
Advances in Computational Methods and Modeling for Science and Engineering
- 1st Edition - February 4, 2025
- Imprint: Morgan Kaufmann
- Editors: Hari M Srivastava, Geeta Arora, Firdous Shah
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 3 0 0 1 2 - 7
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 3 0 0 1 3 - 4
Advances in Computational Methods and Modelling in Science and Engineering explores the application of computational techniques and modeling approaches in science and engine… Read more

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Request a sales quoteThe mathematical treatment of these models is very helpful in understanding these models and their real-world applications. The methods and modeling techniques presented are useful for mathematicians, engineers, scientists, and researchers working on the mathematical treatment of models in a wide range of applications, including disciplines such as engineering, physics, chemistry, computer science, and applied mathematics.
- Provides comprehensive coverage of computational methods and modeling techniques applicable to science and engineering
- Emphasizes practical application by providing real-world examples
- Offers practical guidance and step-by-step examples to help readers overcome challenges related to implementing algorithms, interpreting results, and effectively applying computational methods in their work
- Title of Book
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- About the editors
- A: Computational methods
- Chapter 1: Exploring new traveling and solitary wave solutions of conformable fractional (3+1)-D BLMP differential equations
- 1.1. Introduction
- 1.2. Description of the mERF method
- 1.3. Application to conformable fractional (3+1)-D BLMP models
- 1.4. Physical explanation of solutions
- 1.5. Conclusion
- Chapter 2: Numerical investigation of fractional-order brain tumor growth model via Fibonacci wavelets
- 2.1. Introduction
- 2.2. Formulation of the fractional-order brain tumor growth model
- 2.2.1. Basics of fractional calculus
- 2.2.2. Formulation of the time-fractional brain tumor growth model
- 2.3. Wavelets and function approximation
- 2.3.1. Fibonacci polynomials and wavelets
- 2.3.2. Conventional Haar wavelets
- 2.3.3. Operational matrices associated with Fibonacci and Haar wavelets
- 2.3.4. Quasi-linearization technique for linearization
- 2.4. Method of solution via Fibonacci wavelets
- 2.4.1. Solution of the brain tumor growth model via Fibonacci wavelets
- 2.4.2. Solution of the brain tumor growth model via Haar wavelets
- 2.5. Numerical simulation and discussion
- 2.6. Epilogue
- Chapter 3: Application of dynamical system analysis in cosmology
- 3.1. Introduction
- 3.2. Dynamical systems
- 3.2.1. Introduction to dynamical systems
- 3.3. Modern cosmology
- 3.3.1. Friedmann model
- 3.4. Application of dynamical systems
- 3.4.1. ΛCDM
- 3.4.2. Canonical scalar field
- 3.5. Conclusion
- Chapter 4: Polynomial-based fractional methods of wavelets and their applications
- 4.1. Introduction
- 4.2. Preliminaries
- 4.3. Fractional-order Fibonacci wavelets
- 4.4. Description of the fractional-Fibonacci wavelet scheme
- 4.5. Numerical examples
- 4.6. Shifted fractional-order Gegenbauer wavelet
- 4.7. Operational matrix of fractional-order integration of SFGBW
- 4.8. Applications and numerical simulations
- 4.9. Conclusion
- Chapter 5: A numerical study of exponential modified cubic B-spline for solving nonlinear Fisher's equation
- 5.1. Introduction
- 5.2. Numerical scheme
- 5.3. Methodology implementation and discussions
- 5.4. Conclusions
- Chapter 6: Numerical simulation of symmetric regularized long wave equations arising in mathematical physics
- 6.1. Introduction
- 6.2. Trigonometric cubic B-splines and collocation method
- 6.2.1. Trigonometric cubic B-splines
- 6.2.2. Collocation method
- 6.3. Discretization
- 6.3.1. Initial solution
- 6.4. Numerical simulations
- 6.4.1. Single solitary wave motion
- 6.4.2. Example 2: Collision of two solitons
- 6.5. Conclusion
- Chapter 7: Comparison of exponential and trigonometric B-spline collocation techniques for simulation of singular perturbed delay differential equations
- 7.1. Introduction
- 7.2. Problem statement
- 7.3. Existence of solutions and selection of a mesh
- 7.4. Implementation of collocation methods
- 7.5. Anatomy of convergence
- 7.6. Numerical examples
- 7.7. Discussion of numerical examples
- 7.8. Conclusion
- Chapter 8: A systematic study on diversified Newtonian and non-Newtonian fluids via numerous mathematical techniques
- 8.1. Introduction
- 8.2. Implementation of regime
- 8.3. Results and discussion
- 8.4. Summary of observations
- 8.5. Conclusion
- Chapter 9: Computational analysis for the stress–strength on the reliability model
- 9.1. Introduction
- 9.2. Probability of disaster (α)
- 9.3. Numerical values for the probability of disaster
- 9.4. Stress–strength reliability P=Pr(Y>X)
- 9.5. Interpretive study
- 9.6. Case Study
- 9.7. Conclusion
- Chapter 10: Instability of Casson fluid–viscous fluid interfaces
- 10.1. Introduction
- 10.2. Problem formulated
- 10.3. Boundary and interfacial conditions
- 10.3.1. Boundary conditions
- 10.3.2. Interfacial conditions
- 10.4. Stability analysis
- 10.4.1. Equilibrium state
- 10.4.2. Perturbed state
- 10.5. Dispersion relationship
- 10.6. Results and discussions
- 10.7. Conclusions
- Chapter 11: Remarks on nonlinear fractional differential equations
- 11.1. Generalized Abel integral equations
- 11.1.1. Preliminaries
- 11.1.2. Main results
- 11.1.3. An illustrative example
- 11.1.4. Conclusion
- 11.2. The fractional nonlinear problem with an integral boundary condition
- 11.2.1. Introduction
- 11.2.2. Background
- 11.2.3. Existence and uniqueness
- 11.2.4. Examples
- 11.2.5. Conclusion
- 11.3. Uniqueness of Hadamard-type integral equations
- 11.3.1. Introduction
- 11.3.2. Main results
- 11.3.3. Conclusion
- 11.4. Summary
- Funding
- Chapter 12: Exploring chaotic dynamics in a modified fractional system with the Atangana–Baleanu operator
- 12.1. Introduction
- 12.2. Preliminary concepts
- 12.3. The fractional chaotic system
- 12.4. Approximation of the Atangana–Baleanu fractional derivative
- 12.5. Simulation results
- 12.6. Numerical analysis
- 12.6.1. Equilibria
- 12.6.2. Jacobean matrix and stability
- 12.6.3. Lyapunov exponents
- 12.7. Impact of initial conditions
- 12.8. Conclusion
- CRediT authorship contribution statement
- Funding
- Chapter 13: Impact of viscous correction on the viscoelastic potential flow analysis of the Rayleigh–Taylor instability
- 13.1. Introduction
- 13.2. Mathematical formulation and boundary conditions
- 13.2.1. Boundary conditions
- 13.2.2. Stability analysis
- 13.2.3. Contribution by pressure correction terms
- 13.3. Dispersion relationship
- 13.4. Results and discussion
- 13.4.1. A comparative analysis with (Shukla and Awasthi [17])
- 13.4.2. Impact of the Atwood number
- 13.4.3. Impact of Rivlin–Ericksen fluid thickness
- 13.4.4. Impact of Weber number
- 13.4.5. Impact of the Reynolds number
- 13.4.6. Impact of viscoelasticity
- 13.4.7. Impact of the Froude number
- 13.5. Conclusion
- Chapter 14: Finite element modeling of MHD flow in alveolar ducts
- 14.1. Introduction
- 14.2. Problem formulation
- 14.3. Finite element method
- 14.3.1. Galerkin approximation with variational formulations
- 14.3.2. Time stepping
- 14.4. Numerical results and discussion
- 14.5. Conclusions
- B: Mathematical modeling and optimization
- Chapter 15: The fractional-order Lotka–Volterra competition model: an analysis with the additive Allee effect
- 15.1. Introduction
- 15.2. Mathematical model
- 15.3. Mathematical preliminaries
- 15.4. Mathematical analysis
- 15.4.1. Uniqueness of the solution
- 15.4.2. Positivity of the solutions
- 15.4.3. Uniform boundedness of the solutions
- 15.5. Existence and stability conditions of equilibrium points
- 15.5.1. Equilibrium points
- 15.5.2. Stability analysis
- 15.6. Numerical simulations
- 15.7. Conclusion
- Chapter 16: Modeling and analyzing delay in plant responses under toxicity
- 16.1. Introduction
- 16.2. Mathematical model
- 16.3. Analysis of the model
- 16.3.1. Boundedness of the dynamical system
- 16.3.2. Positivity of solution
- 16.3.3. Equilibrium point of the system
- 16.3.4. Biological interpation of the dynamical system
- 16.4. Sensitivity analysis of the dynamical model
- 16.4.1. Sensitivity analysis w.r.t r2
- 16.4.2. Sensitivity analysis w.r.t r5
- 16.5. Numerical example
- 16.5.1. Results & discussion
- 16.6. Conclusion
- Appendix 16.A.
- Appendix 16.B.
- Appendix 16.C.
- Chapter 17: Mathematical modeling of coral–algal competition under environmental pressures in aquatic ecosystems: a review
- 17.1. Introduction
- 17.2. Basic model of the interaction between algae and corals
- 17.3. Other models depicting the coral–algal relationship
- 17.4. Modeling coral–algal relationship under toxin-induced coral death
- 17.5. Modeling coral–algal relationship under climate change
- 17.6. Modeling the role of herbivorous fishes in coral–algal relationship
- 17.7. Discussion
- 17.8. Conclusion
- Chapter 18: Effect of time delay on directional and stability analysis of plant competition for allelochemicals study
- 18.1. Introduction
- 18.2. Mathematical model
- 18.3. Boundedness
- 18.4. Positivity of the solutions
- 18.5. Equilibrium points
- 18.6. Local stability of the equilibrium E⁎(P1⁎,P2⁎)
- 18.7. Global stability of the equilibrium E⁎(P1⁎,P2⁎)
- 18.8. Direction and stability of Hopf-bifurcating solution
- 18.9. Numerical example
- 18.10. Result and discussion
- 18.11. Conclusion
- CRediT authorship contribution statement
- Formatting of funding sources
- Chapter 19: Harnessing the potentials of machine learning models in Alzheimer's disease prediction and detection
- 19.1. Introduction
- 19.2. Taxonomy
- 19.2.1. ADHD detection and prediction using machine learning (ML) based on neuroimaging
- 19.2.2. Feature-based approaches
- 19.3. Conclusion
- Chapter 20: Impact of deforestation and environmental degradation on Nipah virus outbreaks: a critical review
- 20.1. Introduction
- 20.2. Correlation in trends in human population growth and forest-cover decline
- 20.2.1. Human population explosion
- 20.2.2. Human population growth and environmental degradation
- 20.3. Effect of deforestation on wildlife habitat
- 20.4. Wildlife habitat destruction and zoonotic spillover
- 20.5. Possible correlation between environment degradation on NiV outbreaks
- 20.6. Conclusion
- Chapter 21: Current advances in data-driven modeling and computational tools for novel materials
- 21.1. Introduction
- 21.2. Computational modeling and various methods for materials properties
- 21.2.1. Molecular dynamics
- 21.2.2. Density functional theory
- 21.2.3. Finite element method
- 21.2.4. Monte Carlo simulation
- 21.3. Current advances in data-driven modeling
- 21.4. Conclusion
- Chapter 22: Onset of bioconvection in the presence of magnetic nanoparticles and gyrotactic microorganisms in porous media
- 22.1. Introduction
- 22.2. Physical model of the problem
- 22.3. Mathematical representation of the problem
- 22.4. The steady-state solution
- 22.5. Linearization
- 22.6. Results and discussion
- 22.7. Conclusion
- Chapter 23: Predicting diabetes using artificial neural networks and K-nearest neighbors
- 23.1. Introduction
- 23.2. Diabetes as a chronic metabolic disorder
- 23.2.1. Role of machine learning in diabetes prediction
- 23.3. Methodology
- 23.3.1. Dataset and preprocessing
- 23.3.2. K-nearest neighbors (KNN)
- 23.3.3. Artificial neural networks (ANN)
- 23.4. Performance analysis
- 23.4.1. Performance analysis of the KNN model
- 23.4.2. Performance analysis of ANN model
- 23.5. Results and discussion
- 23.6. Conclusion
- Chapter 24: Real coded GA for finding stable structures of two small molecules
- 24.1. Introduction
- 24.2. Problem discussion
- 24.2.1. Finding a stable conformation for pseudoethane
- 24.2.2. Finding stable conformation for the 1,2,3-trichloro-1-fluoro-propane molecule
- 24.3. Problem complexity
- 24.3.1. Real coded genetic algorithms
- 24.3.2. Parameter settings
- 24.3.3. Computational results and discussion
- 24.3.4. Pseudoethane
- 24.3.5. 1,2,3-trichloro-1-fluoro-propane
- 24.4. Conclusions
- Chapter 25: Cost optimization of a batch retrial queue with a working vacation
- 25.1. Introduction
- 25.1.1. Retrial queue
- 25.2. Literature review
- 25.3. Description of the model
- 25.3.1. Practical application of this model
- 25.4. Steady-state analysis
- 25.4.1. Notations & probabilities
- 25.4.2. Steady-state equations
- 25.4.3. Steady-state solutions
- 25.5. Performance measures of the system
- 25.5.1. State probabilities of the system
- 25.5.2. Average size of system and orbit
- 25.5.3. Reliability measures
- 25.6. Numerical analysis
- 25.7. Conclusion
- Chapter 26: Future now: unleashing the potentials of machine learning algorithms in 6G
- 26.1. Introduction
- 26.2. Preliminaries of 6G
- 26.2.1. Globular trends towards 6G
- 26.2.2. Requirements
- 26.2.3. Candidate technologies
- 26.2.4. Milestones in development of 6G
- 26.3. Machine learning algorithms for 6G
- 26.3.1. Machine learning algorithms for latency minimization
- 26.3.2. Machine learning algorithms that improve energy efficiency
- 26.3.3. Machine learning algorithms for RAT selection
- 26.4. Conclusion
- Index
- Edition: 1
- Published: February 4, 2025
- Imprint: Morgan Kaufmann
- No. of pages: 358
- Language: English
- Paperback ISBN: 9780443300127
- eBook ISBN: 9780443300134
HS
Hari M Srivastava
Dr. Hari M. Srivastava is Professor Emeritus in the Department of Mathematics and Statistics at the University of Victoria, British Columbia, Canada. He earned his Ph.D. degree in 1965 while he was a full-time member of the teaching faculty at the Jai Narain Vyas University of Jodhpur, India. Dr. Srivastava has held (and continues to hold) numerous Visiting, Honorary and Chair Professorships at many universities and research institutes in different parts of the world. Having received several D.Sc. (honoris causa) degrees as well as honorary memberships and fellowships of many scientific academies and scientific societies around the world, he is also actively associated editorially with numerous international scientific research journals as an Honorary or Advisory Editor or as an Editorial Board Member. He has also edited many Special Issues of scientific research journals as the Lead or Joint Guest Editor, including the MDPI journal Axioms, Mathematics, and Symmetry, the Elsevier journals, Journal of Computational and Applied Mathematics, Applied Mathematics and Computation, Chaos, Solitons & Fractals, Alexandria Engineering Journal, and Journal of King Saud University – Science, the Wiley journal, Mathematical Methods in the Applied Sciences, the Springer journals, Advances in Difference Equations, Journal of Inequalities and Applications, Fixed Point Theory and Applications, and Boundary Value Problems, the American Institute of Physics journal, Chaos: An Interdisciplinary Journal of Nonlinear Science, and the American Institute of Mathematical Sciences journal, AIMS Mathematics, among many others. Dr. Srivastava has been a Clarivate Analytics (Web of Science) Highly-Cited Researcher since 2015. Dr. Srivastava’s research interests include several areas of Pure and Applied Mathematical Sciences, such as Real and Complex Analysis, Fractional Calculus and Its Applications, Integral Equations and Transforms, Higher Transcendental Functions and Their Applications, q-Series and q-Polynomials, Analytic Number Theory, Analytic and Geometric Inequalities, Probability and Statistics, and Inventory Modeling and Optimization. He has published 36 books, monographs, and edited volumes, 36 book (and encyclopedia) chapters, 48 papers in international conference proceedings, and more than 1450 peer-reviewed international scientific research journal articles, as well as Forewords and Prefaces to many books and journals.
GA
Geeta Arora
Dr. Geeta Arora is currently a Professor at the Department of Mathematics, Lovely Professional University, Punjab, India. She earned her Ph.D. degree from IIT Roorkee, India in 2011. Her research interests include Numerical Analysis, Partial Differential Equations and Wavelets. She has over eleven years of teaching experience and published around 50 research papers in both international and national Journals. She has authored 10 book chapters in national and international publications. Dr. Arora is the author of a two introductory mathematics books, Quick Calculations in Mathematics and Essential Statistics. Her area of applied research is the development of numerical methods and statistics.
FS