Data Science and Systems Complexity

Engineering researchers who are experts in Mechanical and Industrial Engineering, Applied Mathematics, Computer Science Engineering and Artificial Intelligence participate in the theme Data Science and Systems Complexity that has a strong engineering component within the research lines:

• Complex Systems and their analysis, control and design

'Complexity' here refers to the fact that these systems are large-scale and high-dimensional (possibly after a discretization process), as well as to the complexity of the resulting dynamics and the large amount of data generated by such systems or used to identify them.

The research questions focus on: the development of scalable methodologies for analysis and control/design; visualization to explore large complex systems; and techniques to engineer systems-of-systems that combine resources and capabilities to create a new, more complex systems which offers more functionality and performance than the sum of the constituent systems. Underlying the large diversity of application areas there is a set of core methodologies, which are characterized by keywords such as 'complex networks', 'distributed modeling and control', 'architecture and functionality', 'high-dimensional computing', 'optimization', 'model reduction', and 'complex system dynamics.'

• Advanced Instrumentation and Big Data

Research focuses on modelling of large, complex instruments, such as space-based telescopes, mechatronic systems, or sensor networks. Key issues are reducing large data volumes associated with complex instruments and high-precision engineering to manageable size; developing techniques that make complex advanced instrumentation cheaper and more reliable; integrating of new visualization and visual analytics methods within existing workflows; handling the multi-* (multi-disciplinary, multi-lifecycle, multi-organization) aspects of the systems through optimizing engineering processes; and translating the high-tech concepts into applications for industry.