We describe and analyze a variance reduction approach for Monte Carlo (MC) sampling
that accelerates the estimation of statistics of computationally expensive simulation models …

This paper presents a Bayesian multifidelity uncertainty quantification framework, called
MFNets, which can be used to overcome three of the major challenges that arise when data …

Optimal decision making under uncertainty is critical for control and optimization of complex
systems. However, many techniques for solving problems such as stochastic optimal control …

We present an approach for constructing a surrogate from ensembles of information sources
of varying cost and accuracy. The multifidelity surrogate encodes connections between …

Surrogate models for computational simulations are inexpensive input-output approximations
that allow expensive analyses, such as the forward propagation of uncertainty and …

Multifidelity uncertainty quantification (MF UQ) sampling approaches have been shown to
significantly reduce the variance of statistical estimators while preserving the bias of the …

This paper analyzes the approximate control variate (ACV) approach to multifidelity
uncertainty quantification in the case where weighted estimators are combined to form the …

Methods for efficient simulations of multidimensional quantum dynamics are essential for
theoretical studies of chemical systems where quantum effects are important, such as those …

Predictive analysis of complex computational models, such as uncertainty quantification (UQ),
must often rely on using an existing database of simulation runs. In this paper we consider …

Stochastic optimal control problems frequently arise as motion control problems in the context
of robotics. Unfortunately, all existing approaches that guarantee arbitrary precision suffer …