Polynomial regression with derivative information in nuclear reactor uncertainty quantification
نویسندگان
چکیده
We introduce a novel technique of uncertainty quantification using polynomial regression with derivative information and apply it to analyze the performance of a model of a sodium-cooled fast reactor. We construct a surrogate model as a goal-oriented projection onto an incomplete space of polynomials, find coordinates of projection by collocation, and use derivative information to reduce the number of sample points required by the collocation procedure. This surrogate model can be used to estimate range, sensitivities and the statistical distribution of the output. Numerical experiments show that the suggested approach is significantly more computationally efficient than random sampling, or approaches that do not use derivative information, and that it has greater precision than linear models.
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