8 citations · 8 across the 9 of their papers we have counts for
4 papers · 1 filter
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…
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…