About Kody J. H. Law 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 Events Presented Events May 4 - May 10, 2014 Static and Sequential Probabilistic Inverse Problems: An Extreme-Scale Challenge Application By Dr. Kody Law Kody J. H. Law, Senior Research Scientist, Stochastic Numerics Research Group May 8, 12:30 - 13:30 B9 H2 In many Computational Science and Engineering applications, uncertainties in model, parameters, and initial condition, in concert with increasing computational power, have lead to an increasing interest in probabilistic solutions of large-scale problems. Even crude approximations of the probabilistic solution require many tens of deterministic solves and accurate ones often require thousands or millions. Mar 23 - Mar 29, 2014 MCMC Sampling of Posterior Probability Distributions over Fields by Dr. Kody Law Kody J. H. Law, Senior Research Scientist, Stochastic Numerics Research Group Mar 25, 11:00 - 12:00 B9 R4222 In many problems in science and engineering, one would like to perform inference on parametric fields, of space and/or time, in order to reduce uncertainties and improve predictive capabilities, for example, the computation of quantities of interest such as outputs of the model or various expectations thereof. Examples of such fields include the initial condition of fluid dynamical equations in the context of numerical weather prediction and oceanography, the permeability and porosity of multiphase subsurface flow in the context of oil exploration, or the forcing of a stochastic dynamical system in the context of molecular dynamics. Feb 16 - Feb 22, 2014 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
Static and Sequential Probabilistic Inverse Problems: An Extreme-Scale Challenge Application By Dr. Kody Law Kody J. H. Law, Senior Research Scientist, Stochastic Numerics Research Group May 8, 12:30 - 13:30 B9 H2 In many Computational Science and Engineering applications, uncertainties in model, parameters, and initial condition, in concert with increasing computational power, have lead to an increasing interest in probabilistic solutions of large-scale problems. Even crude approximations of the probabilistic solution require many tens of deterministic solves and accurate ones often require thousands or millions.
MCMC Sampling of Posterior Probability Distributions over Fields by Dr. Kody Law Kody J. H. Law, Senior Research Scientist, Stochastic Numerics Research Group Mar 25, 11:00 - 12:00 B9 R4222 In many problems in science and engineering, one would like to perform inference on parametric fields, of space and/or time, in order to reduce uncertainties and improve predictive capabilities, for example, the computation of quantities of interest such as outputs of the model or various expectations thereof. Examples of such fields include the initial condition of fluid dynamical equations in the context of numerical weather prediction and oceanography, the permeability and porosity of multiphase subsurface flow in the context of oil exploration, or the forcing of a stochastic dynamical system in the context of molecular dynamics.
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
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