Numerical approaches for collaborative data processing |
| |
Authors: | Pete Seiler Michael Frenklach Andrew Packard Ryan Feeley |
| |
Affiliation: | (1) Honeywell Labs in Minneapolis, MN;(2) Department of Mechanical Engineering, University of California, Berkeley |
| |
Abstract: | 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.
This work was supported by the National Science Foundation, Information Technology Research Program, Grant No. CTS-0113985. |
| |
Keywords: | Model validation Prediction Sums-of-squares polynomials Semidefinite programming |
本文献已被 SpringerLink 等数据库收录! |