3 citations · 3 across the 1 of their papers we have counts for
6 papers
Bayesian Structural Identification using Gaussian Process Discrepancy Models
Antonina M. Kosikova, Omid Sedehi, Costas Papadimitriou +1
Bayesian model updating based on Gaussian Process (GP) models has received attention in recent years, which incorporates kernel-based GPs to provide enhanced fidelity response pred…
Data-Driven Uncertainty Quantification and Propagation in Structural Dynamics through a Hierarchical Bayesian Framework
Omid Sedehi, Costas Papadimitriou, Lambros S. Katafygiotis
In the presence of modeling errors, the mainstream Bayesian methods seldom give a realistic account of uncertainties as they commonly underestimate the inherent variability of para…
Optimal sensing for fish school identification
Pascal Weber, Georgios Arampatzis, Guido Novati +3
Fish schooling implies an awareness of the swimmers for their companions. In flow mediated environments, in addition to visual cues, pressure and shear sensors on the fish body are…
Hierarchical Bayesian Operational Modal Analysis: Theory and Computations
Omid Sedehi, Lambros S. Katafygiotis, Costas Papadimitriou
This paper presents a hierarchical Bayesian modeling framework for the uncertainty quantification in modal identification of linear dynamical systems using multiple vibration data…
Optimal sensor placement for artificial swimmers
Siddhartha Verma, Costas Papadimitriou, Nora Luethen +2
Natural swimmers rely for their survival on sensors that gather information from the environment and guide their actions. The spatial organization of these sensors, such as the vis…
Experimental data over quantum mechanics simulations for inferring the repulsive exponent of the Lennard-Jones potential in Molecular Dynamics
Lina Kulakova, Georgios Arampatzis, Panagiotis Angelikopoulos +3
The Lennard-Jones (LJ) potential is a cornerstone of Molecular Dynamics (MD) simulations and among the most widely used computational kernels in science. The potential models atomi…