New Paradigms in Computational Modeling and Its Applications
- 1st Edition - January 9, 2021
- Imprint: Academic Press
- Editor: Snehashish Chakraverty
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 2 1 3 3 - 4
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 2 1 6 8 - 6
In general, every problem of science and engineering is governed by mathematical models. There is often a need to model, solve and interpret the problems one encounters in the wo… Read more
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Request a sales quoteIn general, every problem of science and engineering is governed by mathematical models. There is often a need to model, solve and interpret the problems one encounters in the world of practical problems. Models of practical application problems usually need to be handled by efficient computational models.
New Paradigms in Computational Modeling and Its Applications deals with recent developments in mathematical methods, including theoretical models as well as applied science and engineering. The book focuses on subjects that can benefit from mathematical methods with concepts of simulation, waves, dynamics, uncertainty, machine intelligence, and applied mathematics. The authors bring together leading-edge research on mathematics combining various fields of science and engineering. This perspective acknowledges the inherent characteristic of current research on mathematics operating in parallel over different subject fields.
New Paradigms in Computational Modeling and Its Applications meets the present and future needs for the interaction between various science and technology/engineering areas on the one hand and different branches of mathematics on the other. As such, the book contains 13 chapters covering various aspects of computational modeling from theoretical to application problems. The first six chapters address various problems of structural and fluid dynamics.
The next four chapters include solving problems where the governing parameters are uncertain regarding fuzzy, interval, and affine. The final three chapters will be devoted to the use of machine intelligence in artificial neural networks.
- Presents a self-contained and up to date review of modelling real life scientific and engineering application problems
- Introduces new concepts of various computing techniques to handle different engineering and science problems
- Demonstrates the efficiency and power of the various algorithms and models in a simple and easy to follow style, including numerous examples to illustrate concepts and algorithms
Students, educators, and researchers in the field of computational biology, mathematical physiology, biomedical engineering, mathematics, physics, data science
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Preface
- Chapter 1: Nanostructural dynamics problems with complicating effects
- Abstract
- Acknowledgment
- 1.1: Introduction
- 1.2: Proposed model
- 1.3: Solution procedure
- 1.4: Results and discussion
- 1.5: Conclusion
- Chapter 2: Vibration of functionally graded piezoelectric material beams
- Abstract
- Acknowledgments
- Chapter points
- 2.1: Introduction
- 2.2: Functionally graded piezoelectric beam
- 2.3: Constitutive relation
- 2.4: Mathematical formulation
- 2.5: Numerical results
- 2.6: Conclusions
- Chapter 3: Vibration of microstructural elements
- Abstract
- Acknowledgment
- 3.1: Introduction
- 3.2: Formulation of proposed model
- 3.3: Analytical solution to the proposed model
- 3.4: Results and discussion
- 3.5: Concluding remarks
- Chapter 4: Coupled shallow water wave equations
- Abstract
- 4.1: Introduction
- 4.2: Homotopy perturbation transform method
- 4.3: Solution of CSWWE using HPTM
- 4.4: Conclusion
- Chapter 5: Natural convection in a nanofluid flow
- Abstract
- Acknowledgment
- 5.1: Introduction
- 5.2: Problem formulation
- 5.3: Galerkin’s method [13–15]
- 5.4: Implementation of Galerkin’s method
- 5.5: Results and discussions
- 5.6: Conclusion
- Chapter 6: Fractional fluid mechanics systems
- Abstract
- Acknowledgment
- 6.1: Outline and motivations
- 6.2: Preliminaries
- 6.3: Implementation of HPZZTM
- 6.4: Applications of HPZZTM
- 6.5: Conclusion
- Chapter 7: Inverse problems in diffusion processes with uncertain parameters
- Abstract
- 7.1: Introduction
- 7.2: Preliminaries
- 7.3: Fuzzy inverse iteration method
- 7.4: Estimating fuzzy parameters of diffusion problem by FIIM
- 7.5: Results and discussions
- 7.6: Conclusion
- Chapter 8: Affine approach in solving linear structural dynamic problems with uncertain parameters
- Abstract
- 8.1: Introduction
- 8.2: Preliminaries
- 8.3: Affine arithmetic
- 8.4: Proposed methodology
- 8.5: Numerical examples
- 8.6: Conclusion
- Chapter 9: Numerical solution of Langevin stochastic differential equation with uncertain parameters
- Abstract
- 9.1: Introduction
- 9.2: Preliminaries
- 9.3: Numerical solution of SDEs
- 9.4: Fuzzy arithmetic
- 9.5: The solution of FSDE
- 9.6: Example problem
- 9.7: Conclusions
- Chapter 10: Fuzzy eigenvalue problems of structural dynamics using ANN
- Abstract
- 10.1: Introduction
- 10.2: Fuzzy eigenvalue problem
- 10.3: ANN procedure
- 10.4: Examples related to fuzzy eigenvalue problem
- 10.5: Conclusion
- Chapter 11: Artificial neural network approach for solving fractional order applied problems
- Abstract
- 11.1: Artificial neural network
- 11.2: Overview of fractional differential calculus
- 11.3: General ANN formulation for FDEs
- 11.4: Single-layer Chebyshev neural network model for FDEs
- 11.5: Numerical examples and results
- 11.6: Conclusion
- Chapter 12: Speech emotion recognition using deep learning
- Abstract
- 12.1: Introduction
- 12.2: Proposed model
- 12.3: Experiment and results
- 12.4: Conclusion
- Chapter 13: A user independent hand gesture recognition system using deep CNN feature fusion and machine learning technique
- Abstract
- Acknowledgments
- 13.1: Introduction
- 13.2: Related works
- 13.3: Benchmark datasets
- 13.4: Theoretical backgrounds
- 13.5: Methodology
- 13.6: Experimental evaluation and results
- 13.7: Conclusions
- Chapter 14: A survey on group modeling strategies for recommender systems
- Abstract
- 14.1: Introduction
- 14.2: Group modeling strategies
- 14.3: Comparative study on group modeling strategies for recommender systems
- 14.4: Conclusion
- Chapter 15: Extraction of glacial lakes in the Himalayan region using landsat imagery
- Abstract
- 15.1: Introduction
- 15.2: Proposed framework
- 15.3: Experimental results
- 15.4: Conclusion
- Index
- Edition: 1
- Published: January 9, 2021
- No. of pages (Paperback): 278
- No. of pages (eBook): 278
- Imprint: Academic Press
- Language: English
- Paperback ISBN: 9780128221334
- eBook ISBN: 9780128221686
SC
Snehashish Chakraverty
Snehashish Chakraverty has thirty-one years of experience as a researcher and teacher. Presently, he is working in the Department of Mathematics (Applied Mathematics Group), National Institute of Technology Rourkela, Odisha, as a senior (Higher Administrative Grade) professor. Dr Chakraverty received his PhD in Mathematics from IIT-Roorkee in 1993. Thereafter, he did his post-doctoral research at the Institute of Sound and Vibration Research (ISVR), University of Southampton, UK, and at the Faculty of Engineering and Computer Science, Concordia University, Canada. He was also a visiting professor at Concordia and McGill Universities, Canada, during 1997–1999 and visiting professor at the University of Johannesburg, Johannesburg, South Africa, during 2011–2014. He has authored/co-authored/edited 33 books, published 482 research papers (till date) in journals and conferences. He was the president of the section of mathematical sciences of Indian Science Congress (2015–2016) and was the vice president of Orissa Mathematical Society (2011–2013). Prof. Chakraverty is a recipient of prestigious awards, viz. “Careers360 2nd Faculty Research Award” for the Most Outstanding Researcher in the country in the field of Mathematics, Indian National Science Academy (INSA) nomination under International Collaboration/Bilateral Exchange Program (with the Czech Republic), Platinum Jubilee ISCA Lecture Award (2014), CSIR Young Scientist Award (1997), BOYSCAST Fellow. (DST), UCOST Young Scientist Award (2007, 2008), Golden Jubilee Director’s (CBRI) Award (2001), INSA International Bilateral Exchange Award (2015), Roorkee University Gold Medals (1987, 1988) for first positions in MSc and MPhil (Computer Application). He is in the list of 2% world scientists (2020 to 2024) in the Artificial Intelligence and Image Processing category based on an independent study done by Stanford University scientists.
Affiliations and expertise
HAG Professor, Department of Mathematics, Applied Mathematics Group, National Institute of Technology Rourkela, Rourkela, Odisha, India. Differential Equations (ordinary, partial, and fractional), Numerical Analysis, Computational Methods, Structural Dynamics (FGM, Nano), Fluid Dynamics, Mathematical and Uncertainty Modelling, Soft Computing and Machine Intelligence (Artificial Neural Network, Fuzzy, Interval, and Affine Computations).Read New Paradigms in Computational Modeling and Its Applications on ScienceDirect