Learn More
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)
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)
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)
— 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)
  • 1