activity
20172026
most citedCompressive sensing adaptation for polynomial chaos expansions

67 citations · 132 across the 9 of their papers we have counts for

collaborators

9 papers

physics.flu-dyn2026

Receptivity and Biorthogonal Decomposition in a Reacting Temporal Mixing Layer

Sriram P. Kalathoor, Joseph C. Oefelein

We examine receptivity and biorthogonal decomposition in a reacting temporal mixing layer using direct and adjoint eigenmodes of a finite-thickness compressible linearized operator…

physics.flu-dyn2026

Planar Lagrangian transport and scalar-gradient organization in a turbulent reacting shear layer

Sriram P. Kalathoor, Joseph C. Oefelein

We analyze planar Lagrangian transport and scalar-gradient organization in a supersonic, reacting hydrogen-air temporal mixing layer using time-resolved mid-plane data from a three…

physics.flu-dyn2026

Event-level compression--chemistry coupling in a supersonic reacting temporal mixing layer

Sriram P. Kalathoor, Joseph C. Oefelein

Compression and heat release interact intermittently in high-speed reacting shear layers, and whole-field averages can obscure their coupling. We examine this coupling in a superso…

physics.flu-dyn2026

Near-Field Combustion-Noise Source Dynamics in a Reacting Supersonic Temporal Mixing Layer

Sriram P. Kalathoor, Joseph C. Oefelein

Compressibility and chemical reactions in reacting flows provide source mechanisms for pressure fluctuations whose signatures depend on the flow, source distribution, and acoustic…

physics.flu-dyn2026

Filtering effects on entropy transport and entropy-production structure in a supersonic reacting shear layer

Sriram P. Kalathoor, Joseph C. Oefelein

Spatial filtering is examined in time-resolved mid-plane DNS fields of a supersonic reacting shear layer using a sequence of box filters. The analysis tracks a nondimensional entro…

math.PR2018

Entropy-based closure for probabilistic learning on manifolds

C. Soizea, R. Ghanem, C. Safta +7

In a recent paper, the authors proposed a general methodology for probabilistic learning on manifolds. The method was used to generate numerical samples that are statistically cons…

stat.ML2018★ 67 cited

Compressive sensing adaptation for polynomial chaos expansions

Panagiotis Tsilifis, Xun Huan, Cosmin Safta +5

Basis adaptation in Homogeneous Chaos spaces rely on a suitable rotation of the underlying Gaussian germ. Several rotations have been proposed in the literature resulting in adapta…

physics.data-an2017★ 33 cited

Global Sensitivity Analysis and Estimation of Model Error, Toward Uncertainty Quantification in Scramjet Computations

Xun Huan, Cosmin Safta, Khachik Sargsyan +6

The development of scramjet engines is an important research area for advancing hypersonic and orbital flights. Progress toward optimal engine designs requires accurate flow simula…

stat.CO2017★ 32 cited

Compressive Sensing with Cross-Validation and Stop-Sampling for Sparse Polynomial Chaos Expansions

Xun Huan, Cosmin Safta, Khachik Sargsyan +4

Compressive sensing is a powerful technique for recovering sparse solutions of underdetermined linear systems, which is often encountered in uncertainty quantification analysis of…