uncertainty quantification

Uncertainty Quantification in Molecular Dynamics By Dr. Francesco Rizzi (John Hopkins University, USA)

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B1 R3119

Molecular dynamics (MD) is widely employed in both industrial and academic environments, playing a key role in the study of a large variety of systems, ranging fromliquids to solids, as well as biomolecules, such as proteins and nucleic acids (DNA, RNA). MD simulations provide a suitable and convenient method to explore dynamical properties of a system at the atomic level which, in general, aresignificantly difficult and expensive to investigate in experimental settings.

Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2014)

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B4 B5 A0215

​SRI UQ Annual Meeting 6-10 January 2014 See please the program here: schedule Technical presentations slide: here Full Book of Abstracts: here Full Book of Abstracts with Posters: here​ ​(40 MB)

SRI UQ Annual Workshop 2013

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Auditorium L0 Between B4 and B5

Apr 26 2013 9:00 AM - Apr 30 2013 5:00 PM SRI UQ Annual Meeting 26-30 April 2013 Schedule_2013 To view the WORKSHOP report please click on this link: Report

Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2015)

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B4 B5 A0215

SRI UQ Annual Meeting 6-9 January 2015. The deadline for poster submission is December 1st, 2014. See the program here Book of Abstracts Book of Abstracts With Posters (63 MB) Technical presentations slides and videos​

SIAM Review - A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data

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This work proposes and analyzes a stochastic collocation method for solving elliptic partial differential equations with random coefficients and forcing terms. These input data are assumed to depend on a finite number of random variables.

SIAM J. Numer. Anal. - A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data

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In this paper, we propose and analyze a stochastic collocation method to solve elliptic partial differential equations with random coefficients and forcing terms (input data of the model). The input data are assumed to depend on a finite number of random variables.

Dr. Tembine Hamidou was selected to receive the IEEE ComSoc EMEA Outstanding Young Researcher Award

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Dr. Tembine Hamidou (KAUST CEMSE Senior Research Scientist, SRI Center for Uncertainty Quantification) was selected to receive the IEEE ComSoc EMEA Outstanding Young Researcher Award for his promising research activities for the benefit of society.

Alexander Litvinenko is giving talk "Sampling and low-rank tensor approximation of the response surface" on workshop "Uncertainty Management for Robust Industrial Design in Aeronautics - UMRIDA", April 15-16, TU Delft, Netherlands

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UMRIDA is a Level 1 collaborative project within the European Commission's Seventh Framework Programme. It involves a consortium of 21 partners from the industrial aeronautics sector, leading research institutes and universities as well as SME's.

Prof. Tempone is an invited speaker at the Uncertainty Quantification Workshop Institut Mittag-Leffler, Sweden

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Prof. Tempone is an invited speaker at the Uncertainty Quantification Workshop, from May 9 to 13, 2016. Institut Mittag-Leffler, Sweden.

Alexander Litvinenko, together with colleagues from RWTH Aachen, TU Braunschweig and SkolTech is organizing a minisymposium

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Alexander Litvinenko, together with colleagues from RWTH Aachen, TU Braunschweig (Germany), and SkolTech (Russia) is organizing a minisymposium "Tensor completion techniques in Data Assimilation, Bayesian Updata and UQ problems" at SIAM CSE Conference being held February 27 through March 3, 2017, at the Hilton Atlanta in Atlanta, Georgia, USA.

Prof. Raul Tempone gave a talk entitled "Multilevel and multi-index sampling methods with applications" in the summer school CEMRACS 2017

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Prof. Raul Tempone gave a talk entitled "Multilevel and multi-index sampling methods with applications" in the summer school CEMRACS 2017, Numerical methods for stochastic models: control, uncertainty quantification, mean-field, July 21, 2017, CIRM, Marseille, France.

Professor Raul Tempone awarded Germany’s most endowed research award

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On May 8, Italian-Uruguayan KAUST Professor Raul Fidel Tempone received the Alexander von Humboldt professorship at RWTH Aachen at a ceremony in Berlin, Germany. The award recognizes Tempone as an international leader in mathematics for uncertainty quantification, a fairly new mathematical specialization that is currently making a breakthrough. Tempone teaches and performs research in the University's Applied Mathematics and Computational Science program.

A new Ms/Ph.D. student joins the team

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In January 2023, student Ons Chaabene joined KAUST through the Stochastic Numerics Research Group, where she will pursue her Ms/Ph.D. studies under Prof. Raul Tempone. StochNum Team wishes an excellent experience for Ons!

Sören Wolfers

Sören Wolfers obtained a Ph.D. degree in Applied Mathematics and Computational Sciences under the supervision of Professor Raul F. Tempone's at Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Sören's research interests included Numerical Analysis, Uncertainty Quantification, Sparse Approximation, Multilevel methods and Meshfree methods. Selected Publications Beck, J., Roberts, G., and Wolfers, S. (2018). Bayesian earthquake dating and seismic hazard assessment using chlorine-36 measurements (BED v1). Submitted to

Raul Tempone

Professor Raul Tempone is an worlwide recognized researcher in numerical analysis and uncertainty quantification. Professor Raul Tempone at KAUST leads the stochastic numerics research group, significantly contributing to Saudi Arabia's Vision 2030 goals through advancements in computational science. His work in adaptive algorithms, Bayesian inverse problems, and scientific machine learning drives forward critical applications in technology and sustainability, embodying KAUST's commitment to global scientific leadership and economic diversification.

Quan Long

Quan Long worked as a Research Scientist at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Quan obtained his Ph.D. degree in computational structural mechanics from CSMLab at Cambridge University, where his research focused on isogeometric analysis using subdivision techniques and its applications in large structural deformations, nematic elastomers, and dynamic fractures. During his postdoctoral research, he extended his interests from deterministic problems to stochastic problems, for example, Bayesian

Nathan Quadrio

Nathan Quadrio is a Former visiting student from the Polytechnic University of Turin at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Nathan's research interests include ​Uncertainty Quantification, Multilevel Monte Carlo, Stochastic Partial Differential Equations, Reservoir Modeling. Selected Publications ​Nathan Quadrio, "Multilevel Monte Carlo method for PDEs with fluid dynamic applications​", Master Thesis, KAUST, October 2014. Education Profile ​Master's Degree at the Polytechnic University

Marco Ballesio

Marco Ballesio is a Ph.D. candidate at Stochastic Numerics Research Group (STOCHNUM) under the supervision of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST). Research Interests Marco's research interests include Probability Theory and Stochastic Processes, Stochastic Modeling, Uncertainty Quantification. Education Profile Laurea Magistrale (Master’s Degree) in Mathematics for Engineering, Politecnico di Torino (Grade: 110/110 cum laude), (2013-3/2016). Allievi Honors Program, Allievo Senior, Collegio Carlo Alberto (2013-3/2016). Erasmus Student at Aalto

Longting Mo

Longting Mo is a Former visiting student from Nanjing University at Professor Raul F. Tempone's Stochastic Numerics Research Group (STOCHNUM) at King Abdullah University of Science and Technology (KAUST). Research Interests Longting research interest includes Uncertainty quantification in groundwater science. Education Profile Nanjing University. Professional Memberships American Geophysical Union.

Lauri Mustonen

Lauri Mustonen is a Former visiting student from Aalto University at Professor Raul F. Tempone's Stochastic Numerics Research Group (STOCHNUM) at King Abdullah University of Science and Technology (KAUST). Research Interests Lauri's research interests include uncertainty Quantification, Inverse Problems, Numerical Methods. Education Profile M.Sc. (Tech.), Engineering Physics, and Mathematics, Aalto University, 2014. Professional Memberships http://math.aalto.fi/en/research/applied/inverseproblems/

Kody J. H. Law

Senior Research Scientist, Stochastic Numerics Research Group
Kody J. H. Law worked as a Senior Research Scientist at Professor Raul F. Tempone's Stochastic Numerics Research Group (STOCHNUM) at King Abdullah University of Science and Technology (KAUST). Research Interests Kody specializes in computational approaches to inverse problems, uncertainty quantification, and sequential data assimilation. His interest spans methodology, such as function-space sampling and filtering algorithms, and also applications, such as numerical weather prediction, ocean prediction, climate prediction, and subsurface reconstruction. His interest in data assimilation

Evangelia Kalligiannaki

Evangelia Kalligiannaki worked as a Research Scientist at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Evangelia's research interests concerned the development, analysis, and implementation of model reduction (coarse-graining) techniques for high dimensional particle systems at multiple spaces and time scales, and included studying macro-molecular and stochastic lattice systems. She was particularly interested in the development of effective equilibrium and non-equilibrium coarse-grained models

Erik von Schwerin

Given a mathematical model of a system with uncertainties, a computational method of approximation of that model, and a specific Quantity of Interest (QoI) of the system, how can we, with as small computational effort as possible, approximate the QoI within a user-specified accuracy? Most of Erik von Schwerin's research relates in some way or another to this question.

Carina Suciu

Postdoctoral Research Fellow, Stochastic Numerics Research Group
Carina Suciu worked as a Postdoctoral Fellow at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Carina's research interests included Deterministic and Stochastic Numerical Methods for Partial Differential Equations and Techniques for Predictive Simulations and Uncertainty Quantification. Selected Publications Direct Discretization of Bi-Variate Population Balance Systems with Finite Difference Schemes of Different Order, Chemical Engineering Science 106, 39-52, 2014, joint work with V. John A

Ben Mansour Dia

Postdoctoral Research Fellow, Stochastic Numerics Research Group
Ben Mansour Dia worked as a Postdoctoral Fellow at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Modeling and Numerical Simulations of Incompressible Flows: 2-D shallow water equations, 1-D bilayer shallow water equations. Control Theory: Boundary stabilization and exact controllability of hyperbolic partial differential equations, optimal control of ordinary differential equations and stochastic differential equations Numerical Analysis: Finite Elements Method, Variational Multi-Scale

André Gustavo Carlon

Postdoctoral Research Fellow, Stochastic Numerics Research Group
André Gustavo Carlon is a Postdoctoral Fellow at Stochastic Numerics Research Group under the supervision of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST). Research Interests André's research interests include the analysis of stochastic optimization methods, ​the application of stochastic gradient methods to engineering problems, and Bayesian optimal experimental design with non-linear models. Selected Publications​ A. G. Carlon, L. Espath, R. Tempone. Approximating Hessian matrices using Bayesian inference: a new approach for quasi-Newton methods in

Alvaro Moraes

Postdoctoral Research Fellow, Stochastic Numerics Research Group
Alvaro Moraes worked as a Postdoctoral Fellow at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Prior to that, Alvaro obtained a Ph.D. degree in Applied Mathematics from KAUST. Research Interests Alvaro's research interests included ​Uncertainty Quantification, Multiscale Modeling of Epidemic Diseases, and their Statistical Analysis and Simulation and Inference for Pure Jump Processes. Selected Publications A. Moraes, F. Ruggeri, R. Tempone and P. Vilanova, " Multiscale Modeling of Wear Degradation in Cylinder

Alexander Litvinenko

Senior Research Scientist, Stochastic Numerics Research Group
Alexander Litvinenko worked as a Senior Research Scientist at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). After 5 years as a research scientist at KAUST (in various groups: the Stochastic Numerics Group and Strategic Initiative in Uncertainty Quantification, Extreme Computing Research Center, and Bayesian Computing) Alexander moved to RWTH Aachen to the position Group leader in Uncertainty Quantification. He earned B.S. and M.S. degrees working on data analysis at Sobolev Institute of Mathematics at the

Aku Jaakko Alexis Kammonen

Postdoctoral Research Fellow, Stochastic Numerics Research Group
Aku Jaakko Alexis Kammonen is a Postdoctoral Fellow at Stochastic Numerics Research Group under the supervision of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST). Research Interests Aku's research interests include Machine Learning, Neural Networks, Random Features, Spectral Bias, Adversarial Attacks, and Numerical Analysis. Selected Publications A. Kammonen, L. Liang, A. Pandey, and R. Tempone (2024, March 19–23). Comparing Spectral Bias and Robustness For Two-Layer Neural Networks: SGD vs Adaptive Random Fourier Features [Poster presentation]