data assimilation

Species count mRNA, proteins, dimers - jump processes

AMCS 308 Stochastic Numerics with Application in Simulation and Data Science

stochastic algorithms Stochastic Methods Stochastic Modeling Stochastic Optimal Control Stochastic processes Filtering theory data assimilation Monte carlo methods Variance Reduction Importance sampling

Monte Carlo methods. Simulation, estimation, data assimilation, and optimal control for time-discrete and time-continuous Markov chains

Data Assimilation - Part 2

Kody J. H. Law, Senior Research Scientist, Stochastic Numerics Research Group

Feb 20, 15:00 - 17:30

B3 R5520

data assimilation

Please register in advance at: Registration See please the program here: Schedule See lecture notes here: Notes See Melanie Ades notes - Part I: Slides See MATLAB code here: Code See Mealine Ades notes - Part II: Slides See Kody Law notes here: Slides

Data Assimilation - Part 1

Kody J. H. Law, Senior Research Scientist, Stochastic Numerics Research Group

Feb 17, 09:00 - 11:15

B3 R5520

data assimilation

Please register in advance at: Registration See please the program here: Schedule See lecture notes here: Notes See Melanie Ades notes - Part I: Slides See MATLAB code here: Code See Mealine Ades notes - Part II: Slides See Kody Law notes here: Slides

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

1 min read · Mon, Feb 27 2017

data assimilation uncertainty quantification

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.

Raul Tempone

Professor, Applied Mathematics and Computational Science

numerical analysis Computational finance computational statistics stochastic differential equations uncertainty quantification bayesian inference data assimilation hierarchical and sparse approximation optimal control optimal experimental design stochastic optimization

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.

Kody J. H. Law

Senior Research Scientist, Stochastic Numerics Research Group

uncertainty quantification data assimilation probability theory

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

Håkon A. Hoel

Visiting Researcher, Stochastic Numerics Research Group

numerical analysis Mathematical modeling data assimilation

Håkon Hoel was a visiting researcher in the Stochastic Numerics Group at KAUST. He holds an MSc degree in Computational Science from the University of Oslo (2016) and a Ph.D. in Numerical Analysis from KTH Royal Institute of Technology (2012). His main research interests are numerical analysis of stochastic differential equations, nonlinear filtering, and multilevel Monte Carlo (MLMC) methods. His research interests also include different aspects of implementation and error estimation of stochastic models in the following areas: adaptive weak approximation of stochastic differential equations