Delay Modeling with Conformable and Caputo Derivatives: Analytical and Computational Insights
Yhon Flores, Michel Molina del Sol, Genly Leon, Byron Droguett, Guillermo Fern\'andez-Anaya, and Yoelsy Leyva

TL;DR
This paper compares conformable and Caputo derivatives in fractional delay differential equations, showing conformable derivatives offer more stable and explicit solutions, while Caputo derivatives pose numerical challenges.
Contribution
It introduces analytical and computational methods for delay equations using conformable derivatives and compares them with Caputo-based approaches, highlighting advantages of the conformable formalism.
Findings
Conformable derivatives enable explicit solutions and stable numerical schemes.
Caputo derivatives require complex schemes and are prone to long-term numerical instability.
Comparative experiments favor conformable derivatives for modeling delay phenomena.
Abstract
This work presents an analytical and computational study of fractional-order delay differential equations formulated using both the conformable and Caputo derivatives. For the conformable case, we develop the associated integral, exponential function, and Laplace transform, showing how the conformable Laplace framework preserves algebraic structure and facilitates explicit solutions. Delay terms are treated through series expansions and transform-based methods, ensuring causal and finite representations. In parallel, Caputo-based formulations are examined, highlighting the challenges posed by convolutional memory kernels and the potential for long-term numerical instability. Numerical implementations are carried out using mesh-aligned algorithms: Euler and Runge--Kutta schemes for conformable dynamics, and Euler, L2--, and a series--anchored predictor--corrector method for…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
