Abstract: 

The challenge of optimal information gathering-for the purpose of inference, prediction, design, or control-pervades fields ranging from geophysics to chemical engineering and beyond. These questions can be formalized through the framework of optimal experimental design. Yet extending classical design methodologies to tackle problems of greater scale and dynamic complexity, and to find optimal sequential designs, requires new algorithms and formulations. This minisymposium will gather a wide variety of approaches focusing on design for large-scale inverse problems and nonlinear models, design in the presence of model error, and the approximation and optimization of information metrics. Relevant techniques include surrogate modeling, model reduction, sparse quadrature, asymptotic approximations, PDE-constrained optimization, stochastic optimization, and approximate dynamic programming. We invite contributions focused on methodology and motivated by engineering and science applications.