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

arXiv: https://arxiv.org/abs/2502.18738 · HTML: https://mlsanity.com/paper/2502.18738/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2025.

## Built on by (most cited first, 5 of 5)

- [JaxWildfire: A GPU-Accelerated Wildfire Simulator for Reinforcement Learning](https://mlsanity.com/paper/2512.06102/) (2025)
- [Neural-Parameterized Cellular Automata for Wildfire Spread](https://mlsanity.com/paper/2606.11676/) (2026)
- [ScatterPrism: convergence for generative simulation and inverse problems in particle and nuclear physics](https://mlsanity.com/paper/2604.01313/) (2026)
- [Aerial Wildfire Suppression Planning with a Hybrid CNN-Cellular Automata Fire Model](https://mlsanity.com/paper/2606.13633/) (2026)
- [Interpretable Patch-Based Deep Learning for Wildfire Spread Prediction from Ensemble Simulations](https://mlsanity.com/paper/2609.18555/) (2026)

## Abstract (arXiv)

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.