Matteo Broggi

Dr. Matteo Broggi

Leibniz Universität Hannover
orcid

Research Focus & Metrics

Academic Career Length

19Years (2008-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 March 2018 in Mechanical Systems and Signal Processing
Verified
Journal Article
Closed access
roberto rocchettaroberto rocchetta
roberto rocchetta
MBMatteo Broggi
Matteo Broggi
QHQuentin Huchet
Quentin Huchet
EPEdoardo Patelli
Edoardo Patelli

1 February 2019 in Mechanical Systems and Signal Processing
Verified
Journal Article
Closed access
SBSifeng BI
Sifeng BI
MBMatteo Broggi
Matteo Broggi
MBMichael Beer
Michael Beer

1 July 2011 in Finite Elements in Analysis and Design
Verified
Journal Article
Closed access
BGB. Goller
B. Goller
MBMatteo Broggi
Matteo Broggi
ACA. Calvi
A. Calvi
GSG.I. Schuëller
G.I. Schuëller

1 June 2019 in Mechanical Systems and Signal Processing
Verified
Journal Article
Closed access
PWPengfei Wei
Pengfei Wei
JSJingwen Song
Jingwen Song
SBSifeng BI
Sifeng BI
MBMatteo Broggi
Matteo Broggi
MBMichael Beer
Michael Beer
ZYZhufeng Yue
Zhufeng Yue

23 February 2017 in Archive of Applied Mechanics
Verified
Journal Article
Open access
PDF file
EPEdoardo Patelli
Edoardo Patelli
YGYves Govers
Yves Govers
MBMatteo Broggi
Matteo Broggi
HGHerbert Martins Gomes
Herbert Martins Gomes
MLMichael Link
Michael Link
JMJohn E. Mottershead
John E. Mottershead
Deterministic model updating is now a mature technology widely applied to large-scale industrial structures. It is concerned with the calibration of the parameters of a single model based on one set of test data. It is, of course, well known that different analysts produce different finite element models, make different physics-based assumptions, and parameterize their models differently. Also, tests carried out on the same structure, by different operatives, at different times, under different ambient conditions produce different results. There is no unique model and no unique data. Therefore, model updating needs to take account of modeling and test-data variability. Much emphasis is now placed on what has become known as stochastic model updating where data are available from multiple nominally identical test structures. In this paper two currently prominent stochastic model updating techniques (sensitivity-based updating and Bayesian model updating) are described and applied to the DLR AIRMOD structure.

1 March 2019 in Mechanical Systems and Signal Processing
Verified
Journal Article
Closed access
MFMatthias Faes
Matthias Faes
MBMatteo Broggi
Matteo Broggi
EPEdoardo Patelli
Edoardo Patelli
YGYves Govers
Yves Govers
JMJohn Mottershead
John Mottershead
DMDavid Moens
David Moens