A critical aspect of PDE constrained optimization is to account for uncertainty in the underlying physical models, for example in model coefficients, boundary conditions, and initial data. Uncertainty in physical systems is modeled with random variables, however, in practice there may be some nontrivial ambiguity in the underlying probability distribution from which they are sampled. As stochastic optimal control problems are known to be ill-conditioned to perturbations in the sampling distribution, we describe an analytic framework that is better conditioned to such “meta-uncertainties” and conclude with numerical examples.

About the speaker

Sean Carney is an assistant professor in the Department of Mathematics at Union College. He earned his bachelors degree at the Univ. of Michigan, Ann Arbor before getting his PhD in Mathematics at The University of Texas at Austin under the guidance of Prof. Bjorn Engquist, where he also spent two summers at the Center for Computational Science at the Lawrence Berkeley National Lab. Subsequently, he held postdoc positions at both the University of California, Los Angeles and George Mason University.