Systematic methods for the solution of inverse problems have developed significantly during the past two decades and have become a powerful tool for analysis and design in engineering. Inverse analysis is nowadays a common practice in which teams involved with experiments and numerical simulation synergistically collaborate throughout the research work, in order to obtain the maximum of information regarding the physical problem under study. In this paper, we briefly review various approaches for the solution of inverse problems, including those based on classical regularization techniques and those based on the Bayesian statistics. Applications of inverse problems are then presented for cases of practical interest, such as the characterization of nonhomogeneous materials and the prediction of the temperature field in oil pipelines.

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