Due to their high degree of expressiveness, neural networks have recently been used as
surrogate models for mapping inputs of an engineering system to outputs of interest. Once …

Variance-based sensitivity analysis provides a quantitative measure of how uncertainty in a
model input contributes to uncertainty in the model output. Such sensitivity analyses arise in …

Multifidelity simulation methodologies are often used in an attempt to judiciously combine
low-fidelity and high-fidelity simulation results in an accuracy-increasing, cost-saving way …

We present a probabilistic deep learning methodology that enables the construction of
predictive data-driven surrogates for stochastic systems. Leveraging recent advances in …

In this paper, a modified kernel-based ensemble Gaussian mixture filtering (EnGMF) is
introduced to produce fast and consistent orbit determination capabilities in a sparse …

For practical model-based demands, such as design space exploration and uncertainty
quantification (UQ), a high-fidelity model that produces accurate outputs often has high …

Abstract Markov chain Monte Carlo (MCMC) simulation methods are widely used to assess
parametric uncertainties of hydrologic models conditioned on measurements of observable …

Abstract Multilevel Monte Carlo (MLMC) is a recently proposed variation of Monte Carlo
(MC) simulation that achieves variance reduction by simulating the governing equations on …

Multifidelity Monte Carlo (MFMC) estimation combines low-and high-fidelity models to speed
up the estimation of statistics of the high-fidelity model outputs. MFMC optimally samples the …

To reduce computation time while limiting loss in accuracy when propagating an orbit state
probability density function, this work seeks to develop an adaptive approach to multi-fidelity …