151 citations · 472 across the 49 of their papers we have counts for
30 papers · 1 filter
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…
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…
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…
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…
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.…
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…
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…
CVTN: Cross Variable and Temporal Integration for Time Series Forecasting
Han Zhou, Yuntian Chen
In multivariate time series forecasting, the Transformer architecture encounters two significant challenges: effectively mining features from historical sequences and avoiding over…
Bridging Data Barriers among Participants: Assessing the Potential of Geoenergy through Federated Learning
Weike Peng, Jiaxin Gao, Yuntian Chen +1
Machine learning algorithms emerge as a promising approach in energy fields, but its practical is hindered by data barriers, stemming from high collection costs and privacy concern…
Multi-spatial Multi-temporal Air Quality Forecasting with Integrated Monitoring and Reanalysis Data
Yuxiao Hu, Qian Li, Xiaodan Shi +2
Accurate air quality forecasting is crucial for public health, environmental monitoring and protection, and urban planning. However, existing methods fail to effectively utilize mu…
Focus on Hiders: Exploring Hidden Threats for Enhancing Adversarial Training
Qian Li, Yuxiao Hu, Yinpeng Dong +2
Adversarial training is often formulated as a min-max problem, however, concentrating only on the worst adversarial examples causes alternating repetitive confusion of the model, i…
CLeaRForecast: Contrastive Learning of High-Purity Representations for Time Series Forecasting
Jiaxin Gao, Yuxiao Hu, Qinglong Cao +2
Time series forecasting (TSF) holds significant importance in modern society, spanning numerous domains. Previous representation learning-based TSF algorithms typically embrace a c…
Physics-constrained robust learning of open-form partial differential equations from limited and noisy data
Mengge Du, Yuntian Chen, Longfeng Nie +2
Unveiling the underlying governing equations of nonlinear dynamic systems remains a significant challenge. Insufficient prior knowledge hinders the determination of an accurate can…
Worth of knowledge in deep learning
Hao Xu, Yuntian Chen, Dongxiao Zhang
Knowledge constitutes the accumulated understanding and experience that humans use to gain insight into the world. In deep learning, prior knowledge is essential for mitigating sho…