1 citations · 3 across the 6 of their papers we have counts for
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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…
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 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…