24 citations · 47 across the 5 of their papers we have counts for
6 papers
S-Frame Discrepancy Correction Models for Data-Informed Reynolds Stress Closure
Eric L. Peters, Riccardo Balin, Kenneth E. Jansen +2
Despite their well-known limitations, RANS models remain the most commonly employed tool for modeling turbulent flows in engineering practice. RANS models are predicated on the sol…
On transfer learning of neural networks using bi-fidelity data for uncertainty propagation
Subhayan De, Jolene Britton, Matthew Reynolds +3
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 tra…
Bi-fidelity Stochastic Gradient Descent for Structural Optimization under Uncertainty
Subhayan De, Kurt Maute, Alireza Doostan
The presence of uncertainty in material properties and geometry of a structure is ubiquitous. The design of robust engineering structures, therefore, needs to incorporate uncertain…
Pass-efficient methods for compression of high-dimensional turbulent flow data
Alec M. Dunton, Lluís Jofre, Gianluca Iaccarino +1
The future of high-performance computing, specifically on future Exascale computers, will presumably see memory capacity and bandwidth fail to keep pace with data generated, for in…
Stochastic Subspace Descent
David Kozak, Stephen Becker, Alireza Doostan +1
We present two stochastic descent algorithms that apply to unconstrained optimization and are particularly efficient when the objective function is slow to evaluate and gradients a…
Multi-fidelity uncertainty quantification of irradiated particle-laden turbulence
Lluis Jofre, Gianluca Geraci, Hillary Fairbanks +2
The study of complex systems is often based on computationally intensive, high-fidelity, simulations. To build confidence in the prediction accuracy of such simulations, the impact…