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AuthorAlouini, Mohamed-Slim (110)Ooi, Boon S. (47)Richtarik, Peter (36)Ng, Tien Khee (34)Shamim, Atif (31)View MoreDepartment

Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division (738)

Electrical Engineering Program (350)Computer Science Program (169)Applied Mathematics and Computational Science Program (109)Physical Sciences and Engineering (PSE) Division (87)View MoreJournalIEEE Access (22)IEEE Transactions on Wireless Communications (14)2018 IEEE Global Communications Conference (GLOBECOM) (11)IEEE Transactions on Communications (9)2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS) (8)View MoreKAUST Acknowledged Support UnitOffice of Sponsored Research (OSR) (29)Supercomputing Laboratory (14)OSR (13)Extreme Computing Research Center (8)KAUST Office of Sponsored Research (OSR) (8)View MoreKAUST Grant NumberBAS/1/1614-01-01 (20)GEN/1/6607-01-01 (16)KCR/1/2081-01-01 (15)BAS/1/1664-01-01 (12)URF/1/3437-01-01 (11)View MorePublisherInstitute of Electrical and Electronics Engineers (IEEE) (211)Elsevier BV (57)arXiv (56)IEEE (55)Springer Nature (46)View MoreSubjectdeep learning (7)energy harvesting (7)Localization (7)secrecy outage probability (6)Visible light communication (6)View MoreTypeArticle (475)Conference Paper (168)Preprint (63)Book Chapter (23)Presentation (4)View MoreYear (Issue Date)
2019 (738)

Item AvailabilityOpen Access (466)Metadata Only (161)Embargoed (111)

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Numerical methods for density driven groundwater ow with uncertain data

Litvinenko, Alexander; Logashenko, Dmitry; Keyes, David E.; Wittum, Gabriel; Tempone, Raul (2019-02-20) [Presentation]

Accurate modeling of contamination in subsurface flow and water aquifers is crucial for agriculture and environmental protection. We consider the density-driven subsurface flow and estimate how uncertainty from permeability and porosity propagates to the solution - mass fraction. We take an Elder-like problem as a numerical benchmark and we use random fields to model the limited knowledge on the porosity and permeability. We construct a low-cost generalized polynomial chaos expansion (gPCE) surrogate model, where the gPCE coefficients are computed by projection on sparse and full tensor grids. We parallelize both the numerical solver for the deterministic problem based on the multigrid method, and the quadrature over the parametric space.

Efficient Simulations for Contamination of Groundwater Aquifers under Uncertainties

Litvinenko, Alexander; Logashenko, Dmitry; Tempone, Raul; Keyes, David E.; Wittum, Gabriel (2019-02-25) [Presentation]

Accidental contamination of groundwater can be extremely hazardous and thus, accurately predicting the fate of pollutants in groundwater is essential. Certain pollutants are soluble in water and can leak into groundwater systems, such as seawater into coastal aquifers or wastewater leaks. Indeed, some pollutants can change the density of a fluid and induce density-driven flows within the aquifer. This causes faster propagation of the contamination due to convection. Thus, simulation and analysis of this density-driven flow plays an important role in predicting how pollution can migrate through an aquifer.
We propose the new parallel algorithm to compute a functional approximation of the QoI. Namely, we approximate the QoI with the polynomial chaos expansion (PCE), where all PCE coefficients are computed in parallel. We demonstrate 2D and 3D examples.

Computation of Electromagnetic Fields Scattered From Objects With Uncertain Shapes Using Multilevel Monte Carlo Method

Litvinenko, Alexander; Yucel, Abdulkadir; Bagci, Hakan; Oppelstrup, Jesper; Tempone, Raul; Michielssen, Eric (2019-02-14) [Poster]

Computational tools for characterizing electromagnetic scattering from objects with uncertain shapes are needed in various applications ranging from remote sensing at microwave frequencies to Raman spectroscopy at optical frequencies. Often, such computational tools use the Monte Carlo (MC) method to sample a parametric space describing geometric uncertainties. For each sample, which corresponds to a realization of the geometry, a deterministic electromagnetic solver computes the scattered fields. However, for an accurate statistical characterization the number of MC samples has to be large. In this work, to address this challenge, the continuation multilevel Monte Carlo (CMLMC) method is used together with a surface integral equation solver. The CMLMC method optimally balances statistical errors due to sampling of the parametric space, and numerical errors due to the discretization of the geometry using a hierarchy of discretizations, from coarse to fine. The number of realizations of finer discretizations can be kept low, with most samples computed on coarser discretizations to minimize computational cost. Consequently, the total execution time is significantly reduced, in comparison to the standard MC scheme.

Solution of Density Driven Groundwater Flow with Uncertain Porosity and Permeability Coefficients

Litvinenko, Alexander; Tempone, Raul; Logashenko, Dmitry; Wittum, Gabriel; Keyes, David E. (2019-03) [Presentation]

In many countries, groundwater is the strategic reserve, which is used as drinking water and as an irrigation resource. Therefore, accurate modeling of the pollution of the soil and groundwater aquifer is highly important. As a model, we consider a density-driven groundwater flow problem with uncertain porosity and permeability. This problem may arise in geothermal reservoir simulation, natural saline-disposal basins, modeling of contaminant plumes and subsurface flow. This strongly non-linear problem describes how salt or polluted water streams down building 'fingers". The solving process requires a very fine unstructured mesh and, therefore, high computational resources. Consequently, we run the parallel multigrid solver UG4 (https://github.com/UG4/ughub.wiki.git) on Shaheen II supercomputer. The parallelization is done in both - the physical space and the stochastic space. The novelty of this work is the estimation of risks that the pollution will achieve a specific critical concentration. Additionally, we demonstrate how the multigrid UG4 solver can be run in a black-box fashion for testing different scenarios in the density-driven flow. We solve Elder's problem in 2D and 3D domains, where unknown porosity and permeability are modeled by random fields. For approximations in the stochastic space, we use the generalized polynomial chaos expansion.

Numerical recipies for landslide spatial prediction by using R-INLA: A step-by-step tutorial

Lombardo, Luigi; Opitz, Thomas; Huser, Raphaël (Elsevier, 2019) [Book Chapter]

The geomorphological community typically assesses the landslide susceptibility at the catch- ment or larger scales through spatial predictive models. However, the spatial information is conveyed only through the geographical distribution of the covariates. Spatial dependence, which allows capturing similarities at neighboring sites that are not directly explained by covariate information, is typically not accounted for in the landslides literature, whilst such spatial models have become commonplace in the geostatistical literature. Here we explain step by step how to rigorously model and predict activations of debris flow based on an adequate statistical model by using the R-INLA library from the statistical software R in the context of a real multiple landslide event. This chapter follows the analysis of Lombardo et al. (2018a) with a few modifications; it is written in a tutorial style to provide the geomor- phological community with a hands-on guide to replicate similar analyses in R. While our focus here is on implementation and computing, more details about the underlying statistical theory, modeling and estimation can be found in Lombardo et al. (2018a). Our modeling approach deviates fundamentally from the commonly-used regression models fitted to binary presence/absence data. Specifically, we use a Bayesian hierarchical Cox point process model to describe landslide counts at high resolution (i.e., at the pixel level), while capturing spatial dependence through a latent spatial effect defined at lower resolution over slope units. Our point process modeling approach allows us to derive the distribution of aggregated landslide counts for any areas of interest. Crucially, the latent spatial effect represents the unexplained but spatially structured component in the data when the linear or nonlinear effects of covariates are removed. Thus, in the case of sparse raingauge or seismic networks, we suggest using the latent spatial effect to uncover the trigger distribution over space. In particular, for landslides triggered by extreme precipitation, the meteorological stress can play a dominant role with respect to the covariates that are typically introduced in predictive models; therefore, accounting for the trigger in modeling may dramatically improve the performance of landslide prediction.

Spatial extremes

Davison, Anthony C.; Huser, Raphaël; Thibaud, Emeric (CRC Press, 2019) [Book Chapter]

Scalability of a parallel monolithic multilevel solver for poroelasticity

Nägel, Arne; Wittum, Gabriel (Springer, 2019) [Book Chapter]

This study investigates a solver for the quasi-static Biot model for soil con- solidation. The scheme consists of an extrapolation scheme in time, com- plemented by a scalable monolithic multigrid method for solving the linear systems resulting after spatial discretisation. The key ingredient for the later is a fixed-stress inexact Uzawa smoother that has been suggested and anal- ysed using local Fourier analysis before [8]. The work at hand investigates the parallel properties of the resulting multigrid solver. For a 3D benchmark problem with roughly 400 million degrees of freedom, scalability is demon- strated in a preliminary study on HazelHen. The presented solver framework should be seen as a prototype, and can be extended and generalized, e.g., to non-linear problems easily.

The Impact of Surface Roughness on Avalanche Frequency

Al Attar, Talal (Institute of Electrical and Electronics Engineers (IEEE), 2019-05-13) [Conference Paper]

IMPATT diodes were designed and fabricated in standard CMOS technology to study the impact of the RMS value of surface roughness on the avalanche frequency. By comparing the on-chip measurements of an IMPATT diode integrated in a CPW to an integrated one with a microstrip patch antenna at the same biasing conditions, the results demonstrated a reduction in the avalanche frequency in the antenna integrated structure by 34% compared to the CPW one. Such variation is strongly associated with the increase in the conduction losses by 40%~80% based on the biasing conditions and hence the avalanche frequency.

Mapping seismic data cubes to vertical velocity profiles by deep learning: New full-waveform inversion paradigm?

Kazei, Vladimir; Ovcharenko, Oleg; Plotnitskii, Pavel; Peter, Daniel; Zhang, Xiangliang; Alkhalifah, Tariq Ali (Society of Exploration Geophysicists, submitted to Geophysics, 2019) [Preprint]

Building realistic and reliable models of the subsurface is the primary goal of seismic imaging. Full-waveform inversion (FWI) allows us to incorporate realistic physical descriptions of the Earth through modeling to deliver the highest resolution possible. FWI is a local optimization technique and requires a good initial model to converge to the globally optimal model of the subsurface. In order to estimate the uncertainty in FWI, several initial models are necessary. We construct an ensemble of convolutional neural networks (CNN) to build velocity models directly from the data. CNNs are trained to map the seismic data directly into velocity logs. This allows to integrate the well data and to simplify the mapping by using the regularity of active seismic acquisition. The main feature of our approach is the usage of neighboring common midpoint (CMP) gathers in the prediction of a log at the central CMP location. This allows us to accommodate larger dips compared to using single CMP gathers. At the same time, we still benefit from the regularity of sampling in seismic exploration.

Matlab Tool for Residual Water Suppression and Denoising of MRS Signal using the Schrodinger operator

Bhaduri, Sourav; Achten, Eric; Serrai, Hacene; Chahid, Abderrazak; Laleg-Kirati, Taous-Meriem (2019) [Poster]

A new user interactive platform for MRS data processing is proposed. This toolbox is based on the SemiClassical Signal Analysis (SCSA) for Residual Water Suppression and MRS signal denoising. It allows MRS users to achieve water suppression and data denoising, with data fitting as an additional feature. The toolbox is easy to install and to use: 1) visualization of spectroscopy data, 2) water suppression and denoising, 3) iterative data fitting using nonlinear least squares. This abstract demonstrates how each of these features has been incorporated and provides technical details about the implementation as a graphical user interface in MATLAB.

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