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Machine Learning and Bayesian Methods in Inverse Heat Transfer by Balaji Srinivasan (Associate Professor, Department of Mechanical Engineering, Indian Institute of Technology (IIT) Madras, India) at Abbey's Bookshop,
Machine Learning and Bayesian Methods in Inverse Heat Transfer
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Balaji Srinivasan (Associate Professor, Department of Mechanical Engineering, Indian Institute of Technology (IIT) Madras, India) C. Balaji (Professor, Department of Mechanical Engineering, Indian Institute of Technology (IIT) Madras; Editor-in-Chief of Elsevier’s International Journal of Thermal Sciences, India)

Machine Learning and Bayesian Methods in Inverse Heat Transfer

Balaji Srinivasan (Associate Professor, Department of Mechanical Engineering, Indian Institute of Technology (IIT) Madras, India),  C. Balaji (Professor, Department of Mechanical Engineering, Indian Institute of Technology (IIT) Madras; Editor-in-Chief of Elsevier’s International Journal of Thermal Sciences, India)

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English
Elsevier - Health Sciences Division
20 March 2026
Heat transfer processes; Machine learning
Series: Emerging Technologies and Materials in Thermal Engineering
Machine Learning and Bayesian Methods in Inverse Heat Transfer offers a comprehensive exploration of inverse problems in heat transfer, blending classical techniques with modern advancements in machine learning and Bayesian methods. This essential guide provides a hands-on approach with practical examples, making complex concepts accessible to readers seeking to deepen their understanding of this critical field. The text covers essential topics including Introduction to Inverse Problems, Statistical Description of Errors and General Approach, Classical Techniques, Bayesian Methods, and a Machine Learning Approach to Inverse Problems. Readers will explore key concepts such as Gaussian distribution, linear and non-linear regression, Gauss-Newton algorithm, Tikhonov regularization, and more, gaining a solid foundation in applying these methods to real-world heat transfer scenarios. For engineers, scientists, senior undergraduates, graduates, and researchers in heat transfer and related fields, this book serves as a vital resource. By offering clear explanations, practical examples, and MATLAB codes, it empowers readers to tackle inverse problems with confidence. Whether readers are practicing engineers or graduate students specializing in heat and mass transfer, this book equips them with the tools and knowledge to excel and further advances in their field.
By:   , , , ,
Imprint:   Elsevier - Health Sciences Division
Country of Publication:   United States
Dimensions:   Height: 229mm,  Width: 152mm, 
Weight:   450g
ISBN:   9780443367915
ISBN 10:   0443367914
Series:   Emerging Technologies and Materials in Thermal Engineering
Pages:   310
Publication Date:  
Audience:   Professional and scholarly ,  Undergraduate
Format:   Paperback
Publisher's Status:   Active
1. Introduction to Inverse Problems 2. Statistical Description of Errors and General Approach 3. Classical Techniques 4. Bayesian Methods 5. Machine Learning Approach to Inverse Problems 6. Summary: Conclusion and Future Implications Index

Dr. Balaji Srinivasan is currently an Associate Professor in the Department of Mechanical Engineering at the Indian Institute of Technology (IIT) Madras, Chennai. His areas of research interest include computational fluid dynamics, numerical analysis, turbulence, and applied machine learning. Professor C. Balaji is currently a Professor in the Department of Mechanical Engineering at the Indian Institute of Technology (IIT) Madras, Chennai. Balaji brings over 25 years of experience in teaching and research. His areas of interest include heat transfer, optimization, computational radiation, atmospheric radiation, and inverse heat transfer. He is currently Editor-in-Chief of Elsevier’s International Journal of Thermal Sciences.

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