PyTorchFire: A GPU-accelerated wildfire simulator with Differentiable Cellular Automata
Year: 2025
Type: article
Abstract: 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.
Authors: Zeyu Xia, Sibo Cheng
Language: English
Topic: Fire effects on ecosystems
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: hybrid
Estimated APC paid: $3,500
OpenAlex ID: W4408224315