Submission Deadline: 31 March 2027 View: 65 Submit to Special Issue
Prof. Xiong Luo
Email: xluo@ustb.edu.cn
Affiliation: School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China
Research Interests: computational intelligence, machine learning, natural language processing
Assoc. Prof. Manman Yuan
Email: yuanman@imu.edu.cn
Affiliation: School of Computer Science, Inner Mongolia University, Hohhot, China
Research Interests: computational intelligence, brain science, deep learning
The industrial system is the nerve center widely used across critical infrastructure sectors such as power, petroleum and petrochemicals, water conservancy, transportation, nuclear facilities, and emerging biomanufacturing industries. With the increasing demand for intelligent manufacturing, the essence of the modern industrial system has shifted toward human-cyber-physical systems that integrate physical processes, intelligent control, and computational biology. Therefore, the industrial system has become a complex system with many factors, and the modeling and design guided by machine learning (ML) are gradually applied to large-scale production with higher accuracy.
In recent years, industrial production has reached an unprecedented level. In the complex industrial system operation process, huge amounts of production, operational, control, biological, and other data are generated, which can generally be characterized by massiveness, multi-source, heterogeneity, multimodality, and high dimensionality. The rapid accumulation of such data not only promotes the development of intelligent manufacturing and industrial biotechnology but also brings significant challenges for system modeling and optimization. Due to its limited representational capacity, the traditional modeling method cannot fully extract the information contained in the big data of the industrial system. Thus, intelligent modeling aims to fully exploit the useful information in big data by constructing an appropriate intelligent modeling structure. Accordingly, ML-guided directed evolution has become a new paradigm for industrial design, enabling the optimization of complex functions. Both structured and unstructured data from industrial systems can be used in ML to predict how sequence maps function, without requiring explicit knowledge of the underlying physical or biological mechanisms. Then it can help the system to make more accurate intelligent decisions and promote the concept of digital twins.
The focus of this Special Issue is on the development of ML-guided intelligent modeling to solve problems in industry. Articles submitted to this Special Issue may also address intelligence algorithms for the systematic modeling, simulation, and optimization of complex industrial systems. We invite researchers to contribute original research articles and review articles that will stimulate the ongoing research on the applications of data-enabled intelligence to complex industrial systems and on computing techniques to assess/solve engineering problems.
Topics of interest include but are not restricted to:
- Industrial applications of complex system theory
- Machine learning and deep learning for complex system modeling
- Filter-aided methods for industrial processes
- Data-driven control of industrial systems
- Artificial intelligence for system optimization
- Neurodynamic analysis for industrial process
- Detection classification for complex industrial systems
- Distributed multi-agent modeling algorithms and its industrial applications
- Robust modeling methods for industrial process
- The other related topics
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