[Paper Review] The explicit formula for solution of anomalous diffusion equation in the multi-dimensional space
This paper derives an explicit analytical solution for the n-dimensional anomalous diffusion equation with a Gerasimov–Caputo fractional derivative using Laplace and Fourier transforms. By showing equivalence between the fractional diffusion equation and a parabolic integro-differential equation with a Mittag-Leffler kernel, the solution is expressed in terms of Prabhakar fractional integrals and Fox H-functions, providing a closed-form representation for initial-boundary value problems in infinite domains.
This paper intends on obtaining the explicit solution of $n$-dimensional anomalous diffusion equation in the infinite domain with non-zero initial condition and vanishing condition at infinity. It is shown that this equation can be derived from the parabolic integro-differential equation with memory in which the kernel is $t^{-\\alpha}E_{1-\\alpha, 1-\\alpha}(-t^{1-\\alpha}),\\alpha\\in(0, 1),$ where $E_{\\alpha, \\beta}$ is the Mittag-Liffler function. Based on Laplace and Fourier transforms the properties of the Fox H-function and convolution theorem, explicit solution for anomalous diffusion equation is obtained.
Motivation & Objective
- To derive an explicit analytical solution for the n-dimensional anomalous diffusion equation with non-zero initial condition and vanishing boundary conditions at infinity.
- To establish the equivalence between the time-fractional diffusion equation and a parabolic integro-differential equation with a memory kernel involving the Mittag-Leffler function.
- To apply Laplace and Fourier transforms combined with the convolution theorem to obtain a closed-form solution in terms of special functions.
- To express the solution using the Prabhakar fractional integral and Fox H-function, enabling analytical treatment of anomalous diffusion in complex media.
Proposed method
- Use of the Gerasimov–Caputo fractional derivative to model time non-locality in anomalous diffusion.
- Transformation of the problem via Laplace transform in time and Fourier transform in space to decouple the system.
- Application of the convolution theorem to handle the integral terms arising from the fractional derivative and memory kernel.
- Derivation of the resolvent kernel for the memory integral equation using Laplace transform techniques.
- Identification of the memory kernel as $ t^{-eta} E_{1-eta,1-eta}(-t^{1-eta}) $, where $ \beta = \alpha $, linking it to the Mittag-Leffler function.
- Expression of the final solution in terms of the Fox H-function and Prabhakar integral operators through inverse transform inversion.
Experimental results
Research questions
- RQ1Can an explicit analytical solution be derived for the n-dimensional anomalous diffusion equation with a Gerasimov–Caputo fractional derivative?
- RQ2How is the anomalous diffusion equation related to a parabolic integro-differential equation with a Mittag-Leffler kernel?
- RQ3What role do the Laplace and Fourier transforms play in deriving a closed-form solution for such fractional PDEs?
- RQ4How can the Fox H-function and Prabhakar integral be used to represent the solution in a compact, analytically tractable form?
Key findings
- The solution to the n-dimensional anomalous diffusion equation with $ f(x,t) = 0 $ is given explicitly via the Fox H-function and Prabhakar fractional integral operators.
- The integro-differential equation with kernel $ t^{-eta} E_{1-eta,1-eta}(-t^{1-eta}) $ is mathematically equivalent to the time-fractional diffusion equation with the Gerasimov–Caputo derivative.
- The inverse Laplace transform of the resolvent kernel yields $ r(t) = t^{-eta}/\Gamma(1-\beta) $, confirming the connection to fractional calculus.
- The solution satisfies the initial condition $ u(x,0) = g(x) $ and vanishes at infinity in space, as required by the boundary conditions.
- The final solution is expressed as a series involving Fox H-functions: $ G(x,t) = \frac{1}{(2\pi)^{n/2}|x|^n} \sum_{j=0}^\infty \frac{(-t^{1-\alpha})^j}{j!} \left[ H_{2,0}^{1,2}(...) + H_{2,0}^{1,2}(...) \right] $.
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This review was created by AI and reviewed by human editors.