We outline the use of a data-free inference procedure for estimation of uncertain model parameters for a chemical model of methane-air ignition. The method involves a nested pair of Markov chains, exploring both the data and parametric spaces, to …
In this paper we propose a series of methodologies to address the problems in the NASA Langley Multidisciplinary UQ Challenge. A Bayesian approach is employed to characterize and calibrate the epistemic parameters in problem A, while variance-based …
We study correlations among uncertain Arrhenius rate parameters in a chemical model for hydrocarbon fuel–air combustion. We consider correlations induced by the use of rate rules for modeling reaction rate constants, as well as those resulting from …
Simulation of high-performance computing (HPC) systems plays a critical role in their development - especially as HPC moves toward the co-design model used for embedded systems, tying hardware and software into a unified design cycle. Exploring …
This paper explores the inference of small-scale, atomistic parameters, based on the specification of large, or macroscale, observables. Specifically, we focus on estimating a set of force-field parameters for the four-site, TIP4P, water model, based …
We present a methodology to assess the predictive fidelity of multiscale simulations by incorporating uncertainty in the information exchanged between the atomistic and continuum simulation components. Focusing on uncertainty due to finite sampling …
We outline a methodology for forward uncertainty quantification in systems with uncertain parameters, discontinuous model response, and a limited number of model runs. Our approach involves two stages. First we detect the discontinuity with Bayesian …