Our team develops new methods, frameworks and modeling architectures, techniques and algorithms, for the safety and risk analysis of complex engineered systems, based on a holistic and systemic viewpoint. The modeling, simulation and optimization methods, frameworks, architectures, techniques and algorithms that we develop, integrate a number of competences for viewing and solving the problems from the different, multidisciplinary system perspectives (topological and functional, static and dynamic, etc.) that are needed, and giving due account to the existing uncertainties. The range of application includes industrial systems like renewable energy systems, electric power grids, smart grids, nuclear power plant components, oil and gas systems, automotive and railway transportation systems.

Currently, our research is organized around 2 main axes:

Axis 1: Characterization, representation, modeling and prediction of the aging and failure behavior of systems, structures and components : aiming at modeling and assessing component degradation, analyzing and building maintenance solutions, and carrying out system simulation for reliability, availability, maintainability and safety (RAMS) analysis by multi-state, physic, Bayesian and Markov chains models, Monte Carlo simulation. A particular focus is on failure prediction and prognostics of critical components, by data-driven approaches, e.g. adaptive artificial neural networks, support vector machines and the like.

Axis 2: Modeling and optimization of Energy production and network systems: focusing on modeling, simulating and optimizing of energy production and network systems, i.e., nuclear power plants, power grids, microgrids, smart grids, and also the interdependencies between energy network systems and other critical infrastructure systems such as tranportation and communication systems. The analysis of these systems cannot be carried out only with classical methods of system decomposition and logic analysis; a framework is needed to integrate a number of methods capable of viewing the problem from different perspectives (topological and functional, static and dynamic, ...) and properly treating the related uncertainties by probabilistic and non-probabilistic methods.