“Engineered systems increasingly derive their behavior from complex interactions between tightly coupled parts, covering multiple disciplines. It is therefore important to develop a scientific foundation that helps us to understand the whole rather than just the parts, that focuses on the relationships among the parts and the emergent properties.” – A World in Motion, System Engineering Vision 2025.
Our research goal is to establish scientific foundations for complex systems engineering and design by developing and synergistically integrating theories from different domains. Especially, we place the decision-centric philosophy at the center of our research, which drives for understanding and discovering fundamental concepts, mechanisms and theories in System Science and Design Science. Our view on the systems goes beyond the physical artifacts but also considers human factors in the loop. Through the framework, methodologies, approaches and tools developed with such a decision-centric philosophy, we aim to solve problems which have impeded the modeling of large-scale complex systems, the promotion of social design and manufacturing, and the realization of artificial design intelligence. Our research also facilitates and scales up the engineering design education, especially in the study of learning behaviors in design, and the development of learning environment for STEM innovation. For a taste of our research, the following HOW and WHY questions that we are interested are provided , but not limited.
- How and why do complex systems, such product-customer systems, as air transportation systems and Internet, come to the structure you observe in real world?
- How to model and then forecast such systems’ evolution through the modeling of individual entities’ decision-making behaviors?
- How can the estimated behaviors of individual entities’ be used to design or improve the performance of existing systems?
- How to guide the design activities of crowd as well as the their design decisions through incentives and mechanism design?
- How can human design knowledge and design creativity be measured, extracted and be further utilized to enable the artificial design intelligence?
- How to accurately estimate customers’ preferences in supporting product design and development?
- How do customers’ preferences diffuse across different regions and how such diffusion patterns can be learnt and modeled in supporting the product design and new product launching strategies?
- How can the estimated designers’ and/or customers’ behaviors be used in engineering design optimization, facilitating design decisions, refining design processes and even guiding technology-push directions?