PyTorchFire: A GPU-Accelerated Wildfire Simulator with Differentiable Cellular Automata

TL;DR

PyTorchFire is a GPU-accelerated, differentiable wildfire simulator built on PyTorch that enables real-time, accurate wildfire trend prediction and parameter calibration, outperforming traditional CPU-based models.

Contribution

The paper introduces a novel GPU-accelerated, differentiable cellular automata model for wildfire simulation, enabling real-time calibration and high-resolution predictions.

Findings

Achieves millisecond-level computation speed.

Outperforms traditional CPU-based simulators on real-world fires.

Enables real-time parameter calibration with gradient descent.

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.