2025/02/26 by Zeyu Xia, Sibo Cheng · 3 voices
Computer Science · Environmental Science · Mathematics · Physics and Astronomy · #Advanced Data Storage Technologies #Computer Graphics and Visualization Techniques #Fire effects on ecosystems #cs.CE #nlin.CG #physics.comp-ph #stat.CO
paper · pdf · doi:10.1016/j.envsoft.2025.106401
arxiv published 2025/02/26 · openalex publication_date 2025/03/07 · openalex created_date 2025/10/10 · arxiv updated 2026/06/20 · openalex updated_date 2026/08/01
Accurate and rapid prediction of wildfire trends is crucial for effective management and mitigation. However, the stochastic nature of fire propagation poses significant challenges in developing reliable simulators. In this paper, we introduce PyTorchFire , an open-access, PyTorch -based software that leverages GPU acceleration. With our redesigned differentiable wildfire Cellular Automata (CA) model, we achieve millisecond-level computational efficiency, significantly outperforming traditional CPU-based wildfire simulators on real-world-scale fires at high resolution. Real-time parameter calibration is made possible through gradient descent on our model, aligning simulations closely with observed wildfire behavior both temporally and spatially, thereby enhancing the realism of the simulations. Our PyTorchFire simulator, combined with real-world environmental data, demonstrates superior generalizability compared to supervised learning surrogate models. Its ability to predict and calibrate wildfire behavior in real-time ensures accuracy, stability, and efficiency. PyTorchFire has the potential to revolutionize wildfire simulation, serving as a powerful tool for wildfire prediction and management. • The first differentiable cellular automata wildfire spread prediction model. • Ultra-fast, GPU-accelerated wildfire simulator using PyTorch . • Real-time parameter calibration, accurately mimicking real-world fire events. • Superior generalizability compared to supervised learning surrogate models.