The objectives of CODES lab are:

• Develop methodologies for the design optimization of complex structural or mechanical problems.  The group is particularly interested in highly nonlinear structural and multidisciplinary problems. These problems are typically associated with high computational times and a high sensitivity to uncertainties.
• Propagate various types of uncertainties though the computational modeling and 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. The group has been focusing on machine learning techniques such as support vector machines (SVMs) or data mining approaches (e.g., clustering).
• Develop multi-fidelity modeling techniques to fuse experimental and simulation data (multi-fidelity surrogate construction and model management).
• Apply the methodologies to a wide range of real-world problems such as vehicle crashworthiness, aeroelaticity, biomechanical devices, and even musical instruments.