151 citations · 472 across the 49 of their papers we have counts for
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Beyond empirical models: Discovering new constitutive laws in solids with graph-based equation discovery
Hao Xu, Yuntian Chen, Dongxiao Zhang
Constitutive models are fundamental to solid mechanics and materials science, underpinning the quantitative description and prediction of material responses under diverse loading c…
BuildSTG: A Multi-building Energy Load Forecasting Method using Spatio-Temporal Graph Neural Network
Yongzheng Liu, Yiming Wang, Po Xu +3
Due to the extensive availability of operation data, data-driven methods show strong capabilities in predicting building energy loads. Buildings with similar features often share e…
Capturing Unseen Spatial Heat Extremes Through Dependence-Aware Generative Modeling
Xinyue Liu, Xiao Peng, Shuyue Yan +5
Observed records of climate extremes provide an incomplete view of plausible hazards, missing "unseen" events beyond historical experience. Ignoring spatial dependence further unde…
Generative Discovery of Partial Differential Equations by Learning from Math Handbooks
Hao Xu, Yuntian Chen, Rui Cao +5
Data driven discovery of partial differential equations (PDEs) is a promising approach for uncovering the underlying laws governing complex systems. However, purely data driven tec…
Context-Alignment: Activating and Enhancing LLM Capabilities in Time Series
Yuxiao Hu, Qian Li, Dongxiao Zhang +2
Recently, leveraging pre-trained Large Language Models (LLMs) for time series (TS) tasks has gained increasing attention, which involves activating and enhancing LLMs' capabilities…