151 citations · 472 across the 49 of their papers we have counts for
54 papers
From Score to Diagnosis: Search-Independent Post-Diagnosis for Symbolic Regression
Qian Li, Yuxiao Hu, Juncheng Liu +2
Symbolic regression (SR) solvers employ diverse search mechanisms, yet most rely on the same low-information scalar fitness. Such scores provide little guidance on why a skeleton f…
Beyond Equation Search: Conjecture-Guided Discovery with SCOPE and ClosureBench
Yuxiao Hu, Qian Li, Ding Wang +4
Most symbolic regression methods optimize candidate equations, but improving an equation's fit does not establish whether its underlying scientific conjecture should be retained, r…
Learning to Design SR Solvers: Harness Optimization for Symbolic Regression
Qian Li, Yuxiao Hu, Qihao Duan +2
Symbolic regression (SR) seeks explicit equations that explain observed data, and recent methods increasingly employ large language models (LLMs) and agents for equation discovery.…
DASS: A Solver-Adaptable Dynamic Auxiliary Search Strategy for Symbolic Regression
Yuxiao Hu, Qian Li, Ding Wang +3
Symbolic regression (SR) aims to discover interpretable mathematical expressions from data and plays a key role in scientific discovery. However, existing methods face a common bot…
Constructed regional photovoltaic generation fields improve ultra-short-term distributed solar forecasting
Dayin Chen, Xiaodan Shi, Xin Xu +5
Ultra-short-term forecasting of densely distributed photovoltaic (PV) systems remains challenging because external meteorological and satellite products may not adequately resolve…
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…
CityTrajBench: A Unified Benchmark for City-Scale Vehicle Trajectory Generation
Shibo Zhu, Xiaodan Shi, Dayin Chen +4
Urban trajectory generation is a fundamental task for transportation simulation, urban planning, and mobility analytics. However, systematic comparison across trajectory generation…
Graph-based data-driven discovery of interpretable laws governing corona-induced noise and radio interference for high-voltage transmission lines
Hao Xu, Yuntian Chen, Chongqing Kang +1
The global shift towards renewable energy necessitates the development of ultrahigh-voltage (UHV) AC transmission to bridge the gap between remote energy sources and urban demand.…
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…
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…
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…
When Swarm Learning meets energy series data: A decentralized collaborative learning design based on blockchain
Lei Xu, Yulong Chen, Yuntian Chen +4
Machine learning models offer the capability to forecast future energy production or consumption and infer essential unknown variables from existing data. However, legal and policy…
Optimization of geological carbon storage operations with multimodal latent dynamic model and deep reinforcement learning
Zhongzheng Wang, Yuntian Chen, Guodong Chen +1
Maximizing storage performance in geological carbon storage (GCS) is crucial for commercial deployment, but traditional optimization demands resource-intensive simulations, posing…
A Noise-robust Multi-head Attention Mechanism for Formation Resistivity Prediction: Frequency Aware LSTM
Yongan Zhang, Junfeng Zhao, Jian Li +4
The prediction of formation resistivity plays a crucial role in the evaluation of oil and gas reservoirs, identification and assessment of geothermal energy resources, groundwater…
Cross-variable Linear Integrated ENhanced Transformer for Photovoltaic power forecasting
Jiaxin Gao, Qinglong Cao, Yuntian Chen +1
Photovoltaic (PV) power forecasting plays a crucial role in optimizing the operation and planning of PV systems, thereby enabling efficient energy management and grid integration.…
Discovering an interpretable mathematical expression for a full wind-turbine wake with artificial intelligence enhanced symbolic regression
Ding Wang, Yuntian Chen, Shiyi Chen
The rapid expansion of wind power worldwide underscores the critical significance of engineering-focused analytical wake models in both the design and operation of wind farms. Thes…
A Phone-based Distributed Ambient Temperature Measurement System with An Efficient Label-free Automated Training Strategy
Dayin Chen, Xiaodan Shi, Haoran Zhang +4
Enhancing the energy efficiency of buildings significantly relies on monitoring indoor ambient temperature. The potential limitations of conventional temperature measurement techni…
Promoting AI Equity in Science: Generalized Domain Prompt Learning for Accessible VLM Research
Qinglong Cao, Yuntian Chen, Lu Lu +4
Large-scale Vision-Language Models (VLMs) have demonstrated exceptional performance in natural vision tasks, motivating researchers across domains to explore domain-specific VLMs.…
LLM4ED: Large Language Models for Automatic Equation Discovery
Mengge Du, Yuntian Chen, Zhongzheng Wang +2
Equation discovery is aimed at directly extracting physical laws from data and has emerged as a pivotal research domain. Previous methods based on symbolic mathematics have achieve…