1 citations · 3 across the 5 of their papers we have counts for
6 papers
Model Order Reduction Techniques for the Stochastic Finite Volume Method
Ray Qu, Jesse Chan, Svetlana Tokareva
The stochastic finite volume method (SFV method) is a high-order accurate method for uncertainty quantification (UQ) in hyperbolic conservation laws. However, the computational cos…
Setpoint Tracking and Disturbance Attenuation for Gas Pipeline Flow Subject to Uncertainties using Backstepping
Bhathiya Rathnayake, Anatoly Zlotnik, Svetlana Tokareva +1
In this paper, we consider the problem of regulating the outlet pressure of gas flowing through a pipeline subject to uncertain and variable outlet flow. Gas flow through a pipe is…
The Tensor-Train Stochastic Finite Volume Method for Uncertainty Quantification
Steven Walton, Svetlana Tokareva, Gianmarco Manzini
The stochastic finite volume method offers an efficient one-pass approach for assessing uncertainty in hyperbolic conservation laws. Still, it struggles with the curse of dimension…
Stochastic Finite Volume Method for Uncertainty Management in Gas Pipeline Network Flows
Saif R. Kazi, Sidhant Misra, Svetlana Tokareva +2
Natural gas consumption by users of pipeline networks is subject to increasing uncertainty that originates from the intermittent nature of electric power loads serviced by gas-fire…
Stochastic Active Discretizations for Accelerating Temporal Uncertainty Management of Gas Pipeline Loads
Jake J. Harmon, Svetlana Tokareva, Anatoly Zlotnik
We propose a predictor-corrector adaptive method for the simulation of hyperbolic partial differential equations (PDEs) on networks under general uncertainty in parameters, initial…
Adaptive Uncertainty Quantification for Stochastic Hyperbolic Conservation Laws
Jake J. Harmon, Svetlana Tokareva, Anatoly Zlotnik +1
We propose a predictor-corrector adaptive method for the study of hyperbolic partial differential equations (PDEs) under uncertainty. Constructed around the framework of stochastic…