first denoising diffusion model for predicting wildfire spread, a new kind of AI framework that learns to simulate fires not just as one fixed outcome, but as a range of possible scenarios.