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Heterogeneous computers with processors and accelerators are becoming widespread in scientific computing. However, it is difficult to program hybrid architectures and there is no commonly accepted programming model. Ideally, applications should be written in a way that is portable to many platforms, but providing this portability for general programs is a(More)
We demonstrate an adjoint based approach for accelerating Monte Carlo estimation of risk, and apply it to estimating the probability of unstart in a SCRamjet engine under uncertain conditions that are characterized by various Gaussian and non-Gaussian distributions. The adjoint equation is solved with respect to an objective function that is used to(More)
Adjoint methods are widely used in various areas of computational science to efficiently obtain sensitivities of functionals which result from the solution of partial differential equations (PDEs). In addition, adjoint methods have been used in other settings including error estimation, uncertainty quantification, and inverse problem formulations. When(More)
A modeling paradigm is developed to augment predictive models of turbulence by effectively utilizing limited data generated from physical experiments. The key components of our approach involve inverse modeling to infer the spatial distribution of model discrepancies, and, machine learning to reconstruct discrepancy information from a large number of(More)
Standard Gaussian Process (GP) regression, a powerful machine learning tool, is computationally expensive when it is applied to large datasets, and potentially inaccurate when data points are sparsely distributed in a high-dimensional feature space. To address these challenges, a new multiscale, spar-sified GP algorithm is formulated, with the goal of(More)
In the recent past, adjoint methods have been successfully applied in error estimation of integral outputs (functionals) of the numerical solution of partial differential equations. The adjoint solution can also be used as a grid adaptation indicator, with the objective of optimally targeting and reducing the numerical error in the functional of interest(More)
— We use retrospective cost adaptive control (RCAC) to control the thrust generated by a scramjet. A quasi-one-dimensional version of the mass, momentum, and energy conservation equations of compressible fluid flow with heat release is used to model the physics of the scramjet. First, we study the dynamic behavior of the scramjet model. Then, we apply(More)
The development of reduced models for complex systems that lack scale separation remains one of the principal challenges in computational physics. The optimal prediction framework of Chorin et al. [1], which is a reformulation of the Mori-Zwanzig (M-Z) formalism of non-equilibrium statistical mechanics, provides a methodology for the development of(More)
The numerical prediction of scramjet in-flight performance is a landmark example in which current simulation capability is overwhelmed by abundant uncertainty and error. The aim of this work is to develop a decision-making tool for balancing the available computational resources in order to equally reduce the effects of all sources of uncertainty and error(More)