AI/ML and digital twin/computer simulation provide a powerful analytics tool for the prediction, optimization, and uncertainty quantification of a wide range of smart cyber-physical systems arising in diverse domains. Building upon my foundational and algorithmic research in AI/ML and digital twin/computer simulation, I work on how to assess/certify the resilience/sustainability of systems found in energy, health care, manufacturing, transportation, and how to design and operate resilient and sustainable systems. Resilient (power, manufacturing, transportation) systems • Zhou, C., Xu, J., Miller-Hooks, E., Zhou, W., Chen, C.-H., Lee, L.-H., Chew, E.-P., and Li, H., 2021. Analytics with digital-twinning: A decision support system for maintaining a resilient port. Decision Support Systems, 143 (113496) Download • Xu, J., Yao, R., and Qiu, F. 2021. Mitigating Cascading Outages in Severe Weather Using Simulation-based Optimization. IEEE Transactions on Power Systems, 36(1), pp.204-213. Download • Fujimoto, R.M., Celik, N., Damgacioglu, H., Hunter, M., Jin, D., Son, Y.J. and Xu, J., 2016, December. Dynamic data driven application systems for smart cities and urban infrastructures. In 2016 Winter Simulation Conference (WSC) (pp. 1143-1157). IEEE. • Li, M., F. Yang, R. Uzsoy, and J. Xu. 2016. A metamodel-based Monte Carlo simulation approach for responsive production planning of manufacturing systems. Journal of Manufacturing Systems 38, 114-133. Download Sustainable systems • Xu, J., Vidyashankar, A., and Nielsen, M. 2014. "Drug Resistance or Re-emergence? Simulating Equine Parasites", ACM Transactions on Modeling and Computer Simulation, 24(4), Article 20. Download |
