Shannon Wavelet‐Based Approximation Scheme for Information Entropy Integrals in Confined Domain

ABSTRACT In this work, the author attempted to develop a Shannon wavelet‐based numerical scheme to approximate the information entropies in both configuration and momentum space corresponding to the ground and adjacent excited energy states of one‐dimensional Schrödinger equation appearing in non‐relativistic quantum mechanics. The development of this scheme is based on the judicious use of sinc scale functions as an approximation basis and a suitable numerical quadrature to approximate entropies in position and momentum spaces. Priori and posteriori errors appearing in the approximations of wave functions and entropy integrals have been discussed. The scheme (coded in Python) has been subsequently exercised for various exactly solvable and quasi‐exactly solvable non‐relativistic quantum mechanical models in confined domain.

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Publication Details

Journal
International Journal of Quantum Chemistry
Published
2024-10-28
DOI
https://doi.org/10.1002/qua.27496
Citations
3
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
1.04

Funders

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article

Shannon Wavelet‐Based Approximation Scheme for Information Entropy Integrals in Confined Domain

S. Banik
3 citations
International Journal of Quantum Chemistry
Neural Networks and Applications
1.04
article

Shannon Wavelet‐Based Approximation Scheme for Information Entropy Integrals in Confined Domain

S. Banik
article en
3 citations

Abstract

ABSTRACT In this work, the author attempted to develop a Shannon wavelet‐based numerical scheme to approximate the information entropies in both configuration and momentum space corresponding to the ground and adjacent excited energy states of one‐dimensional Schrödinger equation appearing in non‐relativistic quantum mechanics. The development of this scheme is based on the judicious use of sinc scale functions as an approximation basis and a suitable numerical quadrature to approximate entropies in position and momentum spaces. Priori and posteriori errors appearing in the approximations of wave functions and entropy integrals have been discussed. The scheme (coded in Python) has been subsequently exercised for various exactly solvable and quasi‐exactly solvable non‐relativistic quantum mechanical models in confined domain.

International Journal of Quantum ChemistryVol. 124(21)
Visva-Bharati University (IN)
Council of Scientific and Industrial Research, India
Openalex Percentile: Top 18%
Neural Networks and Applications
1.04
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