AI-Based and Data-Driven Modeling and Control: Mathematical Methods and Industrial Applications

Dear Colleagues,

The integration of artificial intelligence and data-driven methodologies into control system design is rapidly transforming how we model, analyze, and regulate complex industrial processes. This Special Issue aims to gather cutting-edge research at the intersection of mathematics, control theory, and AI-enabled modeling, focusing on both theoretical foundations and real-world implementations.

As traditional model-based approaches face limitations in high-dimensional, nonlinear, or uncertain environments, AI-based and data-driven frameworks offer promising alternatives. However, their success in safety-critical and industrial applications relies on solid mathematical underpinnings, including interpretability, stability, and robustness guarantees.

For this issue, we welcome original research and surveys that advance the understanding of

• Data-driven modeling and identification of dynamical systems;
• Reinforcement learning/machine learning-based control and optimization;
• AI-integrated predictive and adaptive control strategies;
• Scheduling and resource optimization under network constraints;
• Hybrid modeling combining physical laws and learning components (e.g., physics-informed ML);
• Distributional and probabilistic control in uncertain environments;
• Stability, convergence, and performance analysis for learning-based controllers;
• Applications in industrial transportation, energy systems, autonomous platforms, and logistics.

This Special Issue will provide a platform on which researchers and practitioners may explore how intelligent modeling and control methods—rooted in mathematical principles—can drive innovation in complex engineered systems. Contributions that demonstrate rigorous analysis alongside practical relevance are especially encouraged.

Dr. Yuyang Zhou
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 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.

• AI-based optimization
• industrial process control
• data-driven modeling and control
• model-based reinforcement learning
• AI-integrated control frameworks
• network-constrained systems
• physics-informed neural networks
• uncertainty quantification and propagation
• nonlinear and complex system modeling and control
• real-time optimization and control