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Lecture 10: Anomalous diffusion Outline: generalized diffusion equation subdiffusion superdiffusion fractional Wiener process
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anomalous diffusion Recall derivation of Fokker-Planck equation:
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anomalous diffusion Recall derivation of Fokker-Planck equation:
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anomalous diffusion Recall derivation of Fokker-Planck equation: But what if ?
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anomalous diffusion Recall derivation of Fokker-Planck equation: But what if ? And what if the distribution of time steps has infinite mean?
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anomalous diffusion Recall derivation of Fokker-Planck equation: But what if ? And what if the distribution of time steps has infinite mean? Go back and reformulate the problem:
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continuous-time random walk distribution of jump sizes r(x), distribution of waiting times w(t)
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continuous-time random walk distribution of jump sizes r(x), distribution of waiting times w(t) can have joint distribution ψ(x,t) ; here, ψ(x,t) = r(x)w(t)
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continuous-time random walk distribution of jump sizes r(x), distribution of waiting times w(t) can have joint distribution ψ(x,t) ; here, ψ(x,t) = r(x)w(t) Let η(x,t) = probability density of x at a t right after a jump
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continuous-time random walk distribution of jump sizes r(x), distribution of waiting times w(t) can have joint distribution ψ(x,t) ; here, ψ(x,t) = r(x)w(t) Let η(x,t) = probability density of x at a t right after a jump
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continuous-time random walk distribution of jump sizes r(x), distribution of waiting times w(t) can have joint distribution ψ(x,t) ; here, ψ(x,t) = r(x)w(t) Let η(x,t) = probability density of x at a t right after a jump
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continuous-time random walk distribution of jump sizes r(x), distribution of waiting times w(t) can have joint distribution ψ(x,t) ; here, ψ(x,t) = r(x)w(t) Let η(x,t) = probability density of x at a t right after a jump Then
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continuous-time random walk distribution of jump sizes r(x), distribution of waiting times w(t) can have joint distribution ψ(x,t) ; here, ψ(x,t) = r(x)w(t) Let η(x,t) = probability density of x at a t right after a jump Then ______________ prob to survive from t ’ to t without a jump
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Fourier & Laplace transform: Fourier transform in space, Laplace transform in time:
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Fourier & Laplace transform: Fourier transform in space, Laplace transform in time:
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Fourier & Laplace transform: Fourier transform in space, Laplace transform in time: The conventional case:
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Fourier & Laplace transform: Fourier transform in space, Laplace transform in time: The conventional case:
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Fourier & Laplace transform: Fourier transform in space, Laplace transform in time: The conventional case:
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Fourier & Laplace transform: Fourier transform in space, Laplace transform in time: The conventional case:
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Fourier & Laplace transform: Fourier transform in space, Laplace transform in time: The conventional case:
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Fourier & Laplace transform: Fourier transform in space, Laplace transform in time: The conventional case:
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Fourier-Laplace inversion 2 ways:( D = 1 )
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Fourier-Laplace inversion 2 ways: 1. Invert the Laplace transform first: ( D = 1 )
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Fourier-Laplace inversion 2 ways: 1. Invert the Laplace transform first: ( D = 1 )
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Fourier-Laplace inversion 2 ways: 1. Invert the Laplace transform first: ( D = 1 )
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Fourier-Laplace inversion 2 ways: 1. Invert the Laplace transform first: ( D = 1 )
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Fourier-Laplace inversion 2 ways: 1. Invert the Laplace transform first: ( D = 1 )
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other way: 2. Invert the Fourier transform first:
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other way: 2. Invert the Fourier transform first:
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other way: 2. Invert the Fourier transform first:
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other way: 2. Invert the Fourier transform first:
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other way: 2. Invert the Fourier transform first:
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anomalous diffusion: long waiting times:
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anomalous diffusion: long waiting times: long jumps:
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anomalous diffusion: long waiting times: long jumps: =>
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anomalous diffusion: long waiting times: long jumps: =>
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anomalous diffusion: long waiting times: long jumps: =>
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anomalous diffusion: long waiting times: long jumps: =>
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Subdiffusion: long wait time distribution
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Invert Fourier transform first:
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Subdiffusion: long wait time distribution Invert Fourier transform first:
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Subdiffusion: long wait time distribution Invert Fourier transform first:
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Subdiffusion: long wait time distribution Invert Fourier transform first:
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Subdiffusion: long wait time distribution Invert Fourier transform first:
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Subdiffusion: long wait time distribution Invert Fourier transform first:
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Subdiffusion: long wait time distribution Invert Fourier transform first:
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Subdiffusion: long wait time distribution Invert Fourier transform first: α < 1 : subdiffusion
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long-tailed jump distribution: ( α = 1, σ < 2 )
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long-tailed jump distribution: ( α = 1, σ < 2 ) First invert Laplace transform:
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long-tailed jump distribution: ( α = 1, σ < 2 ) First invert Laplace transform:
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long-tailed jump distribution: ( α = 1, σ < 2 ) First invert Laplace transform:
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long-tailed jump distribution: ( α = 1, σ < 2 ) First invert Laplace transform: S σ = stable distribution of order σ
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long-tailed jump distribution: ( α = 1, σ < 2 ) First invert Laplace transform: S σ = stable distribution of order σ
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long-tailed jump distribution: ( α = 1, σ < 2 ) First invert Laplace transform: S σ = stable distribution of order σ x scales like t 1/σ
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long-tailed jump distribution: ( α = 1, σ < 2 ) First invert Laplace transform: S σ = stable distribution of order σ x scales like t 1/σ (superdiffusion: faster than √t ),
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long-tailed jump distribution: ( α = 1, σ < 2 ) First invert Laplace transform: S σ = stable distribution of order σ x scales like t 1/σ (superdiffusion: faster than √t ), but = ∞.
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long-tailed jump distribution: ( α = 1, σ < 2 ) First invert Laplace transform: S σ = stable distribution of order σ x scales like t 1/σ (superdiffusion: faster than √t ), but = ∞. fractional moments:
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long-tailed jump distribution: ( α = 1, σ < 2 ) First invert Laplace transform: S σ = stable distribution of order σ x scales like t 1/σ (superdiffusion: faster than √t ), but = ∞. fractional moments:
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Fractional Wiener process For an ordinary Wiener process,
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Fractional Wiener process For an ordinary Wiener process, How can we get ?
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Fractional Wiener process For an ordinary Wiener process, How can we get ? Consider
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Fractional Wiener process For an ordinary Wiener process, How can we get ? Consider Then
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Fractional Wiener process For an ordinary Wiener process, How can we get ? Consider Then Laplace-transformed:
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Fractional Wiener process For an ordinary Wiener process, How can we get ? Consider Then Laplace-transformed: so choose
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fractional derivatives
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or
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fractional derivatives or
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fractional derivatives or i.e., or
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fractional derivatives or i.e., or nonlocal!
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