Numerical approaches for collaborative data processing

被引:0
作者
Pete Seiler
Michael Frenklach
Andrew Packard
Ryan Feeley
机构
[1] Honeywell Labs in Minneapolis,Department of Mechanical Engineering
[2] University of California,undefined
来源
Optimization and Engineering | 2006年 / 7卷
关键词
Model validation; Prediction; Sums-of-squares polynomials; Semidefinite programming;
D O I
暂无
中图分类号
学科分类号
摘要
We present an approach to uncertainty propagation in dynamic systems, exploiting information provided by related experimental results along with their models. The approach relies on a solution mapping technique to approximate mathematical models by polynomial surrogate models. We use these surrogate models to formulate prediction bounds in terms of polynomial optimizations. Recent results on polynomial optimizations are then applied to solve the prediction problem. Two examples which illustrate the key aspects of the proposed algorithm are given. The proposed algorithm offers a framework for collaborative data processing among researchers.
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收藏
页码:459 / 478
页数:19
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