JOURNAL ARTICLE

Mathematical modeling and simulation of biophysics systems using neural network.

  • Published In: International Journal of Modern Physics B: Condensed Matter Physics; Statistical Physics; Applied Physics, 2024, v. 38, n. 5. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Ul Rahman, Jamshaid; Makhdoom, Faiza; Ali, Akhtar; Danish, Sana 3 of 3

Abstract

Many of the real-life problems including dynamical structures can be modeled in the shape of differential equations. A number of analytic and numerical methods are being proposed for the solution of these differential equations but for some instances, we may come across a few limitations attached to them. However, theoretically strong and computationally favorable tools such as artificial neural networks can be utilized to approximate the solutions of these differential equations. In this work, we developed mathematical models for some biophysics systems on the basis of their dynamical behavior and opted a neural network having single hidden layer of 50 neurons and Broyden–Fletcher–Goldfarb–Shanno algorithm as an optimizer to simulate the results for population of micro-organisms. The graphical representations of the results obtained both from the neural network and analytic methods are compared for different parameters and we got almost the same results. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Modern Physics B: Condensed Matter Physics; Statistical Physics; Applied Physics. 2024/02, Vol. 38, Issue 5, p1
  • Document Type:Article
  • Subject Area:Physics
  • Publication Date:2024
  • ISSN:0217-9792
  • DOI:10.1142/S0217979224500668
  • Accession Number:175704427
  • Copyright Statement:Copyright of International Journal of Modern Physics B: Condensed Matter Physics; Statistical Physics; Applied Physics is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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