PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
May 11, 2026Journal of Mathematics0 citationsOpen Access

Fractional Adomian J‐Transform Method for Time‐Fractional Diffusion‐Wave Equations: Theory and Applications

View Full Paper
NONazek A. ObeidatMRMahmoud S. RawashdehABAli M. Baniatta

Key Points

  • This research aims to introduce the fractional Adomian transform method as a hybrid analytical-numerical framework for time-fractional diffusion-wave equations.
  • Developed and analyzed the fractional Adomian transform method for nonlocal fractional operators.
  • Derived original transform identities and established convergence criteria in Theorems.
  • Compared the new method against traditional techniques in various fractional diffusion scenarios.
  • The fractional Adomian transform method showed enhanced convergence speed compared to standard models.
  • Demonstrated superior computational efficiency in managing power-law kernels.
  • Validated through multiple fractional diffusion scenarios as an effective symbolic processing tool.

Abstract

In this research, we introduce the fractional Adomian ‐transform method , which functions as a robust hybrid analytical‐numerical framework. Diverging from traditional transform techniques, the leverages the specific scaling attributes of the ‐transform to streamline the inversion of nonlocal fractional operators. We establish a comprehensive mathematical structure by deriving original transform identities in Theorems and defining rigorous convergence criteria and error constraints in Theorems. Our comparative assessment indicates that the provides superior computational efficiency when managing power‐law kernels and enhances symbolic processing. The efficacy of this approach is confirmed through various fractional diffusion scenarios, showing enhanced convergence speed compared to standard and models.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Obeidat et al. (2026) studied this question.

synapsesocial.com/papers/6a0171ed3a9f334c28271f75https://doi.org/10.1155/jom/9121715
Ask AI
Helpful
Bookmark
Share
View Full Paper