Wildland fires pose terrifying natural hazards, 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) to learn the unknown parameters of an interpretable wildfire spreading model. The considered wildfire spreading model integrates fundamental physical laws articulated by key model parameters, essential for capturing the complex behavior of wildfires. The proposed machine learning approach leverages the theory of artificial neural networks with the physical constraints governing wildfire dynamics, such as the first principles of mass and energy conservation. Training of the PiNN for physics-informed parameter identification is realized using data of the temporal evolution of one- and two-dimensional (plane surface) fire fronts that have been obtained from a high-fidelity simulator of the wildfire spreading model under consideration. The parameter learning results demonstrate the remarkable predictive ability of the proposed PiNN in uncovering the unknown coefficients in both the one- and two-dimensional fire spreading scenarios. Additionally, this methodology exhibits robustness by identifying the same parameters in the presence of noisy data. The proposed framework is envisioned to be incorporated in a physics-informed digital twin for intelligent wildfire management and risk assessment.

翻译：野火是令人恐惧的自然灾害，这凸显了开发数据驱动且融入物理信息的数字孪生系统以用于野火预防、监测、干预和响应的迫切需求。在此研究方向下，本文引入了一种物理信息神经网络（PiNN），用于学习一个可解释的野火蔓延模型中的未知参数。所考虑的野火蔓延模型整合了由关键模型参数阐述的基本物理定律，这些定律对于捕捉野火的复杂行为至关重要。所提出的机器学习方法结合了人工神经网络理论与支配野火动力学的物理约束，例如质量和能量守恒的第一性原理。PiNN 的物理信息参数识别训练，是利用一维和二维（平面）火锋随时间演化的数据实现的，这些数据来自所考虑野火蔓延模型的高保真度模拟器。参数学习结果表明，所提出的 PiNN 在一维和二维火蔓延场景中揭示未知系数方面具有卓越的预测能力。此外，该方法在存在噪声数据的情况下仍能识别出相同的参数，展现了其鲁棒性。所提出的框架预计将被整合到一个物理信息数字孪生系统中，用于智能野火管理和风险评估。