A foundational review of ordinary differential equation solution methods and their inherent symmetries

  • Emmanuel E. Oguadimma Department of Mathematics, Oregon State University, Corvallis, OR 97331, USA.
  • Mohamed A. F. Elbarkawy College of Business, Imam Mohammad Ibn Saud Islamic University (IMSIU)
  • Dominic O. Oranugo Department of Mathematics, Faculty of Physical Sciences, Nnamdi Azikiwe University, P.O. Box 5025 Awka, Nigeria.
  • Heba E. Salem College of Business and Economics, Qassim University and Faculty of Science, Benha University
  • Mustafa Bayram Department of Computer Engineering, Biruni Univetsity, 34010, Istanbul, Turkey
  • Okechukwu J. Obulezi Department of Statistics, Faculty of Physical Sciences, Nnamdi Azikiwe University, Awka, Nigeria

Abstract

This paper presents a focused pedagogical survey of fundamental solution methods for ordinary differential equations (ODEs), demonstrating the unifying and explanatory role of mathematical symmetry. The core of this study is that classical solution techniques are not mere algebraic manipulations but are inherently motivated by the equations' underlying invariant structure. We provide a targeted analysis showing that two critical classes of ODEs—the first-order homogeneous equation and the Cauchy-Euler equation are direct mathematical expressions of scale invariance. The substitution methods used to solve both types of equations are practical applications of exploiting this invariance property to transform the original non-separable equation into a simpler, solvable form. By explicitly reframing these core methods within a symmetry-based context, this work offers advanced practitioners and students a deeper conceptual foundation. This approach unifies seemingly disparate solution techniques under a single, powerful mathematical principle, thereby highlighting the significance of ODEs as mathematical models for analyzing physical systems that exhibit powerful geometrical and variational symmetries

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Published
2026-03-12
Section
Special Issue: Recent Advances and Innovative Statistics with Enhancing Data Sci