Robustness, prevention, and resilience: design under uncertainty for complex engineering systems

The complexity of modern engineering systems poses a significant challenge to designers and decision-makers. Early-stage design decisions are critical because they lock in many aspects of the system performance and cost. Yet these decisions are made when uncertainty is greatest, making it difficult to guarantee robustness, resilience and reliability. This work proposes an approach in which we represent the design process using a stochastic process model. In doing so, we create a mathematical model for quantification and management of uncertainty that casts system design as a Bayesian estimation problem. Feedback of system sensitivities guides both design decisions and resource allocation decisions. In this way, the design process becomes a series of decisions targeted to achieve system specifications while also managing the associated uncertainty.