Collaborative data processing in developing predictive models of complex reaction systems

Michael Frenklach, Andrew Packard, Pete Seiler, Ryan Feeley

Research output: Contribution to journalArticlepeer-review

101 Scopus citations

Abstract

The subject of this report is a methodology for the transformation of (experimental) data into predictive models. We use a concrete example, drawn from the field of combustion chemistry, and examine the data in terms of precisely defined modes of scientific collaboration. The numerical methodology that we employ is founded on a combination of response surface technique and robust control theory. The numerical results show that an essential element of scientific collaboration is collaborative processing of data, demonstrating that combining the entire collection of data into a joint analysis extracts substantially more of the information content of the data.

Original languageEnglish (US)
Pages (from-to)57-66
Number of pages10
JournalInternational Journal of Chemical Kinetics
Volume36
Issue number1
DOIs
StatePublished - Jan 1 2004

Fingerprint Dive into the research topics of 'Collaborative data processing in developing predictive models of complex reaction systems'. Together they form a unique fingerprint.

Cite this