Stochastic dynamic predictions using Gaussian process models for nanoparticle synthesis
TITLE: Stochastic dynamic predictions using Gaussian process models for nanoparticle synthesis
SPEAKER: Andres Felipe Hernandez Moreno and Professor Martha Grover
Gaussian process model is an empirical modeling approach that has been widely applied in engineering for the approximation of deterministic functions, due its flexibility and ability to interpolate observed data. Despite its statistical properties, Gaussian process models (GPM) have not been employed to describe the dynamics of stochastic complex system like nanoscale phenomena. This presentation describes the methodology to construct approximate models for multivariate stochastic dynamic simulations using GPM, combining ideas from design of experiments, spatial statistics and dynamic systems modeling. In particular, the effect of sampling strategies in the identification and prediction of the GPM is analyzed in detailed. The methodology is applied in the prediction of a dynamic size distribution during the synthesis of platinum nanoparticles under supercritical CO_2 conditions.