发表机构
New York University Abu Dhabi（纽约大学阿布扎比分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种模型无关的物理引导适配器（PA），通过可微分的湿/干响应利用高程作为归纳偏置，实现沿海洪水预测模型向未见区域的高效小样本迁移，显著降低误差。
AI 中文摘要
深度学习代理模型能够以比基于物理的水动力模拟器快数个数量级的速度生成高分辨率的沿海洪水地图，然而将其迁移到新的沿海地区仍然代价高昂，因为生成用于微调的目标区域数据通常需要大量耗时的模拟。为了解决这一瓶颈，我们引入了物理适配器（Physics Adapter, PA），这是一种紧凑且架构无关的适配接口，能够实现洪水预测模型在不同沿海地区间的高效小样本迁移。PA通过一个可微分的湿/干响应来预测峰值水位，该响应将地形高程与学习到的水位进行比较，并通过一个学习的门控机制将这种物理结构化的预测与数据驱动的分支融合。与物理信息公式不同，PA不施加PDE残差或守恒损失，而是将高程作为架构归纳偏置，仅增加可忽略数量的可训练参数。我们将PA集成到12个异构模型中，并在两个具有显著不同几何形状、地形和海岸防护配置的沿海地区进行评估。PA的性能与无物理基线、完全微调以及标准参数高效微调（PEFT）方法进行了基准比较，考虑了区域内对未见海平面上升值的泛化以及区域间迁移。在低样本设置（K=3）下，平均所有骨干网络和迁移设置，与不包含PA的匹配配置相比，仅在冻结骨干网络上适配输出头时，添加PA使均方根误差降低了11.5%；与PEFT方法结合时降低了15.4%；在完全微调下降低了22.9%。综合来看，这项工作的发现为从业者提供了一种具体的方案，用于将基于深度学习的沿海洪水预测器扩展到新的、数据稀缺的地区。
英文摘要
Deep learning surrogates can produce high-resolution coastal flood maps orders of magnitude faster than physics-based hydrodynamic simulators, yet transferring them to new coastal regions remains costly, since generating target-region data for fine-tuning typically requires numerous time-consuming simulations. To tackle this bottleneck, we introduce the Physics Adapter (PA), a compact, architecture-agnostic adaptation interface that enables efficient few-shot transfer of flood prediction models across diverse coastal regions. PA predicts peak water level through a differentiable wet/dry response that compares terrain elevation against a learned water level, and blends this physics-structured prediction with a data-driven branch through a learned gate. Unlike physics-informed formulations, PA imposes no PDE-residual or conservation losses and instead exploits elevation as an architectural inductive bias, adding a negligible number of trainable parameters. We integrate PA into 12 heterogeneous models, and evaluate them on two coastal regions with markedly distinct geometries, topographies, and shoreline protection configurations. The performance of PA is benchmarked against a no-physics baseline, full fine-tuning, and standard parameter-efficient fine-tuning (PEFT) methods, considering both within-region generalization to unseen sea level rise values and between-region transfer. In low-shot regime (K=3), and averaged over all backbones and transfer settings, adding PA reduces root mean square error by 11.5% when only the output head is adapted on a frozen backbone, by 15.4% when combined with PEFT methods, and by 22.9% under full fine-tuning, compared to matched configurations without PA. Taken together, the findings of this work offer practitioners a concrete recipe for extending DL-based coastal flood predictors to new, data-scarce regions.
