Strategic Research Initiative-Uncertainty Quantification Center, KAUST

Introductory Brochure Uncertainty Quantification Center

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TABLE OF CONTENTS Our mission, goals, focuses………………………………………….…………3  The core UQ Thrust………………………………………………………………..6 Reac ve Computa onal Fluid Dynamics Thrust….……………….…8 Large Scale Computa onal Research Thrust……..……………...…10 Green Wireless Communica ons research Thrust…………….…12 UQ in Strategic Decision Making…………………………………...…...14 Learning, Evolu on and Games………………………………..……......15 Low‐rank Approxima on…………………………………………..………..16 Reservoir Modeling under uncertain es……………………..……...17 UQ in numerical Aerodynamics…………………………..……………...18 Bayesian Inverse Problems…………………………………….……….....19 Tutorials and publica ons………………………………………….…..…..22 Partners……………………………………………………..……………….……...24 Thrust Leaders……………………………………………………….…….……..26 Advisory Board Members………………………...…..……….…….……..27 

Our primary mission is to develop state of-the-art Uncertainty Quantification, Verification & Validation Methods, Algorithms and Software.

Broad, multi-partner, multi-disciplinary research will advance the Kingdom, the Region and the World priorities such as water, food, energy, environment, health and transportation.

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A unified UQ framework to address three important research applications Totally aligned with KAUST’s primary goals: food, water, energy and environment.

The SRI UQ Center focuses on high impact applications in Green wireless communications, Complex multi-scale electromagnetic

systems, Reactive computational fluid dynamics, Public good provisioning, Transportation science, Energy markets, Crowd safety,

Epidemic prevention, Mobile advertising, Oil production enhancement.

”The KAUST SRI Center for Uncertainty Quantification in Computational Science and Engineering activities connect disciplines and bring together students and re-searchers around research focusing on UQ.”

-Advisory Board Report, April 2013

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An international research center

Computational and data-driven certification and design The novel UQ methodologies developed in the Center are relevant to systems for which testing is expensive or difficult and that operate out-side the normal range of laboratory.

Research driven education and training UQ Center has a curriculum of courses and research mentorship on U-VV, with direct impact on the KAUST CEMSE Master and PhD pro-grams and on other KAUST programs and divisions.

Key Methodology Of UQ The efforts of UQ center will be coupled with a rigorous mathematical approach to provide new tools for deci-sion-makers, designers and operators to make inter-connected societal networks more resilient in the face of unexpected disruptions, such as those caused by natural disasters, physical phenomenon or epidemic spread.

An international research center

Computational and data-driven certification and design The novel UQ methodologies developed in the Center are relevant to systems for which testing is expensive or difficult and that operate outside the normal range of laboratory.

Research driven education and training UQ Center has a curriculum of courses and research mentorship on UQ-VV, with direct impact on the KAUST CEMSE Master and PhD programs and on other KAUST programs and divisions.

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The Core UQ Thrust: Uncertainty Quantifi-cation is the science of quantitative char-acterization and reduction of uncertainties to a given Quantity of Interest.

The Core UQ Activities are concerned with the systematic quantification and reduction of uncertain-ties that originate from tolerance-based design and fab-rication, noisy experimental measurements, error-prone simulations, limited model predictability.

Observational errors and Modeling errors A mathematical models that tries to capture the ob-

servations and the measurements.

Identification of the Quantities of interest.

What are the target outputs?

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Discretization and computational errors Computational Science predicts the behavior of bio-logical, physical and social phenomena by using discre-tized (approximate) versions of a mathematical theory that can be processed by computers.

Mathematical models are often corrupted as we create the computational models that render them amenable to solution via computer, and this corruption introduces more errors.

Decision-making under uncertainty Strategic decision-making under incomplete information, bounded memory and limited computational capabilities.

Verification and Validation 1. Are we solving our equations correctly?

2. Are we solving the right equations?

Thrust Leaders: KAUST: Raul Tempone, Hamidou Tembine, Kody Law

External: Omar Knio, Serge Prudhomme, Olivier Le Maitre, Mar-co Scavino

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The Reactive Computational Fluid Dynam-ics research thrust consist to Assist the design of internal combustion engines, in-

dustrial burners, stationary power and aircraft engine turbine

Establish framework for inference and validation

Clean energy to sustain a growing popula-tion and economy Counterflow burner applied to electric field.  

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UQ is increasingly recognized as essential for design and planning of experiments. The Center is currently addressing how uncertainties

in the ion chemistry parameters affect ion concentra-tions and flame dynamics.

Thrust Leaders: Prof. Fabrizio Bisetti & Prof. Omar Knio, KAUST

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The Large Scale Computational research thrust consists of: Development of high-order accurate, robust, and

efficient simulators,

Rigorous characterization of uncertainties in the simulators’ input and output parameters.

Uncertainty quantification in large scale electromagnetics

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Stochastic characterization of voltages induced on ter-minations of cables located inside a cockpit.

Sources of Uncertainty In the analysis of EM wave interactions on a car include, for example,

Installation ambiguities in the routing of the cable har-ness,

locations of the tire

Pressure sensor,

GPS and radio antennas,

Values of the parasitic elements of the electronic components, and

The direction of an impinging plane-wave represent-ing external fields.

Thrust Leader: Prof. Hakan Bagci, KAUST

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The Green Wireless Communications re-search thrust consists of: Development and Performance Analysis of new wire-

less channel estimation,

Transceiver design optimization under uncertainty,

Technology transfer.

Sources of Uncertainty: Channel uncertainty,

Measurement noise,

Feedback noise,

Imperfect detection,

Mobility of users,

Battery uncertainty,

Queue data uncertainty.

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Lowering energy consumption of future wireless radio systems

BS sleeping strategy applied to 4G-LTE mobile network pow-ered by multiple energy providers existing in the smart elec-trical grid.

Thrust Leader: Prof. Mohamed Slim Alouini, KAUST

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Uncertainty Quantification in Strategic Decision Making consists of: Network economics under uncertainty

Distributed strategic learning

Mean field stochastic games

Random matrix games

Think strategically, act locally.

Social Network

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LEG: Learning, Evolution and Games

Coalition formation by means of distributed strategic learning The goal is to study the formation of networks and coali-tions using strategic learning process and evaluate the cost of making a coalition.

Fully distributed strategic learning for game-theoretic solutions

Random matrix games are matrix games where the entries of the payoff matrix are random variables. Risk-sensitivity, variance and higher moments provide additional information on the quantity of interest. A mul-ti-objective game theoretic approach is crucial for the analysis of the outcomes.

Thrust Leaders : Center Senior Research Scien-tist Hamidou Tembine, Prof. Raul Tempone and Prof. Mo-hamed Slim Alouini, KAUST

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Uncertainty quantification, inverse prob-lems via Bayesian update and low-rank ap-proximation

Goals: Approximate the whole computational process and the output in low-rank data formats.

Different sparse block matrices for increasing level of ap-proximation (polynomial order p=1,2,4,5). Each blue point is a large stiffness matrix.

(github.com/ezander/sglib)

Hierarchical matrix

Tucker tensor format

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Reservoir modeling under uncertainties Inverse UQ/ Data Assimilation: use available measure-ments to reduce input uncertainty. Optimal Design of Experiments: which measurements to reduce at most the uncertainty on the random input? Optimization under uncertainty: Minimize a given cost functional w.r.t. uncertainty in the input parameters. Effective approaches and solution techniques for condi-tioning, robust design and control in the subsurface: Full spectrum of tasks: Conditional simulation Experimental design Robust design Robust predictive control Risk assessment and prediction of extreme events. Percentage of a certain mineral ore in the rock, 4000 measurements, 25000^3 nodes (together with W. Nowak, Uni Stuttgart)

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UQ in numerical Aerodynamics

Goals: Identification, classification, modeling and min-imization of uncertainties in aerodynamics.

Pressure and shock are uncertain and depend on un-certain input parameters:

Benefits for industry partner: (+) better prediction accuracy,

(+) more accurate use of data, (+) better robust engineering designs, (+) better risk management, (+) more reliable decision support for sustainable management of environmental resources.

Senior Research Scientist Alexander Litvinenko, KAUST

Pressure, Airfoil RAE-2822, Ma=0.734, angle of attack AoA=2.8

Rank-5 approximation error, Airfoil RAE-2822

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Bayesian Inverse Problems Static Inverse Problem: Smoothing

MAP approximations, gradient-free stochastic optimization (enkf-based), dimension-independent, likelihood-informed MCMC.

Samplers Dimension-independent(DI), likelihood-informed(LI) MCMC samplers (blue and red) vs. standard DI pCN (pink). Posterior contours are shown in black. Application of DI MCMC sampler pCN to evaluate standard Gaussian approximations in subsurface application.

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Sequen al Inverse Problem: Filtering 

Analyze accuracy and stability of existing algorithms, from classical and Bayesian perspectives.

D e -

velopment of novel new algorithms.

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Senior Research Scientist Kody Law, KAUST

Planck filter distributions. Pullback attractor for continuous-time 3DVAR for Navier-Stokes. Top panels and bottom left illustrate by individual d.o.f.s ensem-bles of estimators converging to the truth for progressively earlier initial conditions. Bottom right is relative error of an ensemble.

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The Center offers several tutorial courses in top universities and research institutes worldwide. A top worldwide research community

Tutorial courses are given at KAUST, University of Cali-fornia at Berkley, University of California at Los Ange-les, University of Illinois at Urbana Champaign, CNRS Toulouse, Winter School ENSIAS Morocco, Summer School on Cognitive Radio.

The center organizes every year Distributed Strategic Learning Course +83 participants 9 tutorial lectures

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Key Offerings per year 9 tutorials in international conferences

Over 43 invited lectures

6 graduate courses

Over 50 scientific publications

Key Clients Faculty members,

Young researchers,

Engineers and Practitioners,

Postdoctoral fellows,

Ph.D. and MSc students

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Partners:

The SRI UQ Center launched a large series of international and local collaborations and carried out field studies and projects

30 Universities,

6 Industrial Partners,

3 Research Centers,

20 business visitors per year

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Over 30 researchers at KAUST and a wide network of renowned collaborators are actively involved in the UQ Center research The UQ Center has:

· 5 KAUST faculty members, · 4 external participants, · 5 research scientists, · 7 postdoctoral fellows, · 9 PhD students, · 1 Business administrator · 1 administrative assistant

Principal Investigators Raul Tempone Center Director

Raul.tempone@kaust.edu.sa

Omar Knio Center Deputy Director

Omar.knio@kaust.edu.sa

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Thrust Leaders

Mohamed Slim Alouini Professor, Electrical Engineering, KAUST

Hagan Bagci Professor, Electrical Engineering, KAUST

Fabrizio Bisetti Professor, Mechanical Engineering, KAUST

Serge Prudhomme Professor, Ecole Polytechnique, Montreal, Canada

Marco Scavino Professor, Universidad de la República, Montevideo, Uruguay

Olivier Le Maitre Research Director, CNRS, France

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The advisory board, which is formed from external leading experts from Academia and Industry, regularly evaluate the UQ Center. Advisory Board Members

    

Amr El-Bakry (EM)

Andrew Majda (NYU)

Hector Klie (CP)

Hermann Matthies (TU Braunschweig)

Fabio Nobile (EPFL)

Eric Michielssen (UMich)

Mostafa Kaveh (UMN)

Jan Hesthaven (Brown)

Habib Najm (Sandia Lab)

Contact Us CEMSE Division, UN 1500 Building 1, Al-Khawarizmi, 4th Floor, Office 4209

4700 King Abdullah University of Science and Technology,

Thuwal 23955-6900, Kingdom of Saudi Arabia

Office: +966 (12) 808 0374

FAX: +966 (12) 802 1296

Email: sri.uq@kaust.edu.sa

Website: http://sri-uq.kaust.edu.sa/