Connecting Theory to Experiment for Chemical Mechanisms and Chemical Kinetics. Release of the newly developed CheKiPEUQ software for Bayesian Parameter Estimation

Dr. Aditya Savara
Dr. Aditya Savara

Abstract: This talk will showcase several recent years work. The biggest topic will be utilizing Bayesian Parameter Estimation to connect experiment and theory for more physically realistic parameter estimation. This work includes development of CheKiPEUQ, a user friendly and general python based software for Bayesian Parameter Estimation from simulations.  Other topics that will be touched upon include Steady State Detection, prediction of the pre-requisites for steady state detection, and CiteSoft (a standard and a protocol for exporting citable references during software runs). The focus will be on connecting simulations to experiments, rather than on the chemistry.

Bio: Aditya “Ashi” Savara is a mid-career staff researcher in the ORNL Chemical Sciences division. His work primarily involves connecting chemical kinetics simulations with experimentally observed data for the purposes of better understanding what controls the rates and operative mechanisms of chemical reactions. A large part of his work involves simulation + parameter estimation where the simulations are by differential equations or Kinetic Monte Carlo methods. His work is focused on surface chemistry and catalysis.

Join Microsoft Teams Meeting

+1 865-276-6990   United States, Knoxville (Toll)

Conference ID: 448 916 163#

Local numbers | Reset PIN | Learn more about Teams | Meeting options


Last Updated: July 21, 2020 - 8:01 am