Inverse Problems Modelling And Simulation


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Inverse Problems: Modelling and Simulation


Inverse Problems: Modelling and Simulation

Author: Alemdar Hasanov Hasanoğlu

language: en

Publisher:

Release Date: 2025-06


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This volume presents the latest theoretical and experimental advancements in the field of inverse problems in recent years. It includes outstanding research results that reflect current theoretical and numerical aspects of inverse problems and their various applications. The volume is a collection of selected contributions from nearly three hundred invited presentations at the International Conference "Inverse Problems: Modelling and Simulation" (IPMS 2024) held from May 26 to June 1, 2024, in Malta. The topics covered in this volume are closely related to emerging deterministic and stochastic models in the fields of medical imaging, biology, geophysics, radar, computer science, communication theory, signal processing, visualization, engineering, and economics. The contributions in this volume reflect a broad range of problems in the theory and applications of inverse problems that are useful for mathematicians, physicists, engineers, and researchers working with inverse problems.

Computational Methods for Inverse Problems


Computational Methods for Inverse Problems

Author: Curtis R. Vogel

language: en

Publisher: SIAM

Release Date: 2002-01-01


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Provides a basic understanding of both the underlying mathematics and the computational methods used to solve inverse problems.

Inverse Problem Theory and Methods for Model Parameter Estimation


Inverse Problem Theory and Methods for Model Parameter Estimation

Author: Albert Tarantola

language: en

Publisher: SIAM

Release Date: 2005-01-01


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While the prediction of observations is a forward problem, the use of actual observations to infer the properties of a model is an inverse problem. Inverse problems are difficult because they may not have a unique solution. The description of uncertainties plays a central role in the theory, which is based on probability theory. This book proposes a general approach that is valid for linear as well as for nonlinear problems. The philosophy is essentially probabilistic and allows the reader to understand the basic difficulties appearing in the resolution of inverse problems. The book attempts to explain how a method of acquisition of information can be applied to actual real-world problems, and many of the arguments are heuristic.