Inverse Engineering Handbook

Author: admin

  • Inverse Engineering Handbook

    Hi. This site is the companion to the “Inverse Engineering Handbook”. The second edition of the Handbook will be published in Fall 2025. You will find examples and codes from the book at this site.

  • What to expect in the second edition

    The second edition of Inverse Engineering Handbook includes seven new chapters and two heavily revised chapters. Four older legacy chapters are also retained.

    Chapter 1:
    Inverse Problems and Parameter Estimation    		This often-cited article is reproduced from Measurement Science and Technology (1997) and serves as a great introduction to the Handbook
    Chapter 2:
    Parameter Estimation    		This chapter is a revision of Chapter 5 of Parameter Estimation in Engineering and Science by J. V. Beck and K. Arnold. Prof. Beck revised the chapter significantly over several years and the resulting text has never been published.
    Chapter 3:
    Sequential Function Specification Methods    		Revised and updated from the first edition and includes polynomial function assumptions beyond constant and linear.
    Chapter 4:
    Tikhonov Regularization and Optimal Regularization    		A new chapter and comprehensive treatment of the subject with attention paid to proper degree of regularization.
    Chapter 5:
    Filter Coefficient Concepts    		This new chapter on filter coefficient concepts addresses problems of multi-layers and multiple dimensions which can be solved in a near-real-time manner.
    Chapter 6:
    Adjoint Method    		Heavily revised and updated by Helcio R. B. Orlande.
    Chapter 7:
    Effect of Correlations on Uncertain Parameters    		A legacy chapter on the impact of correlations in data and models on estimates by Ashley Emery (1934-2024).
    Chapter 8:
    Experimental Uncertainties in Parameter or Function Estimation    		This new chapter offers insights drawn from experiments into the effects of measurement errors on estimations.
    Chapter 9:
    Statistical Inference and Bayesian Analysis    		This important new chapter provides a lucid presentation of statistical estimation methods.
    Chapter 10:
    Machine Learning and AI for Inverse Problems    		A new chapter on emerging machine learning and AI methods.
    Chapter 11:
    Mollification and Space Marching    		This legacy chapter is the ultimate resource for the mollification method.
    Chapter 12:
    Monte Carlo Methods    		A legacy chapter on random walks in heat conduction problems.
    Chapter 13:
    Boundary Element Techniques    		A legacy chapter on application of BEM to thermal and fluid problems.
    Chapter 14:
    Optimal Experiments    		An updated chapter on designing experiments to maximize information needed for parameter estimation and inverse problems.