Optimization of system planning, design, manufacturing, controls, and operations are critical for ensuring that engineering systems maximize performance. However, each stage of a system’s lifecycle is rife with uncertainty. These uncertainties can arise from uncontrolled factors such as manufacturing precision and operating conditions, leading to aleatoric uncertainty. These uncertainties can also arise because the models used for optimization have unknown parameters that require calibration, leading to the epistemic uncertainty. As a result, optimization of engineering systems requires quantifying and accounting for a variety of (wanted or unwanted) outcomes in the presence of uncertainties. Consideration of this uncertainty is vital to ensure safety; safeguard against costly design alterations late in the design cycle; minimize the probability of operational failures; and maximize the likelihood of mission success. The traditional and widely used approach is to add safety or robustness margins to compensate for uncertainties after a deterministic optimization is performed. This produces a sense of security but is at best an imprecise recognition of the potential outcomes and results in overly conservative designs that can limit performance. Properly accounting for risk during the optimization could allow for more efficient and well-performing designs. In this minisymposium, contributors will survey the state of the art in optimization under uncertainty for complex systems, ranging from methodological developments to challenging applications. This will cover recent developments focused on multifidelity and surrogate approaches, along with their associated efforts for efficient statistical sampling, stochastic and deterministic optimization algorithms, optimal transport theory and applications, etc. Applications are welcome that focus on design, manufacturing, trajectory optimization, and any other optimization-based tasks throughout an engineered system's lifecycle.