1 citations · 4 across the 27 of their papers we have counts for
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Earth System World Model for What-If Simulations: A Case Study for Terrestrial Ecosystems
Zhihao Wang, Ruichen Wang, Ruohan Li +6
Machine learning emulators have become essential for accelerating expensive Earth-system simulations, but most existing approaches remain passive forecasters: they reproduce simula…
Quantifying Event Impacts on Time Series via Multiscale Contrastive Learning
Yiming Sun, Shengyu Chen, Zhengzhang Chen +3
Shocks that spread through the web, such as cybersecurity breach disclosures, can abruptly disrupt financial time series and cause substantial abnormal losses. While these events a…
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…
Physics-Preserving Latent Compression for Zero-Shot Resolution Transfer in 3D Turbulence
Yilong Dai, Yiming Sun, Yiheng Chen +4
High-resolution turbulence modeling is essential for scientific computing, but remains constrained by the cost of direct numerical simulation and the scarcity of full-resolution da…
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…
Beyond Visual Fidelity: Benchmarking Super-Resolution Models for Large-Scale Remote Sensing Imagery via Downstream Task Integration
Zhili Li, Kangyang Chai, Zhihao Wang +6
Super-resolution (SR) techniques have made major advances in reconstructing high-resolution images from low-resolution inputs. The increased resolution provides visual enhancement…
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…