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TITLE: Stochastic dynamic predictions using kriging for nanoparticle synthesis
SPEAKER: Professor Martha Grover
ABSTRACT:
Kriging 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, kriging has not been developed to approximate stochastic functions or to describe the dynamics of systems with multiple outputs. Our paper proposes a methodology to construct approximate models for multivariate stochastic dynamic simulations using kriging, by combining ideas from design of experiments and dynamic systems modeling. We then apply the methodology in the prediction of a dynamic size distribution during the synthesis of nanoparticles.
