About Alexander Litvinenko Alexander Litvinenko Senior Research Scientist, Stochastic Numerics Research Group spatio-temporal statistics uncertainty quantification 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 Events Presented Events May 4 - May 10, 2014 Response Surface in low-rank Tensor Train Format for Uncertainty Quantification by Dr. Alexander Litvinenko Alexander Litvinenko, Senior Research Scientist, Stochastic Numerics Research Group May 5, 16:00 - 17:00 B9 H2 SHAXC 2 Workshop Alexander Litvinenko joined the Stochastic Numerics Group and Strategic Initiative in Uncertainty Quantification at KAUST in 2013. He specializes in efficient numerical methods for stochastic PDEs, uncertainty quantification, and multi-linear algebra. He is involved in Bayesian update methods for solving inverse problems, with the goal of reducing the complexity both the stochastic forward problem as well as the Bayesian update by a low-rank (sparse) tensor data approximation. Apr 20 - Apr 26, 2014 Hierarchical matrix introduction course by Dr. Alexander Litvinenko and Dr. Rio Yokota Alexander Litvinenko, Senior Research Scientist, Stochastic Numerics Research Group Apr 22, 16:00 - Apr 23, 18:30 KAUST Library Alexander Litvinenko joined the Stochastic Numerics Group and Strategic Initiative in Uncertainty Quantification at KAUST in 2013. He specializes in efficient numerical methods for stochastic PDEs, uncertainty quantification, and multi-linear algebra. He is involved in Bayesian update methods for solving inverse problems, with the goal of reducing the complexity both the stochastic forward problem as well as the Bayesian update by a low-rank (sparse) tensor data approximation. Apr 6 - Apr 12, 2014 Scalable hierarchical algorithms for PDEs and UQ By Dr. Alexander Litvinenko and Dr. Rio Yokota Alexander Litvinenko, Senior Research Scientist, Stochastic Numerics Research Group Apr 10, 12:00 - 13:00 B9 H1 Hierarchy is a key ingredient for achieving optimal arithmetic complexity and scalable communication complexity in algorithms. Fast Multipole Methods (FMM) and H-matrices share many common features that arise from their hierarchical nature. In this talk, Rio Yokota will illustrate the common features between FMM and H-matrices and how these features are favorable on computer architectures of the next generation. Feb 2 - Feb 8, 2014 Overview of numerical methods for quantification of uncertainties by Dr. Alexander Litvinenko Alexander Litvinenko, Senior Research Scientist, Stochastic Numerics Research Group Feb 4, 11:00 - 12:00 B9 R4222 In this talk, I will explain what is uncertainty quantification and how to model uncertainties via random variables/random fields. I will give several examples of stochastic partial differential equations with uncertain coefficients, uncertain computations domain, uncertain right-hand side, or boundary conditions.
Response Surface in low-rank Tensor Train Format for Uncertainty Quantification by Dr. Alexander Litvinenko Alexander Litvinenko, Senior Research Scientist, Stochastic Numerics Research Group May 5, 16:00 - 17:00 B9 H2 SHAXC 2 Workshop Alexander Litvinenko joined the Stochastic Numerics Group and Strategic Initiative in Uncertainty Quantification at KAUST in 2013. He specializes in efficient numerical methods for stochastic PDEs, uncertainty quantification, and multi-linear algebra. He is involved in Bayesian update methods for solving inverse problems, with the goal of reducing the complexity both the stochastic forward problem as well as the Bayesian update by a low-rank (sparse) tensor data approximation.
Hierarchical matrix introduction course by Dr. Alexander Litvinenko and Dr. Rio Yokota Alexander Litvinenko, Senior Research Scientist, Stochastic Numerics Research Group Apr 22, 16:00 - Apr 23, 18:30 KAUST Library Alexander Litvinenko joined the Stochastic Numerics Group and Strategic Initiative in Uncertainty Quantification at KAUST in 2013. He specializes in efficient numerical methods for stochastic PDEs, uncertainty quantification, and multi-linear algebra. He is involved in Bayesian update methods for solving inverse problems, with the goal of reducing the complexity both the stochastic forward problem as well as the Bayesian update by a low-rank (sparse) tensor data approximation.
Scalable hierarchical algorithms for PDEs and UQ By Dr. Alexander Litvinenko and Dr. Rio Yokota Alexander Litvinenko, Senior Research Scientist, Stochastic Numerics Research Group Apr 10, 12:00 - 13:00 B9 H1 Hierarchy is a key ingredient for achieving optimal arithmetic complexity and scalable communication complexity in algorithms. Fast Multipole Methods (FMM) and H-matrices share many common features that arise from their hierarchical nature. In this talk, Rio Yokota will illustrate the common features between FMM and H-matrices and how these features are favorable on computer architectures of the next generation.
Overview of numerical methods for quantification of uncertainties by Dr. Alexander Litvinenko Alexander Litvinenko, Senior Research Scientist, Stochastic Numerics Research Group Feb 4, 11:00 - 12:00 B9 R4222 In this talk, I will explain what is uncertainty quantification and how to model uncertainties via random variables/random fields. I will give several examples of stochastic partial differential equations with uncertain coefficients, uncertain computations domain, uncertain right-hand side, or boundary conditions.
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