Numerical Analysis of Deep Learning Methods for the Allen-Cahn and Cahn-Hilliard Models

Date

Authors

Abstract

UNDER EMBARGO

Description

Sinha, Rajen Kumar

Keywords

Allen-Cahn Equations , Cahn-Hilliard Equations , Molecular Beam Epitaxy Equation , Physics-Informed Neural Networks (PINNs) , Operator-Splitting Technique , Metropolis-Hastings-based Sampling , Temporal Domain Decomposition , Transfer Learning , Time-Stacked Training , Error Estimate

URI

https://gyan.iitg.ac.in/handle/123456789/3524

Collections

PhD Theses (Mathematics)

Creative Commons license

https://creativecommons.org/licenses/by-nc-sa/4.0/

Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by-nc-sa/4.0/