Fractional stable diffusion is a type of stochastic process that has gained significant attention in recent years due to its ability to model complex phenomena in various fields such as finance, physics, and biology. This process is characterized by heavy-tailed distributions and long-range dependence, which makes it a suitable tool for modeling non-Gaussian and non-Markovian systems.

In the past, researchers have used fractional Brownian motion (fBm) to model fractional stable diffusion. However, fBm has some limitations, such as being non-stationary and not being able to capture the asymmetry of the distribution. To overcome these limitations, researchers have turned to fractional stochastic differential equations (fSDEs) as an alternative method for modeling fractional stable diffusion.

fSDEs are a type of stochastic differential equation that incorporates fractional calculus. Fractional calculus is a branch of mathematics that deals with derivatives and integrals of non-integer order. By using fractional calculus, fSDEs can capture the long-range dependence and heavy-tailed distributions that are characteristic of fractional stable diffusion.

One of the advantages of using fSDEs to model fractional stable diffusion is that they can capture the asymmetry of the distribution. This is important because many real-world phenomena exhibit asymmetry, such as financial returns and biological data. By capturing the asymmetry, fSDEs can provide a more accurate representation of the underlying system.

Another advantage of using fSDEs is that they are stationary. This means that the statistical properties of the process do not change over time. Stationarity is important because it simplifies the analysis of the process and makes it easier to estimate the parameters.

fSDEs have been used to model fractional stable diffusion in various applications. For example, they have been used to model financial returns, where the heavy-tailed distributions and long-range dependence are well-known phenomena. They have also been used to model biological data, such as the movement of cells, where the non-Gaussian and non-Markovian nature of the process is important.

In addition to modeling fractional stable diffusion, fSDEs have other applications in various fields. For example, they have been used to model turbulence in fluid dynamics, where the long-range dependence and heavy-tailed distributions are also present. They have also been used to model the behavior of particles in complex systems, such as the movement of electrons in semiconductors.

In conclusion, fractional stable diffusion is a powerful tool for modeling complex phenomena in various fields. While fractional Brownian motion has been used in the past to model this process, fractional stochastic differential equations offer several advantages, such as capturing the asymmetry of the distribution and being stationary. fSDEs have been used to model fractional stable diffusion in various applications, including finance, biology, and physics. They also have other applications in fields such as fluid dynamics and semiconductor physics. As research in this area continues, it is likely that fSDEs will become an increasingly important tool for modeling complex systems.