Wildfire behavior predictions typically suffer from significant uncertainty. However, wildfire modeling uncertainties remain largely unquantified in the literature, mainly due to computing constraints. New multifidelity techniques provide a promising opportunity to overcome these limitations. Therefore, this paper explores the applicability of multifidelity approaches to wildland fire spread prediction problems. Using a canonical simulation scenario, we assessed the performance of control variates Monte-Carlo (MC) and multilevel MC strategies, achieving speedups of up to 100x in comparison to a standard MC method. This improvement was leveraged to quantify aleatoric uncertainties and analyze the sensitivity of the fire rate of spread (RoS) to weather and fuel parameters using a full-physics fire model, namely the Wildland-Urban Interface Fire Dynamics Simulator (WFDS), at an affordable computation cost. The proposed methodology may also be used to analyze uncertainty in other relevant fire behavior metrics such as heat transfer, fuel consumption and smoke production indicators.

野火行为预测通常会遭受很大的不确定性。然而，主要由于计算限制，野火模型的不确定性在文献中仍未得到充分量化。新的多保真技术为克服这些局限性提供了一个有前途的机会。因此，本文探讨了多保真方法在野火蔓延预测问题中的适用性。使用规范的模拟方案，我们评估了控制变量蒙特卡洛（MC）和多级MC策略的性能，与标准MC方法相比，加速了100倍。利用全物理火灾模型，利用这一改进来量化不确定的不确定性，并分析传播火率（RoS）对天气和燃料参数的敏感性，，即Wildland-Urban Interface火灾动态模拟器（WFDS），其计算成本可承受。所提出的方法还可以用于分析其他相关火灾行为指标（如传热，燃料消耗和烟雾产生指标）中的不确定性。