Forward propagation of input uncertainties in physics-based wildfire models is computationally prohibitive, limiting the use of high-fidelity simulators in risk assessment workflows. This work introduces a geometry-aligned bi-fidelity surrogate framework that addresses the convection-dominated nature of wildfire spread by mapping low- and high-fidelity solution snapshots onto a common reference domain prior to basis selection and reconstruction. Unlike conventional bi-fidelity schemes, which combine spatially shifted snapshots and thus suffer from oscillations and excess basis requirements near sharp fronts, the proposed mapping aligns the dominant front geometry through per-variable shift/stretch transforms in 1D and an activity indicator-based affine alignment in 2D, so that reduced bases compare physically corresponding structures rather than displaced ones. Building on the ADfiRe physics-based simulator, we demonstrate the method on 1D and 2D test cases in which low- and high-fidelity models differ in mesh resolution and physical completeness. Across both settings, the geometry-aligned surrogate reproduces full-field temperature and fuel composition with substantially lower error than its unmapped counterpart, eliminates Gibbs-type oscillations near steep gradients, and recovers high-fidelity probability density functions for key quantities of interest (e.g., maximum temperature, evaporated moisture, and burned area). After offline training, online predictions are roughly three orders of magnitude cheaper than direct high-fidelity evaluation, making the framework a practical building block for many-query uncertainty quantification once the offline cost is amortized over enough queries. We discuss the conditions under which the geometric alignment is most effective, its limitations for non-convex or topologically complex fronts, and the path toward validation against real data.

翻译：基于物理的野火模型中输入不确定性的前向传播计算成本过高，限制了高保真度模拟器在风险评估工作流程中的应用。本文引入了一个几何对齐的双保真度代理框架，通过将低、高保真度解快照映射到共同参考域上再进行基选择与重构，解决了野火蔓延中对流主导的特性。与传统的将空间偏移快照组合、从而导致尖锋附近出现振荡及冗余基需求的双保真度方案不同，所提出的映射通过一维中的每变量平移/伸缩变换和二维中基于活动指示器的仿射对齐，对齐了主导锋面的几何结构，从而使降阶基比较的是物理对应的结构而非位移后的结构。基于ADfiRe物理模拟器，我们在低、高保真度模型在网格分辨率与物理完整性上存在差异的一维与二维测试案例中演示了该方法。在这两种设置下，几何对齐代理在复现全场的温度与燃料组分时，误差显著低于未对齐的对应方法，消除了陡梯度附近的吉布斯型振荡，并恢复了关键关注量（如最高温度、蒸发水分量及燃烧面积）的高保真度概率密度函数。离线训练后，在线预测的计算成本约为直接高保真度评估的三个数量级，使得该框架在离线成本通过足够多的查询分摊后，成为面向多次查询不确定性量化的实用构建模块。我们讨论了几何对齐效果最优的条件、对非凸或拓扑复杂锋面的局限性，以及朝向真实数据验证的路径。