297 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- ETH ZurichCH15 papers
- Technical University of MunichDE3 papers
- Institute of GeophysicsRU2 papers
- Swiss Seismological ServiceCH2 papers
- Agence Nationale pour la Gestion des Déchets RadioactifsFR1 paper
- Centre National de la Recherche ScientifiqueFR1 paper
- China Earthquake AdministrationCN1 paper
- Earthquake Engineering Research InstituteUS1 paper
- Institut des Sciences de la TerreFR1 paper
- Laboratoire d'Acoustique de l'Université du MansFR1 paper
- Laboratoire de Physique et Modélisation des Milieux CondensésFR1 paper
- Seoul National UniversityKR1 paper
15 papers
Design Space Exploration and Explanation via Conditional Variational Autoencoders in Meta-model-based Conceptual Design of Pedestrian Bridges
Vera M. Balmer, Sophia V. Kuhn, Rafael Bischof +4
For conceptual design, engineers rely on conventional iterative (often manual) techniques. Emerging parametric models facilitate design space exploration based on quantifiable perf…
On off-line and on-line Bayesian filtering for uncertainty quantification of structural deterioration
Antonios Kamariotis, Luca Sardi, Iason Papaioannou +2
Data-informed predictive maintenance planning largely relies on stochastic deterioration models. Monitoring information can be utilized to update sequentially the knowledge on time…
A framework for quantifying the value of vibration-based structural health monitoring
Antonios Kamariotis, Eleni Chatzi, Daniel Straub
The difficulty in quantifying the benefit of Structural Health Monitoring (SHM) for decision support is one of the bottlenecks to an extensive adoption of SHM on real-world structu…
Sequential Bayesian Inference for Uncertain Nonlinear Dynamic Systems: A Tutorial
Konstantinos E. Tatsis, Vasilis K. Dertimanis, Eleni N. Chatzi
In this article, an overview of Bayesian methods for sequential simulation from posterior distributions of nonlinear and non-Gaussian dynamic systems is presented. The focus is mai…
Bayesian tomography with prior-knowledge-based parametrization and surrogate modeling
Giovanni Angelo Meles, Niklas Linde, Stefano Marelli
We present a Bayesian tomography framework operating with prior-knowledge-based parametrization that is accelerated by surrogate models. Standard high-fidelity forward solvers solv…
Value of information from vibration-based structural health monitoring extracted via Bayesian model updating
Antonios Kamariotis, Eleni Chatzi, Daniel Straub
Quantifying the value of the information extracted from a structural health monitoring (SHM) system is an important step towards convincing decision makers to implement these syste…
Localized modes on a metasurface through multi-wave interactions
Martin Lott, Philippe Roux, Léonard Seydoux +4
In this paper, we describe the manifestation of localized states through coherent and incoherent analyses of a diffuse elastic wavefield inside a two-dimensional metamaterial made…
Neural Network Applications in Earthquake Prediction (1994-2019): Meta-Analytic Insight on their Limitations
Arnaud Mignan, Marco Broccardo
In the last few years, deep learning has solved seemingly intractable problems, boosting the hope to find approximate solutions to problems that now are considered unsolvable. Eart…
One neuron is more informative than a deep neural network for aftershock pattern forecasting
Arnaud Mignan, Marco Broccardo
29 August 2018: "Artificial intelligence nails predictions of earthquake aftershocks". This Nature News headline is based on the results of DeVries et al. (2018) who forecasted the…
Principal component analysis and sparse polynomial chaos expansions for global sensitivity analysis and model calibration: application to urban drainage simulation
Joseph B. Nagel, Jörg Rieckermann, Bruno Sudret
This paper presents an efficient surrogate modeling strategy for the uncertainty quantification and Bayesian calibration of a hydrological model. In particular, a process-based dyn…
An active-learning algorithm that combines sparse polynomial chaos expansions and bootstrap for structural reliability analysis
S. Marelli, B. Sudret
Polynomial chaos expansions (PCE) have seen widespread use in the context of uncertainty quantification. However, their application to structural reliability problems has been hind…
Hamiltonian Monte Carlo Methods for Subset Simulation in Reliability Analysis
Ziqi Wang, Marco Broccardo, Junho Song
This paper studies a non-random-walk Markov Chain Monte Carlo method, namely the Hamiltonian Monte Carlo (HMC) method in the context of Subset Simulation used for structural reliab…
Seismic fragility curves for structures using non-parametric representations
C. Mai, K. Konakli, B. Sudret
Fragility curves are commonly used in civil engineering to assess the vulnerability of structures to earthquakes. The probability of failure associated with a prescribed criterion…
Spectral likelihood expansions for Bayesian inference
Joseph B. Nagel, Bruno Sudret
A spectral approach to Bayesian inference is presented. It pursues the emulation of the posterior probability density. The starting point is a series expansion of the likelihood fu…
Using sparse polynomial chaos expansions for the global sensitivity analysis of groundwater lifetime expectancy in a multi-layered hydrogeological model
G. Deman, K. Konakli, B. Sudret +3
The study makes use of polynomial chaos expansions to compute Sobol' indices within the frame of a global sensitivity analysis of hydro-dispersive parameters in a simplified vertic…