학술논문

Numerical Mesh Truncation Boundary Conditions Optimized via Machine Learning
Document Type
Conference
Source
2019 International Applied Computational Electromagnetics Society Symposium (ACES) Applied Computational Electromagnetics Society Symposium (ACES), 2019 International. :1-2 Apr, 2019
Subject
Fields, Waves and Electromagnetics
Finite element analysis
Boundary conditions
Prediction algorithms
Machine learning
Machine learning algorithms
Patch antennas
Language
Abstract
Mesh truncation in Finite Element Method (FEM) is critical and presents conflicting options. Boundary Integral (BI) option yields very accurate results but results in non-sparse solution matrix while Artificial Absorbers (AAs) or Absorbing Boundary Conditions (ABCs) preserves sparsity of the matrix, they need to be placed sufficiently far away from the structure to yield accurate data, resulting in large number of unknowns. Paper presents a method to optimize the placement of the truncation boundary close to the object being modeled to minimize the size of the problem and the run time Optimization is object-dependent as it should be and is achieved using Machine Learning (ML). The proposed scheme is promising but needs further study to fully explore its potential to be of practical use.