Jonas Šukys

Jonas Šukys
Eawag, Switzerland · SIAM

PhD

About

13
Publications
949
Reads
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307
Citations
Citations since 2017
3 Research Items
210 Citations
2017201820192020202120222023010203040
2017201820192020202120222023010203040
2017201820192020202120222023010203040
2017201820192020202120222023010203040
Introduction
I am head of the Scientific Computing group in Eawag. Research interests - Massively parallel high performance computing (HPC) - Scalable parallelization, emerging computing platforms - Uncertainty Quantification and Propagation (UQ+P) for deterministic and stochastic models - Multi-level Monte Carlo (MLMC) methods for optimal hierarchical variance reduction in UQ+P - Hyperbolic nonlinear partial differential equations (PDEs): shallow water, multi-phase cavitation dynamics

Publications

Publications (13)
Preprint
We investigate the process of cloud cavitation collapse through large-scale simulation of a cloud composed of 12500 gas bubbles. A finite volume scheme is used on a structured Cartesian grid to solve the Euler equations, and the bubbles are discretized by a diffuse interface method. We investigate the propagation of the collapse wave front through...
Article
Full-text available
Calibration of individual based models (IBMs), successful in modeling complex ecological dynamical systems, is often performed only ad-hoc. Bayesian inference can be used for both parameter estimation and uncertainty quantification, but its successful application to realistic scenarios has been hindered by the complex stochastic nature of IBMs. Com...
Article
Full-text available
We quantify uncertainties in the location and magnitude of extreme pressure spots revealed from large scale multi-phase flow simulations of cloud cavitation collapse. We examine clouds containing 500 cavities and quantify uncertainties related to their initial spatial arrangement. The resulting 2000-dimensional space is sampled using a non-intrusiv...
Article
We consider the very challenging problem of efficient uncertainty quantification for acoustic wave propagation in a highly heterogeneous, possibly layered, random medium, characterized by possibly anisotropic, piecewise log-exponentially distributed Gaussian random fields. A multi-level Monte Carlo finite volume method is proposed, along with a nov...
Chapter
We propose Monte Carlo (MC), single level Monte Carlo (SLMC) and multilevel Monte Carlo (MLMC) methods for the numerical approximation of statistical solutions to the viscous, incompressible Navier–Stokes equations (NSE) on a bounded, connected domain \(D\subset \mathbb {R}^d\), \(d=1,2\) with no-slip or periodic boundary conditions on the boundary...
Article
Two layer Savage-Hutter type shallow water PDEs model flows such as tsunamis generated by rockslides. On account of heterogeneities in the composition of the granular matter, these models contain uncertain parameters like the ratio of densities of layers, Coulomb and interlayer friction. These parameters are modeled statistically and quantifying th...
Conference Paper
The Multi-Level Monte Carlo algorithm was shown to be a robust solver for uncertainty quantification in the solutions of multi-dimensional systems of stochastic conservation laws. For random fluxes or random initial data with large variances, the time step of the explicit time stepping scheme becomes random due to the random CFL stability restricti...
Article
A mathematical formulation of conservation and of balance laws with random input data, specifically with random initial conditions, random source terms and random flux functions, is reviewed. The concept of random entropy solution is specified. For scalar conservation laws in multi-dimensions, recent results on the existence and on the uniqueness o...
Article
We consider stochastic linear hyperbolic systems of conservation laws in several space dimensions. We prove existence and uniqueness of a random weak solution and provide estimates for the space-time as well as statistical regularity of the solution in terms of the corresponding estimates for the random input data. Multi-Level Monte Carlo Finite Di...
Article
We extend the multi-level Monte Carlo (MLMC) in order to quantify uncertainty in the solutions of multi-dimensional hyperbolic systems of conservation laws with uncertain initial data. The algorithm is presented and several issues arising in the massively parallel numerical implementation are addressed. In particular, we present a novel load balanc...
Article
The initial data and bottom topography, used as inputs in shallow water models, are prone to uncertainty due to measurement errors. We model this uncertainty statistically in terms of random shallow water equations. We extend the multilevel Monte Carlo (MLMC) algorithm to numerically approximate the random shallow water equations efficiently. The M...
Conference Paper
The Multi-Level Monte Carlo finite volumes (MLMC-FVM) algorithm was shown to be a robust and fast solver for uncertainty quantification in the solutions of multi-dimensional systems of stochastic conservation laws. A novel load balancing procedure is used to ensure scalability of the MLMC algorithm on massively parallel hardware. We describe this p...
Article
The Multi-Level Monte Carlo finite volumes (MLMC-FVM) algorithm was shown to be a robust and fast solver for uncertainty quan-tification in the solutions of multi-dimensional systems of stochastic con-servation laws. A novel load balancing procedure is used to ensure scal-ability of the MLMC algorithm on massively parallel hardware. We de-scribe th...

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