activity
20172023
most citedValue of information from vibration-based structural health monitoring extracted via Bayesian model updating

131 citations · 1k across the 33 of their papers we have counts for

collaborators

37 papers

cs.LG2023

POMDP inference and robust solution via deep reinforcement learning: An application to railway optimal maintenance

Giacomo Arcieri, Cyprien Hoelzl, Oliver Schwery +3

Partially Observable Markov Decision Processes (POMDPs) can model complex sequential decision-making problems under stochastic and uncertain environments. A main reason hindering t…

physics.app-ph2023★ 63 cited

A Nonlinear Damped Metamaterial: Wideband Attenuation with Nonlinear Bandgap and Modal Dissipation

Bao Zhao, Henrik R. Thomsen, Xingbo Pu +6

In this paper, we incorporate the effect of nonlinear damping with the concept of locally resonant metamaterials to enable vibration attenuation beyond the conventional bandgap ran…

stat.AP2023★ 2 cited

Assessment of alternative covariance functions for joint input-state estimation via Gaussian Process latent force models in structural dynamics

Silvia Vettori, Emilio Di Lorenzo, Bart Peeters +1

Digital technologies can be used to gather accurate information about the behavior of structural components for improving systems design, as well as for enabling advanced Structura…

stat.AP2023★ 58 cited

A metric for assessing and optimizing data-driven prognostic algorithms for predictive maintenance

Antonios Kamariotis, Konstantinos Tatsis, Eleni Chatzi +2

Prognostic Health Management aims to predict the Remaining Useful Life (RUL) of degrading components/systems utilizing monitoring data. These RUL predictions form the basis for opt…

math.NA2023

VpROM: A novel Variational AutoEncoder-boosted Reduced Order Model for the treatment of parametric dependencies in nonlinear systems

Thomas Simpson, Konstantinos Vlachas, Anthony Garland +2

Reduced Order Models (ROMs) are of considerable importance in many areas of engineering in which computational time presents difficulties. Established approaches employ projection-…

cs.CE2023★ 6 cited

Graph Neural Networks for Aerodynamic Flow Reconstruction from Sparse Sensing

Gregory Duthé, Imad Abdallah, Sarah Barber +1

Sensing the fluid flow around an arbitrary geometry entails extrapolating from the physical quantities perceived at its surface in order to reconstruct the features of the surround…