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.

翻译：传统野火模型依赖于刚性的低维参数和静态燃料地图，常常低估火势蔓延速度。为解决这一缺陷，我们提出一种基于JAX实现的混合深度学习参数化概率元胞自动机（CA）框架。该方法采用多尺度卷积神经网络动态生成控制火势蔓延概率、风向对齐和坡度影响的空间变化参数。这种混合设计既能捕捉复杂的非线性环境相互作用，又能保持底层三态CA的物理可解释性。JAX实现支持硬件加速和基于梯度的参数校准。在美国西部六场大规模野火上的评估显示，经过10天数据同化窗口（期间模型逐步拟合观测到的火线）后，该模型在72小时预测范围内仍能保持IoU>0.6；所得预测结果是在观测数据中已编码的抑制策略条件下火灾发展的条件性投影。