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Mathematics and Statistics

Numerical Methods For Inverse Problems

Numerical Methods For Inverse Problems

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The book "Numerical Methods for Inverse Problems" consists of contemporaneouss articles featuring not only several well-known inverse problems used in the inference of many physical and engineering systems such as that of the partial differential equations but also the statistical and imaging inverse problems. It includes a variety of numerical methods for solving inverse problems such as that of the Tikhonov regularization; finite differences; and orthogonal decomposition; as well as those based on Bayesian inference, artificial neural networks, and quantum annealing.

Olga Moreira is a Ph.D. in Astrophysics and B.Sc. in Physics and Applied Mathematics. She is an experienced technical writer and researcher which former fellowships include postgraduate positions at two of the most renown European institutions in the fields of Astrophysics and Space Science (the European Southern Observatory, and the European Space Agency). Presently, she is an independent scientist working on projects involving machine learning and neural networks research as well as peer-reviewing and edition of academic books.