Neural Nets for Mesh Assessment

Abstract

We investigate the construction, training and application of a neural net for assessing element shape quality of practical unstructured grids arising in mesh generation, adaptive refinement and moving grid applications. Results of numerical experiments are included to validate the process and demonstrate performance of the neural net for both triangulations in 2D and tetrahedral tessellation in 3D.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2005
Accession Number
ADA437983

Entities

People

  • Graham F. Carey
  • Saeed Iqbal

Organizations

  • University of Texas at Austin

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Computational Fluid Dynamics
  • Computational Science
  • Computers
  • Data Sets
  • Demographic Cohorts
  • Information Theory
  • Machine Learning
  • Neural Networks
  • Simulations
  • Supervised Machine Learning
  • Test Sets
  • Three Dimensional
  • Training

Readers

  • Computational Fluid Dynamics (CFD)
  • Instructional Design and Training Evaluation.
  • Neuroscience