Advanced Machine Learning Research in Complex System
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: 31 October 2026
Special Issue Editors
Interests: causal inference; prediction/generation; system identification techniques for complex networks and systems science; artificial intelligence related theory and applications
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Advanced machine learning is increasingly central to the understanding, modeling, and management of complex systems composed of many interacting components that exhibit nonlinear, uncertain, and emergent behaviors. Such systems arise in domains including biological and social networks, cyber–physical infrastructures, energy and transportation systems, and large-scale industrial and information platforms. This Special Issue, entitled “Advanced Machine Learning Research in Complex System,” aims to provide a focused venue for methodological advances at this interface. We particularly welcome contributions that develop new learning paradigms, models, and algorithms tailored to complex systems—such as graph-and network-based learning, representation learning for dynamical systems, causal and structure-learning methods, scalable optimization and inference schemes, and robust or safe reinforcement learning—together with accompanying theoretical analysis. Of special interest are works that rigorously address fundamental challenges including high dimensionality, multi-scale and spatio-temporal dependencies, distribution shifts, partial observability, robustness to uncertainty, safety constraints, and interpretability of learned models and policies. While application studies are welcome, they should primarily serve to validate and elucidate methodological innovations in real-world complex systems. By bringing together researchers and practitioners from machine learning, systems science, and domain-specific fields, this Special Issue seeks to advance advanced machine learning as a key methodological enabler for the analysis, prediction, control, and governance of complex systems.
Dr. Duxin Chen
Dr. Mengli Wei
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics 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 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
- complex systems
- machine learning
- distributed control and optimization
- decentralized learning
- privacy protection of machine learning in complex systems
- time series analysis in complex systems
- applications of complex systems
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