During efforts to improve precision engineering, a new method has surfaced to break the limitations of current modelling techniques. Researchers Dr. Chen Luo, Ao-Jin Li, Jiang Xiao, and Ming Li, led by Professor Yun Li from the Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, have introduced a practical solution. Their study, published in the Scientific Reports, explains a method called grey-box state-space model (SSM), which combines simplicity, accuracy, and transparency for dynamic modelling.
Combining basic scientific principles with advanced data analytics, this grey-box hybrid model merges a white box of physical laws, which are symbolic rules that describe how things like motion and energy behave in the real world, and machine learning techniques with a number of black boxes using universal function approximators like connected artificial neural networks, which involve data-driven training or prediction. This combination creates a model that not only interprets but adjusts to varying complexities in real-world scenarios. “By incorporating expert knowledge within a strong AI framework, we ensure that these models are understandable and effective under different conditions,” said Professor Li.
Testing this approach on a highly sensitive temperature control system used in cleanrooms for manufacturing demonstrated its effectiveness. These systems, which are environments free from dust and contaminants, demand extremely accurate temperature regulation for both air and water. The grey-box model exceeded the performance of traditional methods, managing unpredictable system changes and unique characteristics better than standalone approaches.
Developed with an SSM structure, the grey-box model uses two transformations. One transforms an irregular nonlinear differential equation set into a regular, linear-like global SSM white box, and the other transforms its state-dependent parameters into regular local function approximators. Thus physical laws form the foundation of the model while employing machine learning to adjust parameter settings dynamically. For instance, in cleanroom air temperature control, this model relied on both energy transfer principles, which explain how heat moves between objects, and real-time data, which is information collected as events happen, to achieve optimal performance. “Our model can predict behavior in new scenarios with remarkable accuracy,” Professor Li explained, “making it essential for industries where operating conditions often change.”
Solving common challenges like incomplete information, which refers to gaps or missing data, and inefficient calculations, the grey-box framework displayed a higher ability to adapt while still providing insights into how it works. This blend of adaptability and clarity is essential for practical industrial use.
Future possibilities for grey-box SSM span diverse fields, including aerospace, which involves the design and production of aircraft and spacecraft, and energy management, which focuses on using resources efficiently. Professor Li sees this method as part of a broader move towards smarter, more transparent technology in engineering. This shift represents a future where machines not only perform but also explain their functions, enhancing both trust and efficiency. Professor Li remarked, “Our goal is to develop efficient and explainable ‘AI for Engineering’ tools.”
Journal Reference
Luo, C., Li, A., Xiao, J., Li, M., & Li, Y. “Explainable and Generalizable AI-Driven Multiscale Informatics for Dynamic System Modelling.” Scientific Reports, 2024. https://doi.org/10.1038/s41598-024-67259-4
About the Authors
Yun Li (Fellow, IEEE) received the Ph.D. degree from the University of Strathclyde, Glasgow, U.K., in 1990. He worked as an engineer with National Engineering Laboratory and Industrial Systems and Control Ltd., both in Glasgow. From 1991 to 2018, he was an Intelligent Systems Lecturer, Senior Lecturer, and Professor with the University of Glasgow, Glasgow, and the Founding Director of the University of Glasgow Singapore, Singapore. He is currently a Chair Professor with the Shenzhen Institute for Advanced Study, University of Electronic Science and Techonology of China, Shenzhen, China. He has authored or coauthored over 300 papers, and one of them has been the most popular paper in IEEE Transactions on Control System Technology almost every month since its publication in 2005. Prof. Li is interested in the next generation, explainable artificial intelligence and its engineering applications.
Dr. Chen Luo her PhD from China University of Geosciences, Wuhan, China. She is currently a postdoctoral fellow intersted in artificial intelligence for engineering. Her work addresses critical scientific challenges in the context of smart cities and large-scale engineering projects, ensuring they are both robust and understandable. Dr. Luo’s contributions aim to support smarter, safer, and more sustainable urban development, making her a key figure in the integration of AI with engineering sciences.
Ao-Jin Li received the B.S. degree from Henan Polytechnic University in 2021. He is currently pursuing a Doctor of Engineering degree at Shenzhen Institute for Advanced Study, University of Electronic Science and Techonology of China, Shenzhen, China. His research interests include intelligent control, robotics and embodied intelligence.
Jiang Xiao received the B.S. degree from the University of Electronic Science and Technology of China, Chengdu, China, in 2022. He is currently pursuing the M.S. degree at the Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, China. His recent research interests include computational intelligence, large language models and its applications to communication systems.
Ming Li received his B.S. degree from South China Normal University, Guangzhou, China. He is currently a research student at the Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, China. His work focuses on neural network compression. Ming Li is dedicated to advancing machine learning techniques, particularly in neural network optimization.
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