New Paradigms in Computational Modeling and Its Applications
- 1st Edition - January 16, 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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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 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
1. Nanostructural dynamics problems with complicating effects
Subrat Kumar Jena and Snehashish Chakraverty
2. Vibration of functionally graded piezoelectric material beams
K.K. Pradhan and Snehashish Chakraverty
3. Vibration of microstructural elements
Subrat Kumar Jena, Rashmita Mundari, and Snehashish Chakraverty
4. Coupled shallow water wave equations
P. Karunakar and Snehashish Chakraverty
5. Natural convection in a nanofluid flow
U. Biswal, Snehashish Chakraverty, and B.K. Ojha
6. Fractional fluid mechanics systems
Rajarama Mohan Jena and Snehashish Chakraverty
7. Inverse problems in diffusion processes with uncertain parameters
T.D. Rao and Snehashish Chakraverty
8. Affine approach in solving linear structural dynamic problems with uncertain parameters
S. Rout and Snehashish Chakraverty
9. Numerical solution of Langevin stochastic differential equation with uncertain parameters
Sukanta Nayak and Snehashish Chakraverty
10. Fuzzy eigenvalue problems of structural dynamics using ANN
S.K. Jeswal and Snehashish Chakraverty
11. Artificial neural network approach for solving fractional order applied problems
Susmita Mall and Snehashish Chakraverty
12. Speech emotion recognition using deep learning
Tanmoy Roy, Marwala Tshilidzi, and Snehashish Chakraverty
13. A user independent hand gesture recognition system using deep CNN feature fusion and machine learning technique
Jaya Prakash Sahoo, Samit Ari, and Sarat Kumar Patra
14. A survey on group modeling strategies for recommender systems
Jitendra Kumar, Y.V. Ramanjaneyulu, Korra Sathya Babu, and Bidyut Kumar Patra
15. Extraction of glacial lakes in the Himalayan region using landsat imagery
Jagadeesh Thati, Samit Ari, and Kajal Agrawal
- Edition: 1
- Published: January 16, 2021
- Imprint: Academic Press
- Language: English
SC
Snehashish Chakraverty
Dr. Snehashish Chakraverty is a Senior Professor in the Department of Mathematics (Applied Mathematics Group), National Institute of Technology Rourkela, with over 30 years of teaching and research experience. A gold medalist from the University of Roorkee (now IIT Roorkee), he earned his Ph.D. from IIT Roorkee and completed post-doctoral work at the University of Southampton (UK) and Concordia University (Canada). He has also served as a visiting professor in Canada and South Africa. Dr. Chakraverty has authored/edited 38 books and published over 495 research papers. His research spans differential equations (ordinary, partial, fractional), numerical and computational methods, structural and fluid dynamics, uncertainty modeling, and soft computing techniques. He has guided 27 Ph.D. scholars, with 10 currently under his supervision.
He has led 16 funded research projects and hosted international researchers through prestigious fellowships. Recognized in the top 2% of scientists globally (Stanford-Elsevier list, 2020–2024), he has received numerous awards including the CSIR Young Scientist Award, BOYSCAST Fellowship, INSA Bilateral Exchange, and IOP Top Cited Paper Awards. He is Chief Editor of International Journal of Fuzzy Computation and Modelling and serves on several international editorial boards.
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