Zhao, XiangxueThe goal in this dissertation is to develop new data-driven techniques for prediction, design, and control of the behavior of a variety of engineering systems. The data used can be obtained from a variety of sources, including from offline, high-fidelity system’s simulation, physical experiments, and online, sparse measurements from sensors. Three inter-related research directions are followed in this dissertation. Following the first direction, the author presents a multi-step-ahead prediction technique for evaluating a single-response (or single-output of the) system’s behavior through an integration of the data obtained offline from the system’s high-fidelity simulation, and online from single sensor measurements. With regard to the first research direction, the key contribution includes a reasonably fast and accurate prediction strategy that can be used, among others, for online, multi-step ahead forecasting of the system’s operational behavior. Building on the work from the first direction, the author follows a second research direction to present a multi-step ahead prediction technique, this time for a multi-response system’s behavior, that can be used for evaluating various system’s designs and corresponding operations. Data in this case is obtained from the offline, high-fidelity system’s simulations, and online sparse measurements from multiple sensors (or limited number of physical experiments). The main contribution for this second direction is in construction of a new data-driven, multi-response prediction framework that has a robust predictive capability. Along the third research direction, a data-driven technique is used for prediction and co-optimization of a system’s design and control. The data in this case is obtained from sensor measurements or a simulator. The main contribution achieved through the third direction is a new data-driven reinforcement learning-based prediction and co-optimization approach. The methods from this dissertation have numerous applications, including those demonstrated here: (i) assessment of safe aircraft flight conditions (Chapters 2 and 3), (ii) evaluation of design and operation of a robotic appendage (Chapter 3), and (iii) design and control of a traffic system (Chapter 4).enDissertationEngineeringStatisticsMechanical engineeringCo-optimizationData-driven predictionStatistical LearningUncertainty