用物理一致性筛选环境数据，提升长时间序列预测精度
PIER: Physics-Informed Environmental Retrieval for Time-Series Modeling
- 引入物理验证流，基于通量响应一致性评估候选数据
- 在356个湖泊41年数据上，水温与溶氧预测均优于基线
- 适合作为多种模型的通用增强框架，尤其适合物理规律强的场景
准确建模环境系统对科学理解与决策至关重要，但受限于观测稀疏性及系统间物理动态差异。检索增强方法虽可跨系统迁移知识，但传统基于嵌入的检索无法保证物理过程的一致性，因相似嵌入可能源于不同机制。本文提出物理感知环境检索（PIER），一种模型无关框架，通过物理感知流以局部验证器计算候选样本与目标的通量-响应一致性得分，并结合诊断特征学习每场景权重，自适应平衡双流检索。在涵盖美国中西部356个湖泊、41年时间跨度的数据上，PIER在水温与溶解氧预测任务中持续优于基线，且可作为多种骨干模型的通用增强策略。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。