发表机构
University of Pittsburgh; University of Alabama; University of Minnesota; Aarhus University; University of Wisconsin–Madison; University of Maryland（匹兹堡大学; 阿拉巴马大学; 明尼苏达大学; 奥胡斯大学; 威斯康星大学麦迪逊分校; 马里兰大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对环境系统建模难题，提出PIER框架，通过物理感知流与权重调整机制增强基于嵌入的检索，在对美国中西部湖泊的实验中，该方法在水温及溶解氧预测上表现出色，优于基线且适用于不同主干模型。
AI 中文摘要
准确的环境系统建模对于科学理解和决策至关重要，但因观测有限且物理动态各异而颇具挑战。检索增强方法为跨系统知识转移提供途径，但标准基于嵌入的检索无法保证物理过程一致性。本文提出物理信息环境检索（PIER），这是一个与模型无关的框架，通过物理感知流增强基于嵌入的检索，利用基于物理通量特征训练的局部验证器按通量 - 响应一致性对候选对象评分。权重调整机制学习每个场景的权重，基于总结物理流可靠性的诊断特征自适应平衡两个检索流。对美国中西部41年里356个湖泊的实验表明，PIER在水温及溶解氧预测上持续优于基线，并作为通用增强策略适用于不同主干模型。
英文摘要
Accurate modeling of environmental systems is fundamental to scientific understanding and decision-making, yet remains challenging because observations are limited and physical dynamics vary across systems. Retrieval-augmented approaches offer a natural path to transfer knowledge across systems, but standard embedding-based retrieval does not guarantee consistency of underlying physical processes, since scenarios with similar embeddings may arise from different underlying mechanisms. We propose Physics-Informed Environmental Retrieval (PIER), a model-agnostic framework that augments embedding-based retrieval with a physics-aware stream that scores candidates by flux-response consistency with the target, using local verifiers trained on physics-derived flux features. A weight adjustment mechanism then learns per-scenario weights that adaptively balance the two retrieval streams based on diagnostic features summarizing physics-stream reliability. Experiments on 356 lakes across the Midwestern United States spanning 41 years show that PIER consistently outperforms baselines for water temperature and dissolved oxygen prediction, and serves as a general augmentation strategy across diverse backbones.
