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.

中文翻译：

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用于野火蔓延参数学习的物理信息神经网络


野火构成了可怕的自然灾害，凸显了开发数据驱动和基于物理信息的数字孪生的迫切需求，用于野火预防、监测、干预和响应。在这个研究方向上，这项工作引入了一个物理信息神经网络 （PiNN），旨在学习可解释的野火蔓延模型的未知参数。经过深思熟虑的建模方法集成了由关键模型参数阐明的基本物理定律，这些参数对于捕获野火的复杂行为至关重要。拟议的机器学习框架利用人工神经网络理论，其中物理约束控制野火动力学，包括质量和能量守恒的第一原则。使用来自高保真模拟器的一维和二维火线时空演变的合成数据，以及 2002 年 6 月 19 日加利福尼亚州特洛伊大火的经验数据（地表热图像），实现用于物理信息参数识别的 PiNN 训练。参数学习结果表明，所提出的 PiNN 在揭示野火模型在一维和二维火灾蔓延情景以及特洛伊大火中的未知系数方面的预测能力。此外，这种方法即使在存在嘈杂数据的情况下也能识别相同的参数，从而表现出稳健性。通过将这种 PiNN 方法集成到一个综合框架中，设想中的物理信息数字孪生将增强智能野火管理和风险评估，为主动和被动策略提供强大的工具。