Modelling of Nonlinear Dynamical Systems

A special issue of Modelling (ISSN 2673-3951).

Deadline for manuscript submissions: 31 March 2026 | Viewed by 213

Special Issue Editors


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Guest Editor
Industrial Systems Engineering, University of Regina, Regina, SK S4S 0A2, Canada
Interests: nonlinear dynamics; numerical methods and simulations of nonlinear dynamic systems; diagnosis of nonlinear characteristics; response prediction of nonlinear systems
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Mechanical Engineering, Southwest Petroleum University, Chengdu 610500, China
Interests: dynamics and control; modern design of oil and gas equipment; underground tools and drill bit technology; underground testing and intelligent control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue explores the latest advancements in nonlinear dynamical system modeling. It aims to highlight innovative research and practical applications that leverage nonlinear dynamical system modeling in order to enhance the understanding, analysis, prediction, and control of complex systems. This Special Issue seeks to provide valuable insights for researchers, practitioners, and industry professionals dedicated to deepening their insights into complex phenomena and developing effective solutions through cutting-edge nonlinear dynamical system modeling techniques.

Prof. Dr. Liming Dai
Prof. Dr. Jialin Tian
Guest Editors

Manuscript Submission Information

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Keywords

  • nonlinear dynamical systems
  • data-driven modeling
  • first-principle modeling
  • system identification
  • system control
  • complex systems
  • chaos
  • machine learning

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Published Papers (1 paper)

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Research

15 pages, 1487 KB  
Article
Model-Free Identification of Heat Exchanger Dynamics Using Convolutional Neural Networks
by Mario C. Maya-Rodriguez, Ignacio Carvajal-Mariscal, Mario A. Lopez-Pacheco, Raúl López-Muñoz and René Tolentino-Eslava
Modelling 2025, 6(4), 127; https://doi.org/10.3390/modelling6040127 - 14 Oct 2025
Abstract
Heat exchangers are widely used process equipment in industrial sectors, making the study of their temperature dynamics particularly appealing due to the nonlinearities involved. Model-free approaches enable the use of input and output data to generate specific and accurate estimations for each proposed [...] Read more.
Heat exchangers are widely used process equipment in industrial sectors, making the study of their temperature dynamics particularly appealing due to the nonlinearities involved. Model-free approaches enable the use of input and output data to generate specific and accurate estimations for each proposed system. In this work, a model-free identification strategy is proposed using a convolutional neural network to estimate the system’s behavior. Notably, the model does not rely on direct temperature measurements; instead, temperature is inferred from other system signals such as reference, flow, and control inputs. This data-driven approach offers greater specificity and adaptability, often outperforming manufacturer-provided coefficients whose performance may vary from design expectations. The results yielded an R2 index of 0.9951 under nominal conditions and 0.9936 when the system was subjected to disturbances. Full article
(This article belongs to the Special Issue Modelling of Nonlinear Dynamical Systems)
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