2 citations · 4 across the 6 of their papers we have counts for
6 papers
Bivariate partial mapping for detecting causality in complex non-autonomous system
Yang Ni, Changqing Liu, Yifan Zhang +4
Identifying causality is fundamental for human understanding of the world, where complex non-autonomous systems such as species population changes, brain activities, etc. are exten…
A scalable neural bundle map for multiphysics prediction in lithium-ion battery across varying configurations
Zhiwei Zhao, Changqing Liu, Jie Lin +4
Efficient and accurate prediction of Multiphysics evolution across diverse cell geometries is fundamental to the design, management and safety of lithium-ion batteries. However, ex…
Neural Diffeomorphic-Neural Operator for Residual Stress-Induced Deformation Prediction
Changqing Liu, Kaining Dai, Zhiwei Zhao +2
Accurate prediction of machining deformation in structural components is essential for ensuring dimensional precision and reliability. Such deformation often originates from residu…
A general reduced-order neural operator for spatio-temporal predictive learning on complex spatial domains
Qinglu Meng, Yingguang Li, Zhiliang Deng +5
Predictive learning for spatio-temporal processes (PL-STP) on complex spatial domains plays a critical role in various scientific and engineering fields, with its essence being the…
Diffeomorphism Neural Operator for various domains and parameters of partial differential equations
Zhiwei Zhao, Changqing Liu, Yingguang Li +2
In scientific and engineering applications, solving partial differential equations (PDEs) across various parameters and domains normally relies on resource-intensive numerical meth…
Learning Neural Operators on Riemannian Manifolds
Gengxiang Chen, Xu Liu, Qinglu Meng +3
In Artificial Intelligence (AI) and computational science, learning the mappings between functions (called operators) defined on complex computational domains is a common theoretic…