Search Results for author:
Found 4 papers, 0 papers with code
Error analysis for finite element operator learning methods for solving parametric second-order elliptic PDEs
In this paper, we provide a theoretical analysis of a type of operator learning method without data reliance based on the classical finite element approximation, which is called the finite element operator network (FEONet).
Finite Element Operator Network for Solving Parametric PDEs
Partial differential equations (PDEs) underlie our understanding and prediction of natural phenomena across numerous fields, including physics, engineering, and finance.
Convergence analysis of unsupervised Legendre-Galerkin neural networks for linear second-order elliptic PDEs
In this paper, we perform the convergence analysis of unsupervised Legendre--Galerkin neural networks (ULGNet), a deep-learning-based numerical method for solving partial differential equations (PDEs).
A novel approach for wafer defect pattern classification based on topological data analysis
In semiconductor manufacturing, wafer map defect pattern provides critical information for facility maintenance and yield management, so the classification of defect patterns is one of the most important tasks in the manufacturing process.
