Stochastic Numerics PI Professor Raul Tempone (Chair) and Computational Probability PI Professor Ajay Jasra (Co-Chair)
Sunday, May 19, 2024, 08:00
- 17:00
KAUST, Auditorium 0215
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We are excited to announce the upcoming Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2024, taking place at KAUST, Auditorium 0215 b/w B4&5, from May 19-30, 2024. Following the highly successful last two years edition, this year's workshop promises to be another engaging and insightful event for researchers, faculty members, and students interested in stochastic algorithms, statistical learning, optimization, and approximation. The 2024 workshop aims to build on the achievements of last year's event, which featured 30 talks, two mini-courses, and two poster sessions, attracting over 150 participants from various universities and research institutes. In 2022 and 2023, attendees had the opportunity to learn from through insightful talks, interactive mini-courses, and vibrant poster sessions. This year, the workshop will once again showcase contributions that offer mathematical foundations for algorithmic analysis or highlight relevant applications. Confirmed speakers include renowned experts from institutions such as Ecole Polytechnique, EPFL, Université Pierre et Marie Curie - Paris VI, CUHK Shenzhen, and Imperial College London, among others.
Wednesday, May 15, 2024, 10:00
- 12:00
Building 4, Level 5, Room 5220
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This thesis consists of three papers considering, in general, two challenges: the minimization of computational cost in the ensemble Kalman filtering (EnKF) method and the problem of tracking a rare event within the framework of the EnKF.
Prof. Francesca Gardini, Università di Pavia
Tuesday, April 30, 2024, 16:00
- 17:00
Building 1, Level 3, Room 3119
We will discuss the solution of eigenvalue problems associated with partial differential equations (PDE)s that can be written in the generalised form Ax = λMx, where the matrices A and/or M may depend on a scalar parameter. Parameter dependent matrices occur frequently when stabilised formulations are used for the numerical approximation of PDEs. With the help of classical numerical examples we will show that the presence of one (or both) parameters can produce unexpected results.
Prof. Edgard Pimentel, Department of Mathematics of the University of Coimbra
Tuesday, March 26, 2024, 16:00
- 17:00
Building 2, Level 5, Room 5220
Hessian-dependent functionals play a pivotal role in a wide latitude of problems in mathematics. Arising in the context of differential geometry and probability theory, this class of problems find applications in the mechanics of deformable media (mostly in elasticity theory) and the modelling of slow viscous fluids. We study such functionals from three distinct perspectives.
Thursday, March 21, 2024, 12:00
- 13:00
Building 9, Level 2, Room 2325, Hall 2
In this work, we employ importance sampling (IS) techniques to track a small over-threshold probability of a running maximum associated with the solution of a stochastic differential equation (SDE) within the framework of ensemble Kalman filtering (EnKF).
Prof. Silvia Bertoluzza
Tuesday, March 05, 2024, 16:00
- 17:00
Building 2, Level 5, Room 5209
We present a theoretical analysis of the Weak Adversarial Networks (WAN) method, recently proposed in [1, 2], as a method for approximating the solution of partial differential equations in high dimensions and tested in the framework of inverse problems. In a very general abstract framework.
Prof. Christof Schmidhuber, ZHAW School of Engineering
Tuesday, February 27, 2024, 16:00
- 17:00
Building 9, Level 2, Room 2322
Analogies between financial markets and critical phenomena have long been observed empirically. So far, no convincing theory has emerged that can explain these empirical observations. Here, we take a step towards such a theory by modeling financial markets as a lattice gas.
Prof. Dr. Victorita Dolean, Mathematics and Computer Science, Scientific Computing, TU Eindhoven
Tuesday, February 06, 2024, 16:00
- 17:00
Building 2, Level 5, Room 5220
Wave propagation and scattering problems are of huge importance in many applications in science and engineering - e.g., in seismic and medical imaging and more generally in acoustics and electromagnetics.
Prof. Zhiming Chen, Academy of mathematics and Systems Science, Chinese Academy of Sciences
Wednesday, January 24, 2024, 14:30
- 16:00
Building 4, Level 5, Room 5220
In this short course, we will introduce some elements in deriving the hp a posteriori error estimate for a high-order unfitted finite element method for elliptic interface problems. The key ingredient is an hp domain inverse estimate, which allows us to prove a sharp lower bound of the hp a posteriori error estimator.
Stochastic Numerics PI Professor Raul Tempone (Chair) and Computational Probability PI Professor Ajay Jasra (Co-Chair)
Sunday, May 21, 2023, 08:00
- 17:00
KAUST, Building 9
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Dear Kaustians, We are excited to announce the upcoming Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2023, taking place at KAUST, Building 9, from May 21 to June 1, 2023. Following the highly successful 2022 edition, this year's workshop promises to be another engaging and insightful event for researchers, faculty members, and students interested in stochastic algorithms, statistical learning, optimization, and approximation. The 2023 workshop aims to build on the achievements of last year's event, which featured 28 talks, two mini-courses, and two poster sessions, attracting over 150 participants from various universities and research institutes. In 2022, attendees had the opportunity to learn from through insightful talks, interactive mini-courses, and vibrant poster sessions. This year, the workshop will once again showcase contributions that offer mathematical foundations for algorithmic analysis or highlight relevant applications. Confirmed speakers include renowned experts from institutions such as Ecole Polytechnique, EPFL, Université Pierre et Marie Curie - Paris VI, and Imperial College London, among others.
Stochastic Numerics PI Professor Raul Tempone (Chair) and Computational Probability PI Professor Ajay Jasra (Co-Chair)
Sunday, May 15, 2022, 08:00
- 17:00
KAUST Campus
Contact Person
This scientific meeting will concentrate on stochastic algorithms and their rigorous numerical analysis for various problems, including statistical learning, optimization, and approximation. Stochastic algorithms are valuable tools when addressing challenging computational problems.
Wednesday, January 12, 2022, 11:00
- 12:00
KAUST
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This thesis studies novel and efficient computational sampling methods for applications in three types of stochastic inversion problems: seismic waveform inversion, filtering problems, and static parameter estimation.
Thursday, July 02, 2020, 14:00
- 16:00
KAUST
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In biochemically reactive systems with small copy numbers of one or more reactant molecules, stochastic effects dominate the dynamics. In the first part of this thesis, we design novel efficient simulation techniques, based on multilevel Monte Carlo methods and importance sampling, for a reliable and fast estimation of various statistical quantities for stochastic biological and chemical systems under the framework of Stochastic Reaction Networks (SRNs). In the second part of this thesis, we design novel numerical methods for pricing financial derivatives. Option pricing is usually challenging due to a combination of two complications: 1) The high dimensionality of the input space, and 2) The low regularity of the integrand on the input parameters. We address these challenges by using different techniques for smoothing the integrand to uncover the available regularity and improve quadrature methods' convergence behavior. We develop different ways of smoothing that depend on the characteristics of the problem at hand. Then, we approximate the resulting integrals using hierarchical quadrature methods combined with Brownian bridge construction and Richardson extrapolation.
Thursday, May 02, 2019, 12:00
- 13:00
B9 L2 Hall 1
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Optimal experimental design for parameter estimation is a fast-growing area of research. Let us consider the experimental goal to be the inference of some attributes of a complex system using measurement data of some chosen system responses, and the optimal designs are those that maximize the value of measurement data. The value of data is quantified by the expected information gain utility, which measures the informativeness of an experiment. Often, a mathematical model is used that approximates the relationship between the system responses and the model parameters acting as proxies for the attributes of interest.
Dr. Eric Hall
Wednesday, February 06, 2019, 14:00
- 15:00
Building 1, Level 4, Room 4214
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Hierarchical porous media that feature properties and processes at multiple scales arise in many engineering applications including the design of novel materials for energy storage devices. Microscopic (pore-scale) properties of the media impact their macroscopic (continuum- or Darcy-scale) counterparts and understanding the relationships between processes on these two scales is essential for informing engineering decision tasks.
Wednesday, February 06, 2019, 10:00
- 11:30
Building 4, Level 5, Room 5220
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I​n the field of uncertainty quantification, the effects of parameter uncertainties on scientific simulations may be studied by integrating or approximating a quantity of interest as a function over the parameter space. If this is done numerically, using regular grids with a fixed resolution, the required computational work increases exponentially with respect to the number of uncertain parameters -- a phenomenon known as the curse of dimensionality.
Monday, October 08, 2018, 09:00
- 10:30
Building 1, Room 3119
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The probability that a sum of random variables (RVs) exceeds (respectively falls below) a given threshold, is often encountered in the performance analysis of wireless communication systems. Generally, a closed-form expression of the sum distribution does not exist and a naive Monte Carlo (MC) simulation is computationally expensive when dealing with rare events. An alternative approach is represented by the use of variance reduction techniques, known for their efficiency in requiring less computations for achieving the same accuracy requirement.
Wednesday, September 26, 2018, 16:00
- 17:30
Building 3, Room 5209
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This work employs statistical and Bayesian techniques to analyze mathematical forward models with several sources of uncertainty. The forward models usually arise from phenomenological and physical phenomena and are expressed through regression-based models or partial differential equations (PDEs) associated with uncertain parameters and input data. One of the critical challenges in real-world applications is, for any proposed model, to estimate and quantify uncertainties of its unknown parameters using the available observations.
Professor Charbel Farhat
Sunday, July 15, 2018, 15:00
- 16:00
Building 3, Level 5, Room 5220
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A feasible, nonparametric, probabilistic approach for modeling and quantifying model-form uncertainties associated with a High-Dimensional Computational Model (HDM) and/or a corresponding Hyperreduced Projection-based Reduced-Order Model (HPROM) [1,2] designed for the solution of computational mechanics problems is presented.
Thursday, August 24, 2017, 15:00
- 16:30
Building 3, Room 5209
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Stochastic Differential Equations (SDE) offer a rich framework to model the probabilistic evolution of the state of a system. Numerical approximation methods are typically needed in evaluating relevant Quantities of Interest arising from such models. In this dissertation, we present novel effective methods for evaluating Quantities of Interest relevant to computational finance when the state of the system is described by an SDE.
Wednesday, April 12, 2017, 14:30
- 16:00
Building 3, Level 5, Room 5209
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Ms. Soumaya ElKantassi is a master student in Applied Mathematics and Computational Science and a member of the Stochastic Numerics Group at KAUST. She obtained her Bachelor's degree in Engineering from Tunisia Polytechnic School majoring in Economic and Scientific Management.
Thursday, May 19, 2016, 14:00
- 15:00
Building 2, Room 5220
Most problems in engineering and natural sciences involve parametric equations in which the parameters are not known exactly due to measurement errors, lack of measurement data, or even intrinsic variability. In such problems, one objective is to compute point or aggregate values, called "quantities of interest".