40 citations · 63 across the 24 of their papers we have counts for
25 papers
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…
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.…
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…
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…
AS-XAI: Self-supervised Automatic Semantic Interpretation for CNN
Changqi Sun, Hao Xu, Yuntian Chen +1
Explainable artificial intelligence (XAI) aims to develop transparent explanatory approaches for "black-box" deep learning models. However,it remains difficult for existing methods…
A knowledge-based data-driven (KBDD) framework for all-day identification of cloud types using satellite remote sensing
Longfeng Nie, Yuntian Chen, Mengge Du +2
Cloud types, as a type of meteorological data, are of particular significance for evaluating changes in rainfall, heatwaves, water resources, floods and droughts, food security and…
QIENet: Quantitative irradiance estimation network using recurrent neural network based on satellite remote sensing data
Longfeng Nie, Yuntian Chen, Dongxiao Zhang +2
Global horizontal irradiance (GHI) plays a vital role in estimating solar energy resources, which are used to generate sustainable green energy. In order to estimate GHI with high…
Interpretable AI-Driven Discovery of Terrain-Precipitation Relationships for Enhanced Climate Insights
Hao Xu, Yuntian Chen, Zhenzhong Zeng +3
Despite the remarkable strides made by AI-driven models in modern precipitation forecasting, these black-box models cannot inherently deepen the comprehension of underlying mechani…
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…
Discrete Point-wise Attack Is Not Enough: Generalized Manifold Adversarial Attack for Face Recognition
Qian Li, Yuxiao Hu, Ye Liu +3
Classical adversarial attacks for Face Recognition (FR) models typically generate discrete examples for target identity with a single state image. However, such paradigm of point-w…
DISCOVER: Deep identification of symbolically concise open-form PDEs via enhanced reinforcement-learning
Mengge Du, Yuntian Chen, Dongxiao Zhang
The working mechanisms of complex natural systems tend to abide by concise and profound partial differential equations (PDEs). Methods that directly mine equations from data are ca…
Interpretable machine learning optimization (InterOpt) for operational parameters: a case study of highly-efficient shale gas development
Yuntian Chen, Dongxiao Zhang, Qun Zhao +1
An algorithm named InterOpt for optimizing operational parameters is proposed based on interpretable machine learning, and is demonstrated via optimization of shale gas development…