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A new formulation for symbolic regression to identify physico-chemical laws from experimental data

; ; ; ;

In
The chemical engineering journal 387, Seiten/Artikel-Nr.:123412

ImpressumAmsterdam : Elsevier

ISSN1385-8947

Received 17 June 2019, Revised 5 November 2019, Accepted 6 November 2019, Available online 9 November 2019

Online
DOI: 10.1016/j.cej.2019.123412


Einrichtungen

  1. Lehrstuhl für Prozesstechnik (416410)



Dokumenttyp
Journal Article

Format
online, print

Sprache
English

Anmerkung
Peer reviewed article

Externe Identnummern
SCOPUS: SCOPUS:2-s2.0-85075484705
WOS Core Collection: WOS:000527331600014

Interne Identnummern
RWTH-2020-05353
Datensatz-ID: 789662

Beteiligte Länder
Germany, Singapore, UK

Lizenzstatus der Zeitschrift

 GO

Medline ; BIOSIS Previews ; Clarivate Analytics Master Journal List ; Current Contents - Engineering, Computing and Technology ; Ebsco Academic Search ; IF >= 5 ; JCR ; NCBI Molecular Biology Database ; NationallizenzNationallizenz ; SCOPUS ; Science Citation Index ; Science Citation Index Expanded ; Web of Science Core Collection
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The record appears in these collections:
Document types > Articles > Journal Articles
Faculty of Mechanical Engineering (Fac.4)
Public records
Publications database
416410
 Record created 2020-05-18, last modified 2025-10-15
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