A novel optimization-based physics-informed neural network scheme for solving fractional differential equations

Author:

S M Sivalingam,Kumar PushpendraORCID,Govindaraj V.

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,General Engineering,Modeling and Simulation,Software

Reference44 articles.

1. McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5(4):115–133

2. Lagaris IE, Likas A, Fotiadis DI (1998) Artificial neural networks for solving ordinary and partial differential equations. IEEE Trans Neural Networks 9(5):987–1000

3. Kilbas A, Srivastava HM, Trujillo JJ (2006) Theory and applications of fractional differential equations. Elsevier Science

4. Podlubny I (1998) Fractional differential equations: an introduction to fractional derivatives, fractional differential equations, to methods of their solution and some of their applications. Elsevier

5. Losada J, Nieto JJ (2015) Properties of a new fractional derivative without singular kernel. Progress Fract Differ Appl 1(2):87–92

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