4 papers
GAIA: Geometry-Adaptive Operator Learning for Forward and Inverse Problems
Meenakshi Krishnan, Pranav Pulijala, Ke Chen +2
Operator learning for partial differential equations (PDEs) on arbitrary geometries builds fast neural surrogates for large-scale simulation. Although recent geometry-adaptive neur…
Data Completion for Electrical Impedance Tomography by Conditional Diffusion Models
Ke Chen, Haizhao Yang, Chugang Yi
Data scarcity is a fundamental barrier in Electrical Impedance Tomography (EIT), as undersampled Dirichlet-to-Neumann (DtN) measurements can substantially degrade conductivity reco…
Neural Correction Operator: A Reliable and Fast Approach for Electrical Impedance Tomography
Amit Bhat, Ke Chen, Chunmei Wang
Electrical Impedance Tomography (EIT) is a non-invasive medical imaging method that reconstructs electrical conductivity mediums from boundary voltage-current measurements, but its…
Error analysis for learning the time-stepping operator of evolutionary PDEs
Ke Chen, Meenakshi Krishnan, Haizhao Yang
Deep neural networks (DNNs) have recently emerged as effective tools for approximating solution operators of partial differential equations (PDEs) including evolutionary problems.…