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Wavelets As Galerkin Basis Aleksandr Yakovlev Department of Computational Physics St Petersburg State University
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Contents 1.Introduction 2.Galerkin Method 3.Multiresolution analysis 4.Multiresolution analysis and Galerkin Method 5.Solution for an example equations 5.1 Finite differences method 5.2 Wavelet Galerkin solution 5.3 Incorporation of boundary conditions 5.4 Offsetting boundary conditions to control error 5.5 Comparison of results
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Introduction 1.Differential equations ODE PDE constant coefficients variable coefficients 2.Domain and boundary conditions Dirichlet Neuman Cyclic 3.Method Galerkin method 4.Improvements Use Wavelet basis Increase resolution Increase order
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The Galerkin Method
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Multiresolution Analysis
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Daubechies D6 scaling function
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Multiresolution Analysis and Galerkin method
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Finite difference solution to DE
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Finite difference solution to DE (1)
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Wavelet Galerkin Method
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Wavelet Galerkin Method (1)
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Wavelet Galerkin Method (2)
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Wavelet Galerkin Method (3)
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Wavelet Galerkin Method (4)
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Incorporation of boundary conditions
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Offsetting boundary conditions to control error
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D12 wavelet coefficients of the delta function
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Error in wavelet solution to boundary value problem
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Offsetting of boundary sources to control error
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Error in wavelet solution to boundary value problem with offset sources
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Comparsion of results
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Decay in error of wavelet and finite difference solutions with increasing sample size
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Variation of computation time with increasing sample size
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Acknowlegements Prof. S.Yu. Slavyanov, St Petersburg State Univercity Prof. A.V. Tsiganov, St Petersburg State Univercity Prof. S.L. Yakovlev, St Petersburg State Univercity And other colleagues of mine from the Department of Computational Physics
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