Machine Learning and Bayesian Methods in Inverse Heat Transfer

Balaji Srinivasan, C. Balaji (Autoren)

Buch | Softcover

310 Seiten

2026
Elsevier - Health Sciences Division (Verlag)
978-0-443-36791-5 (ISBN)

Noch nicht erschienen (ca. März 2026)
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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.

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.

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

Erscheint lt. Verlag	1.3.2026
Reihe/Serie	Emerging Technologies and Materials in Thermal Engineering
Verlagsort	Philadelphia
Sprache	englisch
Maße	152 x 229 mm
Gewicht	450 g
Themenwelt	Naturwissenschaften ► Physik / Astronomie
Themenwelt	Technik ► Elektrotechnik / Energietechnik
ISBN-10	0-443-36791-4 / 0443367914
ISBN-13	978-0-443-36791-5 / 9780443367915
Zustand	Neuware