# Neural-Parameterized Cellular Automata for Wildfire Spread

arXiv: https://arxiv.org/abs/2606.11676 · HTML: https://mlsanity.com/paper/2606.11676/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 10 Jun 2026.

## Builds on (most cited first, 10 of 10)

- [A review of machine learning applications in wildfire science and management](https://mlsanity.com/paper/2003.00646/) (2020)
- [A Probabilistic Approach to Wildfire Spread Prediction Using a Denoising Diffusion Surrogate Model](https://mlsanity.com/paper/2507.00761/) (2025)
- [Improved Wildfire Spread Prediction with Time-Series Data and the WSTS+ Benchmark](https://mlsanity.com/paper/2502.12003/) (2025)
- [PyTorchFire: A GPU-Accelerated Wildfire Simulator with Differentiable Cellular Automata](https://mlsanity.com/paper/2502.18738/) (2025)
- [JaxWildfire: A GPU-Accelerated Wildfire Simulator for Reinforcement Learning](https://mlsanity.com/paper/2512.06102/) (2025)
- [Wildfire spread forecasting with Deep Learning](https://mlsanity.com/paper/2505.17556/) (2025)
- [Leveraging Cellular Automata for Real-Time Wildfire Spread Modeling in California](https://mlsanity.com/paper/2510.09708/) (2025)
- [PhysFire-WM: A Physics-Informed World Model for Emulating Fire Spread Dynamics](https://mlsanity.com/paper/2512.17152/) (2025)
- [Advanced Global Wildfire Activity Modeling with Hierarchical Graph ODE](https://mlsanity.com/paper/2601.01501/) (2026)
- [Spatiotemporal Wildfire Prediction and Reinforcement Learning for Helitack Suppression](https://mlsanity.com/paper/2601.14238/) (2026)

## Built on by (most cited first, 1 of 1)

- [Aerial Wildfire Suppression Planning with a Hybrid CNN-Cellular Automata Fire Model](https://mlsanity.com/paper/2606.13633/) (2026)

## Abstract (arXiv)

Traditional wildfire models rely on rigid, low-dimensional parameters and static fuel maps, frequently underpredicting fire spread. To address this weakness, we introduce a hybrid deep-learning parameterized Probabilistic Cellular Automata (CA) framework implemented in JAX. Our approach employs a Multi-Scale Convolutional Neural Network to dynamically generate spatially varying parameters that govern fire-spread probability, wind alignment, and slope influence. This hybrid design captures complex, nonlinear environmental interactions while preserving the physical interpretability of the underlying three-state CA. The JAX implementation enables hardware acceleration and gradient-based parameter calibration. Evaluated on six large-scale wildfires in the western United States, the model maintains IoU > 0.6 over 72-hour forecast horizons after a 10-day data assimilation window during which the model is fitted incrementally to observed perimeters; the resulting forecast is a conditional projection of fire growth under the suppression regime already ncoded in those observations.