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Fractional Calculus in Machine Learning: A Comprehensive Literature Review

Year: 2025

Type: article

Abstract: Fractional calculus generalizes classical differentiation and integration to non-integer orders, enabling models to incorporate long-range dependencies and memory effects. These properties are increasingly relevant in machine learning, especially for problems involving dynamic, nonlocal, or complex systems. This review surveys the mathematical foundations of fractional calculus, its integration into various machine learning paradigms, performance advantages over traditional methods, and the open challenges that motivate future research.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.17212518

Authors: Dipti R Sangpal, Mrs., Vrushali Y Shinde, Mrs.

Language: English

Topic: Fractional Differential Equations Solutions

Subfield: Modeling and Simulation

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7083592591

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