Data-driven techniques are increasingly being applied to complement physics-based models in fire science. However, the lack of sufficiently large datasets continues to hinder the application of certain machine learning techniques. In this paper, we use simulated data to investigate the ability of neural networks to parameterize dynamics in fire science. In particular, we investigate neural networks that map five key parameters in fire spread to the first arrival time, and the corresponding inverse problem. By using simulated data, we are able to characterize the error, the required dataset size, and the convergence properties of these neural networks. For the inverse problem, we quantify the network’s sensitivity in estimating each of the key parameters. The findings demonstrate the potential of machine learning in fire science, highlight the challenges associated with limited dataset sizes, and quantify the sensitivity of neural networks to estimate key parameters governing fire spread dynamics.

中文翻译：

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数据驱动的火灾建模：使用神经网络学习首次到达时间和模型参数


数据驱动技术越来越多地被应用于补充消防科学中基于物理的模型。然而，缺乏足够大的数据集继续阻碍某些机器学习技术的应用。在本文中，我们使用模拟数据来研究神经网络在消防科学中参数化动力学的能力。特别是，我们研究了将火灾蔓延的五个关键参数映射到首次到达时间的神经网络，以及相应的逆问题。通过使用模拟数据，我们能够描述这些神经网络的误差、所需的数据集大小和收敛特性。对于逆问题，我们在估计每个关键参数时量化了网络的敏感性。这些发现展示了机器学习在火灾科学中的潜力，突出了与有限数据集规模相关的挑战，并量化了神经网络的敏感性，以估计控制火灾蔓延动力学的关键参数。