A novel machine learning framework to approximate modified Bessel functions
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Anuwedita Singh
Abstract
This article presents the precise approximations for modified Bessel functions of the second kind. Building on a well-established method for function approximation, the enhanced approach, incorporating machine learning techniques, is used to approximate Bessel functions such as K0, K1, K1/4, I0, I1, and I1/3. These approximations are demonstrated to be highly effective in various applications, such as thermal average and partition function, providing a robust alternative to repeated numerical integration.
Acknowledgments
I would like to thank Mr. Aviv Orly for the helpful discussion.
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Research ethics: This article does not contain any studies with human participants or animals performed by any of the authors.
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Informed consent: This study did not involve human participants, and informed consent was therefore not required.
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Author contributions: Anuwedita Singh: Conceptualization; methodology; investigation; writing original draft; writing review editing.
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Use of Large Language Models, AI and Machine Learning Tools: OpenAI, GPT-3 was used solely for grammar correction and language refinement.
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Conflict of interest: The author certify that they have no conflict of interest in the subject or materials discussed in this manuscript.
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Research funding: There is no grant for this article.
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Data availability: Data sharing does not apply to this article, as no datasets were generated or analyzed during the current study.
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