SOUPy: Stochastic PDE-constrained optimization under high-dimensional uncertainty in Python

Computational models governed by partial differential equations (PDEs) are frequently used by engineers to optimize the performance of various physical systems through decisions relating to their configuration (optimal design) and operation (optimal control).However, the ability to make optimal choices is often hindered by uncertainty, such as uncertainty in model parameters (e.g., material properties) and operating conditions (e.g., forces on a structure).The need to account for these uncertainties in order to arrive at robust and risk-informed decisions thus gives rise to problems of optimization under uncertainty (OUU) (D.