Wildland fires pose a terrifying natural hazard, underscoring the urgent need to develop data-driven and physics-informed digital twins for wildfire prevention, monitoring, intervention, and response. In this direction of research, this work introduces a physics-informed neural network (PiNN) designed to learn the unknown parameters of an interpretable wildfire spreading model. The considered modeling approach integrates fundamental physical laws articulated by key model parameters essential for capturing the complex behavior of wildfires. The proposed machine learning framework leverages the theory of artificial neural networks with the physical constraints governing wildfire dynamics, including the first principles of mass and energy conservation. Training of the PiNN for physics-informed parameter identification is realized using synthetic data on the spatiotemporal evolution of one- and two-dimensional firefronts, derived from a high-fidelity simulator, as well as empirical data (ground surface thermal images) from the Troy Fire that occurred on June 19, 2002, in California. The parameter learning results demonstrate the predictive ability of the proposed PiNN in uncovering the unknown coefficients of the wildfire model in one- and two-dimensional fire spreading scenarios as well as the Troy Fire. Additionally, this methodology exhibits robustness by identifying the same parameters even in the presence of noisy data. By integrating this PiNN approach into a comprehensive framework, the envisioned physics-informed digital twin will enhance intelligent wildfire management and risk assessment, providing a powerful tool for proactive and reactive strategies.

翻译：野火是一种令人恐惧的自然灾害，凸显了开发数据驱动和物理信息化的数字孪生系统以实现野火预防、监测、干预和响应的迫切需求。在此研究方向下，本文提出了一种基于物理信息的神经网络（PiNN），旨在学习一个可解释的野火蔓延模型中的未知参数。所考虑的建模方法整合了由关键模型参数阐述的基本物理定律，这些参数对于捕捉野火的复杂行为至关重要。所提出的机器学习框架将人工神经网络理论与支配野火动力学的物理约束相结合，包括质量和能量守恒的第一性原理。PiNN的训练用于基于物理信息的参数识别，其实现依赖于从高保真模拟器生成的一维和二维火锋时空演化的合成数据，以及来自2002年6月19日发生在加利福尼亚州的Troy火灾的经验数据（地表热成像）。参数学习结果表明，所提出的PiNN在一维和二维火蔓延场景以及Troy火灾中，具备揭示野火模型未知系数的预测能力。此外，该方法即使在存在噪声数据的情况下也能识别出相同的参数，从而展现出鲁棒性。通过将此PiNN方法整合到一个综合框架中，所设想的基于物理信息的数字孪生系统将提升智能野火管理和风险评估水平，为主动和被动策略提供有力工具。