3 citations · 3 across the 5 of their papers we have counts for
5 papers
Embedding Model-form Uncertainty in Probabilistic Calibration of Digital Twins for Bridges
Daniel Andrés Arcones, Martin Weiser, Phaedon-Stelios Koutsourelakis +1
Digital twins of bridges rely on physics-based models to infer full-field structural responses from sparse monitoring data. The reliability of these predictions depends on the cali…
Bayesian Tendon Breakage Localization under Model Uncertainty Using Distributed Fiber Optic Sensors
Daniel Andrés Arcones, Aeneas Paul, Martin Weiser +3
This study develops a Bayesian, uncertainty-aware framework for tendon breakage localization in pre-stressed concrete members using high-resolution data from distributed fiber-opti…
Gaussian mixture models for model improvement
Paolo Villani, Daniel Andrés Arcones, Jörg F. Unger +1
Modeling complex physical systems such as they arise in civil engineering applications requires finding a trade-off between physical fidelity and practicality. Consequently, deviat…
Posterior sampling with Adaptive Gaussian Processes in Bayesian parameter identification
Paolo Villani, Daniel Andrés-Arcones, Jörg F. Unger +1
Posterior sampling by Monte Carlo methods provides a more comprehensive solution approach to inverse problems than computing point estimates such as the maximum posterior using opt…
Model bias identification for Bayesian calibration of stochastic digital twins of bridges
Daniel Andrés Arcones, Martin Weiser, Phaedon-Stelios Koutsourelakis +1
Simulation-based digital twins must provide accurate, robust and reliable digital representations of their physical counterparts. Quantifying the uncertainty in their predictions p…