Inverse Engineering Handbook

Inverse Engineering Handbook, Second Edition, is a comprehensive resource that details methods of determining a "cause" from an observed "effect," allowing readers to understand, implement, and benefit from a variety of problem-solving techniques. Leading experts in inverse problems have joined to produce a new edition of Inverse Engineering Handbook. Along with the revision of the existing chapters, this second edition includes four new chapters: a new chapter on the classical Tikhonov regularization technique, a new chapter on the topic of filter coefficient concepts for linear problems, a chapter dedicated to Bayesian solutions of inverse problems, and finally, a new chapter on machine learning and artificial intelligence. The focus of most of the work in this new edition is on parabolic and elliptic problems typified by transient and steady-state heat conduction; however, the scope of application extends to any mathematically similar problems (in chemical transport, mass transfer, etc.). Engineering researchers interested in inverse problems, regardless of their specialty, will find the Inverse Engineering Handbook, Second Edition, a unique and invaluable compendium of up-to-date techniques.

Keith A. Woodbury is Professor Emeritus in Mechanical Engineering at The University of Alabama, USA. He holds BS and MS degrees in Mechanical Engineering from The University of Alabama and earned his PhD in Mechanical Engineering at Virginia Polytechnic Institute and State University (Virginia Tech) in 1984. From 1984 to 1988, Dr. Woodbury conducted research for Reynolds Aluminum in the Metallurgical Research Division, focusing primarily on thermal challenges in ingot production and hot rolling operations. He joined the faculty at the University of Alabama in 1988 and retired from active faculty after 33 years in 2021. He is a Fellow of the ASME and served as associate editor of Inverse Problems in Science and Engineering. Dr. Woodbury authored over 100 articles and 2 books, contributed 14 book chapters, and organized or co-organized countless sessions, symposia, and international conferences on inverse problems since 1990.

1. Inverse Problems and Parameter Estimation: Integration of Measurements and Analysis 2. Matrix Analysis for Parameter Estimation 3. Sequential Function Specification Method 4. Tikhonov Regularization and Optimal Regularization 5. Filter Coefficients Approach for Solving Inverse Heat Conduction Problems 6. Adjoint Method Primer 7. The Iterative Regularization Technique Based on the Conjugate Gradient Method with Adjoint Problem Formulation 8. The Effect of Correlations and Uncertain Parameters on the Efficiency of Estimating and the Precision of Estimated Parameters 9. Estimation of Parameters or Functions after Analysis of Experimental Uncertainties 10. Statistical Inference and Bayesian Analysis 11. Machine Learning and AI for Inverse Problems 12. Mollification and Space Marching 13. Inverse Heat Conduction Using Monte Carlo Method 14. Optimal Experiment Design to Solve Inverse Heat Transfer Problems