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Mathematical Modeling with Differential Equations in Physics, Chemistry, Biology, and Economics

Edited by
July 2022
150 pages
  • ISBN978-3-0365-4625-4 (Hardback)
  • ISBN978-3-0365-4626-1 (PDF)
https://doi.org/10.3390/books978-3-0365-4626-1
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This book is a reprint of the Special Issue Mathematical Modeling with Differential Equations in Physics, Chemistry, Biology, and Economics that was published in

Computer Science & Mathematics
Engineering
Physical Sciences
Public Health & Healthcare
Summary

This volume was conceived as a Special Issue of the MDPI journal Mathematics to illustrate and show relevant applications of differential equations in different fields, coherently with the latest trends in applied mathematics research. All the articles that were submitted for publication are valuable, interesting, and original. The readers will certainly appreciate the heterogeneity of the 10 papers included in this book and will discover how helpful all the kinds of differential equations are in a wide range of disciplines. We are confident that this book will be inspirational for young scholars as well.

Format
  • Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
q-Hermite polynomials; zeros of q-Hermite polynomials; differential equation; splitted separation; Lie symmetries; gauss hypergeometric functions; initial value problem; Kepler-type orbits; Runge–Kutta; differential evolution; dynamical systems; stability; economics; relationships; networks; initial value problem; oscillatory problems; Runge–Kutta; differential evolution; SEIR ODE model; COVID-19 transmission; convalescent plasma transfusion (CPT); degeneracy; elliptic PDE; ladder operator; commuting operator; eigenvalues; mixing process; simultaneous differential equations; variable production rate; simulated annealing; differential evolution; dynamical systems; financial markets; investment style; border collision bifurcation; fundamental analysis; technical analysis; market maker; differential equations with discontinuous right-hand sides; Hopfield artificial neural networks; stability; n/a