Complex Systems Modelling, Data Analysis, and Machine Learning with Applications

Dear Colleagues,

Complex systems, characterized by nonlinearity, emergence, self-organization, and high-dimensional interactions, are ubiquitous in fields such as biology, engineering, social sciences, and physics. The modelling, analysis, and prediction of such systems pose significant challenges due to their intricate structures and dynamic behaviours. Recent advances in data analytics and machine learning have provided powerful tools for uncovering hidden patterns, forecasting system evolution, and improving decision-making processes within complex systems. This Special Issue aims to explore cutting-edge techniques and emerging trends in the analysis of complex systems, with a focus on innovations that bridge the gap between technological advancements and real-world applications.

Over the past decade, the explosion of data, computational capabilities, and machine learning techniques have driven the development of state-of-the-art methods for the analysis of complex systems. Current research trends are shifting towards data-driven and interdisciplinary approaches for understanding, modelling, and optimizing these systems. Rather than relying solely on traditional analytical methods, researchers are increasingly integrating machine learning, graph-based learning, online learning, and contrastive learning to model complex behaviours, adapt to environmental dynamics, and improve predictive accuracy. Key challenges remain in developing scalable, intelligent, and application-oriented solutions that can effectively address the multifaceted demands of modern complex systems.

The Special Issue on “Complex Systems Modelling, Data Analysis, and Machine Learning with Applications” aims to provide a platform for recent findings in the field of complex systems research, with a particular emphasis on methodological advances and real-world implementations. Researchers and practitioners are encouraged to submit research articles, case studies, and review papers that contribute novel methodologies, theoretical insights, or practical applications in this multidisciplinary field. For this Special Issue, topics of interest include, but are not limited to, the following:

• Big data analytics for complex systems;
• Time series analysis and dynamical systems;
• Optimization and control techniques for complex system;
• Progression prediction and monitoring in complex systems;
• Agent-based modelling and simulations;
• Multi-scale and hierarchical modelling approaches;
• Anomaly detection and outlier analysis in complex systems;
• Transfer learning and domain adaptation in complex systems.

Dr. Yanjiao Li
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. 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.

• complex systems
• data analytics
• machine learning
• system modelling
• optimization and control
• multivariate analysis