Foundations of Mathematical Modelling for Engineering Problem Solving

✍ Scribed by Parikshit Narendra Mahalle, Nancy Ambritta P., Sachin R. Sakhare, Atul P. Kulkarni

• Publisher: Springer
• Year: 2023
• Tongue: English
• Leaves: 177
• Series: Studies in Autonomic, Data-driven and Industrial Computing
• Category: Library

✦ Synopsis

This book aims at improving the mathematical modelling skills of users by enhancing the ability to understand, connect, apply and use the mathematical concepts to the problem at hand. This book provides the readers with an in-depth knowledge of the various categories/classes of research problems that professionals, researchers and students might encounter following which the applications of appropriate mathematical models is explained with the help of case studies. The book is targeted at academicians, researchers, students and professionals who belong to all engineering disciplines.

✦ Table of Contents

Preface
Contents
About the Authors
1 Introduction
1.1 Modeling
1.2 Mathematical Modeling
1.3 General Steps
1.4 Trends in Teaching and Learning
1.5 Summary
References
2 Problem Solving and Mathematical Modeling
2.1 Problem Evolution
2.2 Problem Solving
2.3 Problem Classification
2.4 Modeling Process and Teaching Approaches
2.5 Summary
References
3 Decision Problems
3.1 Introduction
3.1.1 Control Theory
3.1.2 Game Theory
3.1.3 Probability and Statistics
3.1.4 Multicriteria Decision Analysis
3.2 Motivation
3.3 Case Studies
3.4 Summary
References
4 Optimization Problems
4.1 Introduction
4.1.1 Mathematical Modeling
4.1.2 History Development of Optimization
4.1.3 Optimization
4.2 Motivation
4.3 Case Study
4.4 Summary
References
5 Delay Problems
5.1 Introduction
5.1.1 Queuing Theory
5.1.2 Delay Differential Equations
5.2 Motivation
5.3 Case Studies
5.4 Summary
References
6 Data Science Problems
6.1 Introduction
6.1.1 Data Analytics and Learning Methodologies
6.1.2 Data Visualization Tools and Data Modeling
6.2 Motivation
6.3 Case Studies
6.4 Summary
References
7 Pandemic Problems
7.1 Introduction
7.1.1 Impacts of Pandemics
7.2 Motivation
7.2.1 Types of Epidemic Models
7.3 Case Studies
7.3.1 Compartmental Models
7.3.2 COVID-19 Pandemic Problem
7.4 Summary
References
8 Interdisciplinary Engineering Problems
8.1 Introduction
8.2 Motivation
8.3 Case Studies
8.4 Summary
References
9 Conclusion
9.1 Summary
9.2 Research Openings and Future Outlook