Special Issue "Probabilistic Methods for Inverse Problems"
Deadline for manuscript submissions: 31 January 2018
Inverse problems arise in many applications. Whatever the domain of application, when the unknown quantities on which we want to infer, and the quantities on which we can do measurements, and the mathematical relations linking them are identified, the problem then become inference. Deterministic regularization methods have been successfully developed and used. Two main difficulties still remain: How to choose the different criteria and how to weight them and how to quantify the uncertainties. In the three last decades, the probabilistic methods and, in particular, the Bayesian approach have shown their efficiency. The focus of this Special Issue is to have original papers on these probabilistic methods where the real advantages on regularization methods have been shown. The papers with real applications in different area such as biological and medical imaging, industrial nondestructive testing, radio astronomical, and geophysical imaging are preferred.
Prof. Dr. Ali Mohammad-Djafari
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.
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- Inverse problems
- Bayesian inference
- Approximate Bayesian computation
- Variational methods
- Variational Bayesian approximation
- Expectation propagation
- Uncertainty quantification
- Computed tomography
- Medical imaging
- Nondestructive industrial imaging
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Tentative title: Stochastic proximal gradient algorithms for multi-source quantitative PAT
Authors: Simon Rabanser, Lukas Neumann, and Markus Haltmeier
Tentative title: An improved chaotic optimization algorithm applied to a DC electrical motor modeling
Authors: Ruben DI FILIPPO (1) and Simone FIORI (2)
Affiliations: (1) Graduate school of System and Control, Technische Universiteit Eindhoven, Eindhoven (Germany); (2) Dipartimento di Ingegneria dell'Informazione, Università Politecnica delle Marche, Ancona (Italy)
Abstract: The chaos-based optimization algorithm (COA) is a method to optimize possibly non-linear complex functions of several variables by chaos search. The main innovation behind the chaos-based optimization algorithm is to generate chaotic trajectories by means of nonlinear, discrete-time dynamical systems to explore the search space while looking for the global minimum of a complex criterion function. The aim of the present research is to investigate the numerical properties of the COA, both on complex optimization test-functions from the literature and on a real-world problem, to contribute to the understanding of its global-search features and to propose a refinement of the original COA algorithm in order to improve its performances. In particular, the real-world optimization problem tackled within the paper is the estimation of six electro-mechanical parameters of a model of a direct-current (DC) electrical motor. A large number of test results prove that the algorithm achieves an excellent numerical precision at a little expense in the computational complexity, which appears as extremely limited, compared to the complexity of other benchmark optimization algorithms, namely, the genetic algorithm and the simulated annealing algorithm.