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-
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- FuzzyOverlapping authors or contributors · 62%sgl-project/sglang →
“Shared author/contributor keys: luo”
- LinkedLinked via arxiv author · 85%Shiyuan Luo →
“PIER: Physics-Informed Environmental Retrieval for Time-Series Modeling”
- LinkedLinked via arxiv author · 85%Runlong Yu →
“PIER: Physics-Informed Environmental Retrieval for Time-Series Modeling”
- LinkedLinked via arxiv author · 85%Chonghao Qiu →
“PIER: Physics-Informed Environmental Retrieval for Time-Series Modeling”
- LinkedLinked via arxiv author · 85%Yue Qin →
“PIER: Physics-Informed Environmental Retrieval for Time-Series Modeling”
- LinkedLinked via arxiv author · 85%Rahul Ghosh →
“PIER: Physics-Informed Environmental Retrieval for Time-Series Modeling”
- LinkedLinked via arxiv author · 85%Robert Ladwig →
“PIER: Physics-Informed Environmental Retrieval for Time-Series Modeling”