Neural network time series forecasting of finite-element mesh adaptation

Larry Manevitz, Akram Bitar, Dan Givoli

Research output: Contribution to journalArticlepeer-review


Basic learning algorithms and the neural network model are applied to the problem of mesh adaptation for the finite-element method for solving time-dependent partial differential equations. Time series prediction via the neural network methodology is used to predict the areas of "interest" in order to obtain an effective mesh refinement at the appropriate times. This allows for increased numerical accuracy with the same computational resources as compared with more "traditional" methods.

Original languageEnglish
Pages (from-to)447-463
Number of pages17
Issue numberSPEC. ISS.
StatePublished - Jan 2005

Bibliographical note

Funding Information:
Supported in part by the HIACS Research Center, the University of Haifa.


  • Finite-element method
  • Mesh adaptation
  • Neural networks
  • Time series prediction
  • Time-dependent PDEs

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence


Dive into the research topics of 'Neural network time series forecasting of finite-element mesh adaptation'. Together they form a unique fingerprint.

Cite this