Inverse Engineering Handbook

Nyhet

Inbunden, Engelska, 2026

3 139 kr

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Leading experts in inverse problems have joined forces to produce a new edition of the definitive reference that allows readers to understand, implement, and benefit from a variety of problem-solving techniques.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.) As well as revision to existing chapters, this second edition includes four new chapters: a new chapter on the classis Tikhonov regularization technique, a new chapter on the topic of filter coefficient concepts for linear problems, a chapter dedicated to Bayesian solution of inverse problems, and finally a new chapter on machine learning and artificial intelligence.Anyone interested in inverse problems, regardless of their specialty, will find the Inverse Engineering Handbook to be a unique and invaluable compendium of up-to-date techniques.

Produktinformation

Utgivningsdatum2026-04-16
Mått178 x 254 x undefined mm
FormatInbunden
SpråkEngelska
SerieHandbook Series for Mechanical Engineering
Antal sidor504
Upplaga2
FörlagTaylor & Francis Ltd
ISBN9781032868813

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Keith A. Woodbury is Professor Emeritus of Mechanical Engineering at the University of Alabama, where his research in inverse heat conduction supported many investigations into quenching and metal casting. Dr. Woodbury is a Life Member and Fellow of ASME and has organized numerous technical sessions on inverse problems through the Heat Transfer Division’s K-20 Committee. He is the editor of the Inverse Engineering Handbook (2003) and lead author of Inverse Heat Conduction: Ill-posed problems (2023).

• Sequential Methods in Parameter Estimation • Sequential Function Specification Method • Tikhonov Regularization and Optimal Regularization • Filter Coefficient Concepts in Inverse Problems • The Adjoint Method to Compute the Numerical Solutions of Inverse Problems • The Effect of Correlations and Uncertain Parameters on the Efficiency of Estimating and the Precision of Estimated Parameters • Bayesian Methods for Inverse Problems • Machine Learning and Artificial Intelligence for Inverse Problems • Mollification and Space Marching • Inverse Heat Conduction Using Monte Carlo Method • Boundary Element Techniques for Inverse Problems • Optimal Experiment Design To Solve Inverse Heat Transfer Problems