Objectives Define methodologies for the design optimization of complex structural or mechanical problems. In particular, the group is interested in highly nonlinear structural and multidisciplinary problems. These problems are typically associated with high computational times and a high sensitivity to uncertainties. Incorporate the presence of loading (environmental), manufacturing and design uncertainties in the design  process (Reliability-based Design Optimization (RBDO) and robust design). Use of techniques from the fields of statistics and computer science that have not yet migrated towards engineering design. For instance, the group is currently focusing on the use of data mining and machine learning techniques. Apply the methodologies to real-world problems such as vehicle crashworthiness or the design of biomechanical devices. Current work The group is currently focusing on: Use of data mining and machine learning techniques in probabilistic design. Reliability-based design for nonlinear aeroelasticity (Limit-Cycle Oscillations). Robust optimal design: structural impact. Multiscale modeling and tailoring of metal foams. Evolutionary optimization algorithms (Cellular Automata). Algorithms for Multidisciplinary Design Optimization with maximum disciplinary autonomy. Structural optimization with acoustic constraints. Fluid-structure interaction simulation and probabilistic design. Application to aortic aneurysm problems.