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Caputo-Tempered Derivative Overview

Updated 19 January 2026
  • Caputo-Tempered derivative is a modified fractional derivative that uses an exponentially truncated kernel to bridge anomalous (power-law) and normal (Gaussian) diffusion.
  • Its formulation employs Laplace and Fourier transforms, ensuring a rigorous connection between fractional calculus and classical diffusion models.
  • The derivative enables finite-variance modeling in fractional Cattaneo equations, mitigating unrealistic long-range jumps in transport phenomena.

The Caputo-Tempered derivative is a modification of the classical Caputo space-fractional derivative, in which the underlying convolution kernel is exponentially truncated. This construction systematically incorporates an exponential tempering parameter λ>0\lambda>0 into the fractional calculus framework, interpolating between strictly power-law memory and short-range exponential decay. As formalized in the context of space-fractional transport equations, notably the Cattaneo (telegrapher’s) equation, the Caputo-Tempered derivative enables the description of phenomena exhibiting a crossover from anomalous (fractional) to normal (Gaussian) diffusion, thereby providing a physically realistic mechanism suppressing unrealistically long-range jumps present in pure Caputo-based models (Beghin et al., 2022).

1. Formal Definition and Integral Representations

Let fC1([0,))f\in C^1([0,\infty)) with f(0)f(0) finite, 0<α<10<\alpha<1, and λ>0\lambda>0. The Caputo-Tempered derivative of order α\alpha and tempering λ\lambda is defined as

Dxα,λf(x):=eλx  DxC,α[eλxf(x)]=eλxΓ(1α)0x(xy)αddy(eλyf(y))dy.D_{x}^{\alpha,\lambda}\,f(x) := e^{-\lambda x}\;D_{x}^{C,\alpha}\bigl[e^{\lambda\,x}\,f(x)\bigr] = \frac{e^{-\lambda x}}{\Gamma(1-\alpha)} \int_{0}^{x} (x-y)^{-\alpha} \frac{d}{dy}\bigl(e^{\lambda y}f(y)\bigr)\,dy.

The tempered fractional integral of order γ>0\gamma>0 is

Ixγ,λf(x)=1Γ(γ)0x(xy)γ1eλ(xy)f(y)dy,I_x^{\gamma,\lambda}f(x) = \frac{1}{\Gamma(\gamma)} \int_0^x (x-y)^{\gamma-1}\,e^{-\lambda(x-y)}\,f(y)\,dy,

providing the alternative forms

fC1([0,))f\in C^1([0,\infty))0

An explicit representation reflecting the tempering property is

fC1([0,))f\in C^1([0,\infty))1

where the exponential fC1([0,))f\in C^1([0,\infty))2 truncates the algebraic kernel.

2. Laplace and Fourier Transform Symbols

Under the one-sided Laplace transform,

fC1([0,))f\in C^1([0,\infty))3

the Caputo-Tempered derivative satisfies

fC1([0,))f\in C^1([0,\infty))4

If the domain is extended to all real fC1([0,))f\in C^1([0,\infty))5 with vanishing boundary terms at fC1([0,))f\in C^1([0,\infty))6, the Fourier transform yields

fC1([0,))f\in C^1([0,\infty))7

where fC1([0,))f\in C^1([0,\infty))8 is the Fourier transform of fC1([0,))f\in C^1([0,\infty))9. The transform symbol reduces to the classical form f(0)f(0)0 in the limit f(0)f(0)1.

3. Application in the Time-Fractional Cattaneo Equation

The Caputo-Tempered derivative is employed within a generalized form of the Cattaneo (telegrapher's) equation, a canonical model for heat transfer and wave propagation. In this context, the second spatial derivative in the classical model

f(0)f(0)2

is replaced by f(0)f(0)3, and the time derivative is fractionalized using the Caputo derivative of order f(0)f(0)4. The equation reads

f(0)f(0)5

subject to the initial and boundary conditions

f(0)f(0)6

With the kernel f(0)f(0)7, the spatial flux law acquires an exponentially truncated memory, providing a continuous interpolation between ballistic (wave-like) and diffusive transport regimes.

4. Solution: Characteristic Function and Stochastic Process

The Fourier transform in f(0)f(0)8 and Laplace transform in f(0)f(0)9 reduce the time-fractional Cattaneo equation to a solvable algebraic form. Define

0<α<10<\alpha<10

Given the symbol 0<α<10<\alpha<11 and Caputo time-fractional symbol 0<α<10<\alpha<12, one obtains

0<α<10<\alpha<13

Factoring the denominator with 0<α<10<\alpha<14, the solution for the characteristic function is

0<α<10<\alpha<15

where 0<α<10<\alpha<16 is the Mittag–Leffler function. 0<α<10<\alpha<17 serves as the characteristic function of the associated random motion 0<α<10<\alpha<18,

0<α<10<\alpha<19

with λ>0\lambda>00 a tempered stable subordinator (generator λ>0\lambda>01) and λ>0\lambda>02 its inverse of index λ>0\lambda>03. This construction yields finite moments for all orders and reproduces the two-term Mittag–Leffler mixture in closed form.

5. Tempering vs. Classical Caputo Fractional Derivative

A principal distinction arises between the tempered (λ>0\lambda>04) and un-tempered (λ>0\lambda>05) cases:

  • With λ>0\lambda>06, λ>0\lambda>07 reduces to the standard Caputo or Riemann–Liouville space-fractional derivative, possessing algebraically decaying kernel λ>0\lambda>08 and symbol λ>0\lambda>09, admitting long-range “jumps” in underlying Lévy processes.
  • For α\alpha0, the exponential cutoff α\alpha1 suppresses the kernel’s tail, producing the transform symbol

α\alpha2

leading to exponential damping at large α\alpha3 or α\alpha4. This tempering imparts finite moments of all orders and causes a continuous transition from fractional to standard Gaussian behavior with increasing scale.

  • In the Cattaneo equation, tempering tunes the transition between ballistic and diffusive dynamics, mitigating the issue of infinite variance and unrealistic long-distance propagation characteristic of pure power-law kernels.

6. Physical Interpretation and Modeling Implications

The Caputo-Tempered derivative modifies the nonlocal spatial memory inherent in fractional models, enabling finite-variance stochastic descriptions and reconciling anomalous transport with classical diffusion over large domains. Within the time-fractional Cattaneo equation, the derivative yields a solution characterized by a two-term Mittag–Leffler mixture for the process characteristic function. This formulation precisely interpolates between the limiting regimes—wave-like, ballistic motion at short time or length scales, and normal diffusion at longer times or distances. The exponential truncation, both in real-space convolution and transform domains, is essential for capturing crossover phenomena and constraining anomalous propagation in physical models (Beghin et al., 2022).

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