Stochastic dynamic predictions using Gaussian process models for nanoparticle synthesis
نویسندگان
چکیده
Gaussian process modeling (also known as kriging) is an empirical modeling approach that has been widely applied in engineering for the approximation of deterministic functions, due to 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 systems with multiple outputs. Our
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ورودعنوان ژورنال:
- Computers & Chemical Engineering
دوره 34 شماره
صفحات -
تاریخ انتشار 2010