Symmetry in Optimized Machine Learning Algorithms for Modeling Dynamical Systems
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".
Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 790
Special Issue Editors
2. The Invernizzi Centre for Research in Innovation, Organization, Strategy and Entrepreneurship (ICRIOS), Bocconi University, Via Sarfatti, 25, 20136 Milano, Italy
Interests: mathematical economics; machine learning and data science; epidemics models; fractional calculus
Special Issues, Collections and Topics in MDPI journals
Interests: PDEs; game theory; applied mathematics; topology
Special Issues, Collections and Topics in MDPI journals
Interests: game theory; numerical analysis; applied mathematics
Special Issues, Collections and Topics in MDPI journals
Interests: fractional calculus; fuzzy sets and systems; mathematical modelling; numerical analysis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The main aim of this Special Issue is to explore what machine learning algorithms can better predict in particular by a structural combination of real dynamical systems. Recent studies have confirmed that symmetry plays a new role in building real machine learning systems and was demonstrated that ignoring symmetries can have dire over-fitting consequences and that incorporating symmetry into the model reduces over-fitting issues. Mathematical objects used to make models of physical phenomena dependent on time are dynamic systems. These models are used in economic forecasting, medical issues, environmental modeling, etc. There is an overlap between machine learning and dynamic systems. To address this relation, let us assume a framework for dynamical system learning, using the idea of instrumental–variable regression to transform dynamical system learning into a sequence of machine learning problems. This transformation allows applying strong literature on machine learning to incorporate many types of prior knowledge. Hence, a family of fast and practical learning algorithms for a variety of dynamical system models are employed to forecast the real behavior of such dynamical systems precisely. Further, machine learning folks often use dynamical systems’ taxonomy and reformulate it to some fancy term to make the idea sound sort of new. In this Special Issue, we would like to attract leading researchers in these areas in order to include new high-quality results on these topics involving their dynamical properties as well as their symmetry characteristics, both from a theoretical and an applied point of view. Please note that all submitted papers must be within the general scope of the Symmetry journal.
Prof. Dr. Massimiliano Ferrara
Dr. Bruno Antonio Pansera
Dr. Mehdi Salimi
Dr. Ali Ahmadian
Dr. Tiziana Ciano
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. Symmetry 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 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.
Keywords
- machine learning
- supervised algorithms
- unsupervised algorithms
- optimization dynamical systems
- symmetry
- real-world applications
