Analyzing nonlinear dynamical systems and chaos using machine learning in physical and biological systems

Vaidya, Gendlal; Iqbal, Syed Mohd Uzair; Khan, Mehraj Israr; Gupta, Kapil; Ashirova, Anorgul; Goyal, Shweta

doi:10.47974/JIM-2501

Open Access Research Article

Analyzing nonlinear dynamical systems and chaos using machine learning in physical and biological systems

Gendlal Vaidyagendlalvaidya@gmail.comDepartment of Computer TechnologyYeshwantrao Chavan College of EngineeringNagpur, Maharashtra, 441110, IndiaView full profile → , ^*Syed Mohd Uzair IqbalCorresponding authorsyed.iqbal@symlaw.ac.inSymbiosis Law School Pune (SLS-P)Symbiosis Centre for Advanced Legal Studies and Research (SCALSAR), Symbiosis International (Deemed University)Pune, Maharashtra, 411014, IndiaView full profile → , Mehraj Israr Khanmehrajk_ioe@bkc.met.eduDepartment of Computer ApplicationsBhujbal Knowledge CityInstitute of EngineeringNashik, Maharashtra, 422003, IndiaView full profile → , Kapil Guptakaps04gupta@gmail.comDepartment of Computer EngineeringSt. Vincent Pallotti College of Engineering and TechnologyNagpur, Maharashtra, 441108, IndiaView full profile → , Anorgul Ashirovaashirova_anorgul@mamunedu.uzDepartment of General Professional SciencesMamun UniversityKhiva, 220900, Uzbekistan0000-0002-3399-7527View full profile → , Shweta Goyalshwetugoyal@gmail.comDepartment of Electrical EngineeringGraphic Era (Deemed to be University)Dehradun, Uttarakhand, 248002, IndiaView full profile →

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pp. 663–670Vol. 29Issue 3March 2026DOI: 10.47974/JIM-2501XML

Received:: 01 Apr 2025
Published Online:: 18 Mar 2026
Article type:: Research Article
Language:: EN
Article no.:: JIM-2501
Pages:: 663–670

Abstract

Chaos and nonlinear dynamical systems are important for understanding complicated things that happen in biology and physics. The complexity and sensitivity to starting conditions of these systems make it hard for traditional analysis methods to work with them. In this study used Machine Learning and Deep learning Based method, to successfully look at and predict chaotic and nonlinear behaviours. After data is gathered from real-world and computer-simulated biological and physical systems, it is preprocessed and features are extracted in a way that works best for time-series and spatial datasets.

Keywords

Nonlinear dynamical systems Chaos theory Machine learning Recurrent neural networks Time-series analysis

Subject Classifications

34H1031C45

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