Bayesian Inference in Inverse Problem

Dear Colleagues,

With the development of digital applications in engineering applications, the questions of data assimilation and model parameter inference have risen to primary importance.
Various methods exist to tackle these challenging problems, which are more or less adapted to the physics content involved within either the model or the system.

Among them, Bayesian inference is a powerful statistical method based on the well-known equation of conditional probability established by Bayes in the 1930s. Nevertheless, this method implies a sampling within the parameters domain that is very time consuming for complex systems. Moreover, this method has evolved to include new efficient techniques from different communities and applications.

In this Special Issue, we invite the scientific community to publish their works dealing with operational applications of the Bayesian inference in different uses of this method depending on the physics involved and the final application.

The following suggested subtopics are of particular interest:

- System numerical twin using Bayesian inference;
- Inverse method and model identification;
- Bayesian experimental design;
- Maximum entropy and choice of prior distributions;
- Bayesian modeling and inference;
- High-performance computing for Bayesian data analysis;
- Bayesian methods for the analysis of big data.

Prof. Emmanuelle Abisset-Chavanne
Prof. Battaglia Jean-Luc
Guest Editors

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 submissions that pass pre-check are 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. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.