1. Computational Methods for Engineering Systems Design
The research objectives are to understand (i) how simulation models and data can be effectively used to support systems design, (ii) how machine learning can be used in conjunction with physics-based models to facilitate design, and (iii) how to leverage emerging community-based design paradigms such as crowdsourcing and open-source design.
- Digital engineering and Industry 4.0
- Model-driven and data-driven design
- Machine learning in engineering design
2. Human Behavior in Engineering Design
The research objectives are (i) to understand human behavior in design at multiple levels ranging from individual cognition to inter-organizational level, (ii) to enable creation of AI-based autonomous systems for engineering design, and (iii) to support human-AI collaboration in design. The approach consists of using computational models of human behavior and cognition, in conjunction with data-driven calibration of models using controlled behavioral experiments, and field data.
- Design automation
- Human-AI collaboration
- Autonomous systems design
3. Secure Design and Manufacturing
The research objectives are (i) to enable collaboration for engineering systems design while preventing loss of intellectual property, (ii) to establish anti-counterfeiting techniques for mechanical components, and (iii) to establish design methods for incorporating security considerations in product design and manufacturing. Approaches include using secure multi-party communication protocols for secure collaboration in scientific computing, using inherent randomness of manufacturing processes for part fingerprinting, and information embedding in parts through process control.
- Counterfeit prevention
- Part fingerprinting