Gaussian Fields and Their Application in Computational Engineering and Mathematical Physics
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".
Deadline for manuscript submissions: 15 February 2025 | Viewed by 63
Special Issue Editors
2. Lithuanian Energy Institute, Breslaujos St. 3, 44403 Kaunas, Lithuania
Interests: probabilistic risk assessment; complex systems; Bayesian inference; artificial intelligence; big data analytics; data mining; machine learning; artificial neural networks
Special Issues, Collections and Topics in MDPI journals
2. Director of the Lloyd's Register Foundation, Turing Programme on Data Centric Engineering, The Alan Turing Institute, The British Library, 96 Euston Road, London NW1 2DB, UK
Interests: achine learning; computational statistics; Bayesian statistics; Monte Carlo methods
Interests: probabilistic modeling; random fields; Bayesian inference; uncertainty quantification; numerical analysis; computational statistics; data-drivern engineering
Special Issue Information
Dear Colleagues,
The real world is inherently associated with uncertainty. Therefore, any digital model of a real-world phenomenon should account for these uncertainties. Probabilistic modeling, as a natural way of addressing such uncertainties, enables one to reason under uncertainty and make informed decisions in situations where complete information is unavailable. In recent years, probabilistic modeling has gained substantial attention due to the continuous increase in computing power and the growing availability of data.
Consequently, Gaussian fields such as one-dimensional Gaussian processes, as a subset of probabilistic modeling, play a significant role in computational engineering. Because of their versatility and flexibility, Gaussian random fields are often employed for forecasting, surrogate modeling, and the modeling of population variability.
Despite the advances made, challenges such as computational efficiency and the need for physically viable fields hinder their full potential in computational engineering and mathematical physics. This Special Issue aims to address these theoretical challenges as well as various applications of Gaussian processes. Therefore, we are seeking contributions regarding topics that include, but are not limited to, the following themes:
- Constraint Gaussian fields (e.g., embedded information and monotonic Gaussian fields);
- Non-Gaussian fields (and applications beyond Gaussianity, e.g. including truncated and transformed fields);
- Gaussian processes and applications for the dimensionality reduction of large-scale problems;
- Scalable Gaussian fields for big data (e.g., state-of-the-art decompositions);
- Uncertainty incorporation, extrapolation, and pattern discovery;
- Multi-output Gaussian fields and applications.
Prof. Dr. Robertas Alzbutas
Prof. Dr. Mark Girolami
Dr. Hussein Rappel
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. Entropy is an international peer-reviewed open access monthly 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 2600 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.
Keywords
- Gaussian fields
- probabilistic modeling
- Bayesian inference
- forecasting
