15 citations · 43 across the 7 of their papers we have counts for
8 papers
CWENO Interpolation for Non-Oscillatory Stochastic Collocation in Uncertainty Quantification Problems
Alina Chertock, Arsen S. Iskhakov, Anna Iskhakova +1
Uncertainty quantification (UQ) in mathematical models is essential for accurately predicting system behavior under variability. This study provides guidance on method selection fo…
Spline-Based Stochastic Collocation Methods for Uncertainty Quantification in Nonlinear Hyperbolic PDEs
Alina Chertock, Arsen S. Iskhakov, Safa Janajra +1
In this paper, we study the stochastic collocation (SC) methods for uncertainty quantification (UQ) in hyperbolic systems of nonlinear partial differential equations (PDEs). In the…
New High-Order Numerical Methods for Hyperbolic Systems of Nonlinear PDEs with Uncertainties
Alina Chertock, Michael Herty, Arsen S. Iskhakov +3
In this paper, we develop new high-order numerical methods for hyperbolic systems of nonlinear partial differential equations (PDEs) with uncertainties. The new approach is realize…
Direct Numerical Simulation of Low and Unitary Prandtl Number Fluids in Reactor Downcomer Geometry
Cheng-Kai Tai, Tri Nguyen, Arsen S. Iskhakov +3
Buoyancy effect on low-flow condition convective heat transfer of non-conventional coolants, such as liquid metal and molten salts, is a crucial safety factor to advanced reactors…
Data-driven Hi2Lo for Coarse-grid System Thermal Hydraulic Modeling
Arsen S. Iskhakov, Nam T. Dinh, Victor Coppo Leite +1
Traditional 1D system thermal hydraulic analysis has been widely applied in nuclear industry for licensing purposes due to its numerical efficiency. However, such codes are inheren…
Review of Physics-based and Data-driven Multiscale Simulation Methods for Computational Fluid Dynamics and Nuclear Thermal Hydraulics
Arsen S. Iskhakov, Nam T. Dinh
Modeling of fluid flows requires corresponding adequate and effective approaches that would account for multiscale nature of the considered physics. Despite the tremendous growth o…
Physics-integrated machine learning: embedding a neural network in the Navier-Stokes equations. Part II
Arsen S. Iskhakov, Nam T. Dinh
The work is a continuation of a paper by Iskhakov A.S. and Dinh N.T. "Physics-integrated machine learning: embedding a neural network in the Navier-Stokes equations". Part I // arX…
Physics-integrated machine learning: embedding a neural network in the Navier-Stokes equations. Part I
Arsen S. Iskhakov, Nam T. Dinh
In this paper the physics- (or PDE-) integrated machine learning (ML) framework is investigated. The Navier-Stokes (NS) equations are solved using Tensorflow library for Python via…