Symmetry in Deep Learning Networks and Its Applications in the Real World
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".
Deadline for manuscript submissions: 30 November 2025 | Viewed by 531
Special Issue Editors
Interests: traffic classification; support vector machine; feature selection; parameters optimization
Special Issues, Collections and Topics in MDPI journals
Interests: artificial intelligence; deep learning; computer vision
Interests: artificial intelligence algorithms; ad-hoc networks; aeronautical communications; wireless communications
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Deep learning networks have been widely applied in real-world engineering problems. Some effective deep learning networks have typical symmetric structures. For example, the encoder and decoder in U-Net networks have symmetry, as well as the generator and discriminator in GANs. Actual engineering problems are usually based on nonlinear data or models. Nonlinear models typically exhibit symmetry, nonconvexity, and multiple equivalent solutions. Symmetry problems involve the deep integration and clever application of mathematical principles, physical laws, and engineering design. By conducting in-depth research and utilizing the symmetry of deep learning networks, more efficient and powerful deep learning models can be designed to achieve better application results in practical engineering applications.
Dr. Bin Li
Dr. Zhuang Li
Dr. Jiankang Zhang
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
- deep learning
- symmetry in engineering problems
- deep learning networks with symmetrical structure
- optimization of deep neural networks
- optimization methods in engineering
- design of deep neural networks
- the application of deep learning in engineering
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.
Further information on MDPI's Special Issue policies can be found here.
