PIER解决了嵌入检索可能忽略物理过程一致性的问题，在356个湖泊数据上验证了水温和溶氧预测的显著提升。
PIER是一个模型无关的检索增强框架，通过引入物理感知的流来补充标准嵌入检索，利用局部验证器评估候选与目标场景的通量响应一致性。实验基于美国中西部356个湖泊41年的数据，显示PIER在水温和溶解氧预测上持续优于基线方法。该框架可作为通用增强策略适用于多种主干模型。
PIER: Physics-Informed Environmental Retrieval for Time-Series Modeling
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.