3 citations · 6 across the 4 of their papers we have counts for
5 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…
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…