TITLE: Stochastic dynamic predictions using Gaussian process models for nanoparticle synthesis

SPEAKER: Andres Felipe Hernandez Moreno and Professor Martha Grover

ABSTRACT:

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.