3 citations · 7 across the 5 of their papers we have counts for
6 papers
Geometry-physics confounding impairs PDE learning across varying domains
Yinghao Cheng, Gengxiang Chen, Xu Liu +6
Learning partial differential equation (PDE) dynamics across varying domains is central to predictive modelling and data-driven discovery of governing equations. However, geometric…
Shape-morphing programming of soft materials on complex geometries via neural operator
Lu Chen, Gengxiang Chen, Xu Liu +4
Shape-morphing soft materials can enable diverse target morphologies through voxel-level material distribution design, offering significant potential for various applications. Desp…
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…
Residual fourier neural operator for thermochemical curing of composites
Gengxiang Chen, Yingguang Li, Xu liu +3
During the curing process of composites, the temperature history heavily determines the evolutions of the field of degree of cure as well as the residual stress, which will further…