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Costas Papadimitriou  - - - 
Top co-authors See all
Michael I. Friswell

390 shared publications

College of Engineernig, Swansea Uninversity, United Kingdom

Miguel Ángel Aguirre

315 shared publications

Department of Analytical Chemistry and Food Science and University Institute of Materials, Faculty of Science, University of Alicante, P.O. Box 99, 03080 Alicante, Spain

C. Soize

195 shared publications

Laboratoire Modélisation et Simulation Multi Echelle; MSME UMR 8208 CNRS, Université Paris-Est, 5 bd Descartes, 77454 Marne-la-Vallee France

D. Straub

177 shared publications

Technische Universität München; Engineering Risk Analysis (ERA) Group; Arcisstr. 21 80290 München Germany

Annalisa Paolone

159 shared publications

University of Roma - U.O.S. La Sapienza, Italy

137
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Publication Record
Distribution of Articles published per year 
(1993 - 2018)
Total number of journals
published in
 
25
 
Publications See all
Article 0 Reads 0 Citations Probabilistic hierarchical Bayesian framework for time-domain model updating and robust predictions Omid Sedehi, Costas Papadimitriou, Lambros S. Katafygiotis Published: 01 October 2018
Mechanical Systems and Signal Processing, doi: 10.1016/j.ymssp.2018.09.041
DOI See at publisher website
BOOK-CHAPTER 0 Reads 0 Citations Fatigue Monitoring and Remaining Lifetime Prognosis Using Operational Vibration Measurements Costas Papadimitriou, Eleni N. Chatzi, Saeed Eftekhar Azam, ... Published: 31 July 2018
Conference Proceedings of the Society for Experimental Mechanics Series, doi: 10.1007/978-3-319-74793-4_17
DOI See at publisher website
Article 0 Reads 0 Citations Bayesian optimal sensor placement for crack identification in structures using strain measurements Costas Argyris, Sharmistha Chowdhury, Volkmar Zabel, Costas ... Published: 31 January 2018
Structural Control and Health Monitoring, doi: 10.1002/stc.2137
DOI See at publisher website
Article 0 Reads 1 Citation Bayesian optimal estimation for output-only nonlinear system and damage identification of civil structures Hamed Ebrahimian, Rodrigo Astroza, Joel P. Conte, Costas Pap... Published: 24 January 2018
Structural Control and Health Monitoring, doi: 10.1002/stc.2128
DOI See at publisher website
Article 0 Reads 1 Citation Data driven inference for the repulsive exponent of the Lennard-Jones potential in molecular dynamics simulations Lina Kulakova, Georgios Arampatzis, Panagiotis Angelikopoulo... Published: 29 November 2017
Scientific Reports, doi: 10.1038/s41598-017-16314-4
DOI See at publisher website ABS Show/hide abstract
The Lennard-Jones (LJ) potential is a cornerstone of Molecular Dynamics (MD) simulations and among the most widely used computational kernels in science. The LJ potential models atomistic attraction and repulsion with century old prescribed parameters (q = 6, p = 12, respectively), originally related by a factor of two for simplicity of calculations. We propose the inference of the repulsion exponent through Hierarchical Bayesian uncertainty quantification We use experimental data of the radial distribution function and dimer interaction energies from quantum mechanics simulations. We find that the repulsion exponent p ≈ 6.5 provides an excellent fit for the experimental data of liquid argon, for a range of thermodynamic conditions, as well as for saturated argon vapour. Calibration using the quantum simulation data did not provide a good fit in these cases. However, values p ≈ 12.7 obtained by dimer quantum simulations are preferred for the argon gas while lower values are promoted by experimental data. These results show that the proposed LJ 6-p potential applies to a wider range of thermodynamic conditions, than the classical LJ 6-12 potential. We suggest that calibration of the repulsive exponent in the LJ potential widens the range of applicability and accuracy of MD simulations.
Article 0 Reads 1 Citation Optimal sensor placement for multi-setup modal analysis of structures Jie Zhang, Kristof Maes, Guido De Roeck, Edwin Reynders, Cos... Published: 01 August 2017
Journal of Sound and Vibration, doi: 10.1016/j.jsv.2017.04.041
DOI See at publisher website
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