Numerical Methods for Inverse Problems (Hardcover)

Olga Moreira

ISBN 10: 1774077078 / ISBN 13: 9781774077078

Published by Arcler Press, 2020

About this Item

Hardcover. 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. Presents contempory articles featuring well-known inverse problems used in the inference of many physical and engineering systems, such as that of the partial differential equations, plus statistical and imaging inverse problems. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Seller Inventory # 9781774077078

Bibliographic Details

• Title: Numerical Methods for Inverse Problems (Hardcover)
• Publisher: Arcler Press
• Publication Date: 2020
• Language: English
• ISBN 10: 1774077078
• ISBN 13: 9781774077078
• Binding: Hardcover
• Condition: new

About this title

Synopsis

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.

About the Author

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.