14 papers · 1 filter
PIER: Physics-Informed Environmental Retrieval for Time-Series Modeling
Shiyuan Luo, Runlong Yu, Chonghao Qiu +6
Accurate modeling of environmental systems is fundamental to scientific understanding and decision-making, yet remains challenging because observations are limited and physical dyn…
Flow Learners for PDEs: Toward a Physics-to-Physics Paradigm for Scientific Computing
Yilong Dai, Shengyu Chen, Xiaowei Jia +1
Partial differential equations (PDEs) govern nearly every physical process in science and engineering, but solving them at scale remains prohibitively expensive. Generative AI has…
Retrieval-Augmented Multi-scale Framework for County-Level Crop Yield Prediction Across Large Regions
Yiming Sun, Qi Cheng, Licheng Liu +3
This paper proposes a new method for crop yield prediction, which is essential for developing management strategies, informing insurance assessments, and ensuring long-term food se…
Role-Aware Conditional Inference for Spatiotemporal Ecosystem Carbon Flux Prediction
Yiming Sun, Runlong Yu, Rongchao Dong +6
Accurate prediction of terrestrial ecosystem carbon fluxes (e.g., CO, GPP, and CH) is essential for understanding the global carbon cycle and managing its impacts. However,…
Learning PDE Solvers with Physics and Data: A Unifying View of Physics-Informed Neural Networks and Neural Operators
Yilong Dai, Shengyu Chen, Ziyi Wang +4
Partial differential equations (PDEs) are central to scientific modeling. Modern workflows increasingly rely on learning-based components to support model reuse, inference, and int…
GREAT: Generalizable Representation Enhancement via Auxiliary Transformations for Zero-Shot Environmental Prediction
Shiyuan Luo, Chonghao Qiu, Runlong Yu +2
Environmental modeling faces critical challenges in predicting ecosystem dynamics across unmonitored regions due to limited and geographically imbalanced observation data. This cha…