Advancements in Mathematical Models, Probability Distributions, and Digital Twins: Bridging the Gap between Theory and Practice

Dear Colleagues,

Merging math and digital twin approaches is crucial both presently and in the future. Integrated models facilitate live simulations, predictive analytics, and prompt adaptation to evolving circumstances. They are priceless for optimizing processes and making informed decisions.

This Special Issue aims to serve as a catalyst for further research and investigation, stimulating intellectual discourse and inspiring future studies combining mathematical and digital twin approaches. The goal is to capture the progress in this area and provide insights for new scholars and experienced researchers. We look forward to contributions that bridge theory and practice in this area and to submitting novel and original research. Possible topics include, but are not restricted to

1. Examining the applications of digital twins in manufacturing, IoT, and industry;
2. Developing statistical methods to assess the accuracy and reliability of digital twin predictions;
3. Hybrid Models: Combining mathematical and digital twin approaches, as well as investigating the synergies and integration of mathematical models with digital twin technologies;
4. Uncertainty Quantification in Digital Twins: Addressing uncertainty and variability in digital twin simulations via probabilistic modeling;
5. How to apply networked digital twin systems to large-scale applications;
6. Data-driven approaches for digital twins;
7. Environmental Modeling and Simulation: Focusing on mathematical and digital twin models for environmental monitoring and sustainability;
8. Prospects and Ethical Considerations: Discussing the potential of mathematical models, probability distributions, and digital twins in shaping our future, along with ethical considerations and challenges.

The articles within this Special Issue will enhance the comprehension of mathematical models, probability distributions, and digital twins. This publication will also function as an extensive point of reference for both scholars and professionals, as its purpose is to encapsulate the current pinnacle of achievement within this discipline and offer valuable insights to those commencing their exploration of the subject. In essence, it will bridge the chasm between theoretical concepts and pragmatic implementations in mathematical modeling and digital twins.

For further information, please send an email to [email protected].

Dr. Sandra Ferreira
Guest Editor

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. Mathematical and Computational Applications 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 1400 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.

• digital twins
• statistical methods
• uncertainty quantification
• data-driven approaches