14 papers
Joint discovery of governing partial differential equations from multi-source datasets by competitive optimization
Hao Xu, Siyu Lou, Yuntian Chen +1
Discovering governing equations directly from observational data is a key step towards interpretable scientific machine learning. Current data-driven approaches typically operate o…
Data-driven discovery of governing differential equations across physical systems
Siyu Lou, Hao Xu, Wenguan Wang +6
Differential equations play a critical role in scientific discovery because they provide a mathematical framework to describe the behaviour of physical phenomena. As a promising al…
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…
A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems
Xiangnan Yu, Hao Xu, Zhiping Mao +4
In complex physical systems, conventional differential equations often fall short in capturing non-local and memory effects, as they are limited to local dynamics and integer-order…
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…
AutoPV: Automatically Design Your Photovoltaic Power Forecasting Model
Dayin Chen, Xiaodan Shi, Mingkun Jiang +4
Photovoltaic power forecasting (PVPF) is a critical area in time series forecasting (TSF), enabling the efficient utilization of solar energy. With advancements in machine learning…