Fractal and Fractional, Volume 8, Issue 9
2024 September - 54 articles
Cover Story: Neural fractional differential equations (FDEs) are a neural network architecture designed to fit the solutions of fractional differential equations to data. This architecture combines an analytical component, the fractional derivative, with a neural network component, forming an initial value problem. We investigate the non-uniqueness of the optimal order of the derivative and its interaction with the neural network. We examine how different initialisations and values of the order of the derivative (in the optimisation process) impact its final optimal value. Consequently, neural FDEs do not require a unique α value; instead, they can use a wide range of α values to fit data. This flexibility is beneficial when fitting to given data is required and the underlying physics is not known. View this paper - Issues are regarded as officially published after their release is announced to the table of contents alert mailing list .
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