Symmetry in Artificial Intelligence and Machine Learning
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "A: Computer Science".
Deadline for manuscript submissions: 30 June 2027 | Viewed by 19
Editors
Interests: artificial intelligence and its engineering applications; intelligent detection and digital twin
Special Issue Information
Dear Colleagues,
Artificial intelligence (AI) and machine learning (ML) have emerged as transformative paradigms across science, engineering, and society, enabling systems to learn from data, recognize complex patterns, and make intelligent decisions with minimal human intervention. Motivated by breakthroughs in deep learning, large-scale neural architectures, and generative models, AI/ML is now driving innovation in fields ranging from fundamental physics and materials science to healthcare, autonomous systems, and natural language understanding.
Significant theoretical and practical advances have been made since the inception of modern machine learning. Theoretical aspects of this work include deep learning theory, optimization algorithms, probabilistic graphical models, reinforcement learning, federated learning, explainable AI (XAI), and neuro-symbolic integration, while experimental and applied aspects cover computer vision, large language models, scientific machine learning (SciML), AI for drug discovery, robotics, and physics-informed neural networks, among others. Notably, the concept of symmetry—central to this journal—plays a profound role in AI/ML, from equivariant neural networks and symmetry-aware data augmentation to invariant feature extraction and the application of Noether’s theorem in learning dynamics.
This Special Issue of Symmetry, titled "Symmetry in Artificial Intelligence and Machine Learning", provides a platform for the publication of recent research and review articles in both theoretical and applied areas. We welcome contributions that explore novel algorithms, theoretical foundations, interdisciplinary applications, and the fundamental role of symmetry principles in advancing intelligent systems. We kindly invite all colleagues working in this area to contribute to this Special Issue.
Please note that all submissions should be in the full scope of Symmetry.
Dr. Jian Zhao
Dr. Tianxing Li
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 250 words) can be sent to the Editorial Office for assessment.
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-anonymized 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
- artificial intelligence
- machine learning
- deep learning
- neural networks
- computer vision
- natural language processing
- reinforcement learning
- explainable AI
- federated learning
- scientific machine learning
- symmetry in machine learning
- generative models
- physics-informed neural networks
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