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Variational description of statistical field theories using Daubechies' wavelets

1994/02/16 by Christoph Best, Best, Christoph, Andreas Schaefer +1
Computer Science · Environmental Science · Physics and Astronomy · #Blind Source Separation Techniques #Cellular Automata and Lattice Gases (nlin.CG) #FOS: Physical sciences #High Energy Physics - Lattice (hep-lat) #Image and Signal Denoising Methods #Soil Geostatistics and Mapping #comp-gas #hep-lat #nlin.CG

paper · pdf · doi:10.48550/arxiv.hep-lat/9402012

21pp, LaTeX with Postscript figures

arxiv created 1994/02/16 · openalex publication_date 1994/02/16 · arxiv updated 2009/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We investigate the description of statistical field theories using Daubechies' orthonormal compact wavelets on a lattice. A simple variational approach is used to extend mean field theory and make predictions for the fluctuation strengths of wavelet coefficients and thus for the correlation function. The results are compared to Monte Carlo simulations. We find that wavelets provide a reasonable description of critical phenomena with only a small number of variational parameters. This lets us hope for an implementation of the renormalization group in wavelet space.

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