1 citations · 3 across the 20 of their papers we have counts for
25 papers
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…
CHAM-net: A Contrastive Hierarchical Adaptive Meta-network for Robust Global Methane Flux Prediction
Rongchao Dong, Yiming Sun, Shuo Chen +4
Methane is a potent greenhouse gas that significantly contributes to global warming. However, accurately estimating global methane emissions and consumption remains challenging due…
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…
AgroFlux: A Spatial-Temporal Benchmark for Carbon and Nitrogen Flux Prediction in Agricultural Ecosystems
Qi Cheng, Licheng Liu, Yao Zhang +3
Agroecosystem, which heavily influenced by human actions and accounts for a quarter of global greenhouse gas emissions (GHGs), plays a crucial role in mitigating global climate cha…
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…
Geo-Aware Models for Stream Temperature Prediction across Different Spatial Regions and Scales
Shiyuan Luo, Runlong Yu, Shengyu Chen +4
Understanding environmental ecosystems is vital for the sustainable management of our planet. However,existing physics-based and data-driven models often fail to generalize to vary…
Learning to Retrieve for Environmental Knowledge Discovery: An Augmentation-Adaptive Self-Supervised Learning Framework
Shiyuan Luo, Runlong Yu, Chonghao Qiu +5
The discovery of environmental knowledge depends on labeled task-specific data, but is often constrained by the high cost of data collection. Existing machine learning approaches u…
RAG for Geoscience: What We Expect, Gaps and Opportunities
Runlong Yu, Shiyuan Luo, Rahul Ghosh +3
Retrieval-Augmented Generation (RAG) enhances language models by combining retrieval with generation. However, its current workflow remains largely text-centric, limiting its appli…
LLM-based Evaluation Policy Extraction for Ecological Modeling
Qi Cheng, Licheng Liu, Qing Zhu +4
Evaluating ecological time series is critical for benchmarking model performance in many important applications, including predicting greenhouse gas fluxes, capturing carbon-nitrog…
Knowledge Guided Encoder-Decoder Framework: Integrating Multiple Physical Models for Agricultural Ecosystem Modeling
Qi Cheng, Licheng Liu, Yao Zhang +5
Agricultural monitoring is critical for ensuring food security, maintaining sustainable farming practices, informing policies on mitigating food shortage, and managing greenhouse g…
Multi-Scale Graph Learning for Anti-Sparse Downscaling
Yingda Fan, Runlong Yu, Janet R. Barclay +4
Water temperature can vary substantially even across short distances within the same sub-watershed. Accurate prediction of stream water temperature at fine spatial resolutions (i.e…
X-MethaneWet: A Cross-scale Global Wetland Methane Emission Benchmark Dataset for Advancing Science Discovery with AI
Yiming Sun, Shuo Chen, Shengyu Chen +9
Methane (CH) is the second most powerful greenhouse gas after carbon dioxide and plays a crucial role in climate change due to its high global warming potential. Accurately mod…
Foundation Models for Environmental Science: A Survey of Emerging Frontiers
Runlong Yu, Shengyu Chen, Yiqun Xie +3
Modeling environmental ecosystems is essential for effective resource management, sustainable development, and understanding complex ecological processes. However, traditional data…
A Survey of Foundation Models for Environmental Science
Runlong Yu, Shengyu Chen, Yiqun Xie +1
Modeling environmental ecosystems is essential for effective resource management, sustainable development, and understanding complex ecological processes. However, traditional meth…
Physics-Guided Foundation Model for Scientific Discovery: An Application to Aquatic Science
Runlong Yu, Chonghao Qiu, Robert Ladwig +3
Physics-guided machine learning (PGML) has become a prevalent approach in studying scientific systems due to its ability to integrate scientific theories for enhancing machine lear…
Physics-Guided Fair Graph Sampling for Water Temperature Prediction in River Networks
Erhu He, Declan Kutscher, Yiqun Xie +4
This work introduces a novel graph neural networks (GNNs)-based method to predict stream water temperature and reduce model bias across locations of different income and education…
Adaptive Process-Guided Learning: An Application in Predicting Lake DO Concentrations
Runlong Yu, Chonghao Qiu, Robert Ladwig +4
This paper introduces a \textit{Process-Guided Learning (Pril)} framework that integrates physical models with recurrent neural networks (RNNs) to enhance the prediction of dissolv…
Urban-Focused Multi-Task Offline Reinforcement Learning with Contrastive Data Sharing
Xinbo Zhao, Yingxue Zhang, Xin Zhang +4
Enhancing diverse human decision-making processes in an urban environment is a critical issue across various applications, including ride-sharing vehicle dispatching, public transp…
When are Foundation Models Effective? Understanding the Suitability for Pixel-Level Classification Using Multispectral Imagery
Yiqun Xie, Zhihao Wang, Weiye Chen +7
Foundation models, i.e., very large deep learning models, have demonstrated impressive performances in various language and vision tasks that are otherwise difficult to reach using…
Evolution-based Feature Selection for Predicting Dissolved Oxygen Concentrations in Lakes
Runlong Yu, Robert Ladwig, Xiang Xu +4
Accurate prediction of dissolved oxygen (DO) concentrations in lakes requires a comprehensive study of phenological patterns across ecosystems, highlighting the need for precise se…
Referee-Meta-Learning for Fast Adaptation of Locational Fairness
Weiye Chen, Yiqun Xie, Xiaowei Jia +4
When dealing with data from distinct locations, machine learning algorithms tend to demonstrate an implicit preference of some locations over the others, which constitutes biases t…
SimFair: Physics-Guided Fairness-Aware Learning with Simulation Models
Zhihao Wang, Yiqun Xie, Zhili Li +4
Fairness-awareness has emerged as an essential building block for the responsible use of artificial intelligence in real applications. In many cases, inequity in performance is due…