8 citations · 8 across the 9 of their papers we have counts for
9 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…
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…
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…
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…
Intelligent Chemical Purification Technique Based on Machine Learning
Wenchao Wu, Hao Xu, Dongxiao Zhang +1
We present an innovative of artificial intelligence with column chromatography, aiming to resolve inefficiencies and standardize data collection in chemical separation and purifica…
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…
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…