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Elsevier

Artificial Intelligence Modeling for Dynamical Problems

  • 1st Edition - April 1, 2027
  • Latest edition
  • Editors: Snehashish Chakraverty, Dhabaleswar Mohapatra, Arup Kumar Sahoo
  • Language: English

Artificial Intelligence Modeling for Dynamical Problems provides a comprehensive exploration of AI-driven methodologies tailored to address the intricate challenges posed by dynami… Read more

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Description

Artificial Intelligence Modeling for Dynamical Problems provides a comprehensive exploration of AI-driven methodologies tailored to address the intricate challenges posed by dynamical systems. The chapters in this book delve into cutting-edge techniques, including scientific machine learning, operator learning, fuzzy logic, and optimization algorithms, highlighting their applications in structural dynamics, fluid dynamics, robotics, and wave dynamics. Readers will gain insights into innovative state-of-the-art approaches such as Physics-Informed Neural Networks (PINNs) for precise navigation and control in autonomous vehicles, convolutional neural networks (CNNs) for frequency dynamics recognition, and data-driven models for market sentiment analysis. Additionally, the book explores the role of uncertainty modeling, statistical inference, and hybrid AI techniques, such as Type-2 fuzzy fractional modeling, in advancing the field of dynamical system analysis. Each chapter combines foundational principles with practical applications, including recent investigations, making it a valuable resource for researchers, practitioners, students of STEM seeking to apply theoretical AI models to real-world dynamical problems.

Key features

  • Presents a systematic approach to the AI and machine learning models and techniques, and how they can be applied to analyzing and modeling dynamical systems.
  • Presents advanced techniques such as and physics-informed machine learning, DeepONet, Type-2 fuzzy sets, uncertainty analysis, and lightweight modeling.
  • Provides readers with easy-to-follow examples of generalized systems governed by linear or non-linear differential equations.
  • Presents extensive applications across a variety of research disciplines, including techniques such as deep learning, data fusion, data-driven modeling, and statistical inference in fields such as bioinformatics, robotics, finance, and other engineering topics.

Readership

Researchers in computational modelling, applied mathematicians, and computer scientists working with researchers, engineers, and scientists in a wide range of modelling applications for engineering and scientific research. The primary audience also includes researchers and professionals in the fields of mathematics, IT, biomedicine, AI, ML, biology, healthcare, physics, and environmental science

Table of contents

1. Neural Network Modeling for Dynamical Systems
2. Scientific Machine Learning for Fluid Dynamics
3. Optimization Techniques for Dynamical Models
4. Fuzzy Models for Structural Dynamics
5. Type-2 Fuzzy Fractional Modeling
6. Uncertainty Analysis in Wave Dynamics
7. Lightweight Robotics Modeling
8. Physics-informed Machine Learning for Robot Tracking Control
9. Neural Networks Model for Navigation Systems
10. Deep Learning for Remote Sensing Applications
11. Data-Driven Models for Financial Analysis
12. CNNs for Bioinformatics Applications
13. Deep CNNs for Frequency Dynamics Recognition
14. Data Mining for Market Sentiment Analysis
15. Statistical Inference for Dynamical Systems
16. Machine Learning in Poverty Alleviation Dynamics

Product details

  • Edition: 1
  • Latest edition
  • Published: April 1, 2027
  • Language: English

About the editors

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
Department of Mathematics, Applied Mathematics Group, National Institute of Technology Rourkela, Rourkela, Odisha, India

DM

Dhabaleswar Mohapatra

Dhabaleswar Mohapatra is currently working as an Assistant Professor in the Department of Mathematics at the Institute of Technical Education and Research, Siksha ‘O’ Anusandhan (Deemed to be University), Odisha, India. He received his PhD from the National Institute of Technology, Rourkela, Odisha, India. To date, he has published 12 research articles in journals and book chapters.

Affiliations and expertise
Department of Mathematics, Siksha “O” Anusandhan (Deemed to be University), Odisha, India

AS

Arup Kumar Sahoo

Arup Kumar Sahoo is currently working as an Assistant Professor in the Department of Computer Science and Engineering at Siksha “O” Anusandhan (Deemed to be University), Odisha, India. He has joined as a postdoctoral research fellow at the Autonomous Navigation and Sensor Fusion Lab (ANSFL), The Hatter Department of Marine Technologies, University of Haifa, Israel. Dr. Sahoo holds a PhD from the Department of Mathematics, National Institute of Technology Rourkela, Odisha, India. He earned his MPhil in Mathematics from Utkal University, Bhubaneswar, Odisha, India, and MSc in Mathematics and Computing from Biju Patnaik University of Technology, Rourkela, Odisha, India. Dr Sahoo has authored and co-authored 13 research papers and book chapters published in journals and conferences. In 2023, he received the Best Paper Presenter Award at an IEEE Conference.

Affiliations and expertise
The Hatter Department of Marine Technologies, Charney School of Marine Sciences, University of Haifa, Israel