Zobrazeno 1 - 10
of 118
pro vyhledávání: '"Broccardo, Marco"'
Stochastic ground motion models (GMMs) are gaining popularity and momentum among engineers to perform time-history analysis of structures and infrastructures. This paper aims to review and validate hierarchical stochastic GMMs, with a focus on identi
Externí odkaz:
http://arxiv.org/abs/2411.07401
The active learning (AL) technique, one of the state-of-the-art methods for constructing surrogate models, has shown high accuracy and efficiency in forward uncertainty quantification (UQ) analysis. This paper provides a comprehensive study on AL-bas
Externí odkaz:
http://arxiv.org/abs/2404.07323
Synthetic ground motions (GMs) play a fundamental role in both deterministic and probabilistic seismic engineering assessments. This paper shows that the family of filtered and modulated white noise stochastic GM models overlooks a key parameter -- t
Externí odkaz:
http://arxiv.org/abs/2401.03747
Publikováno v:
In Reliability Engineering and System Safety December 2024 252
With the unfolding of the COVID-19 pandemic, mathematical modeling of epidemics has been perceived and used as a central element in understanding, predicting, and governing the pandemic event. However, soon it became clear that long term predictions
Externí odkaz:
http://arxiv.org/abs/2007.04136
Since the early 1900s, numerous research efforts have been devoted to developing quantitative solutions to stochastic mechanical systems. In general, the problem is perceived as solved when a complete or partial probabilistic description on the quant
Externí odkaz:
http://arxiv.org/abs/2003.02205
Autor:
Mignan, Arnaud, Broccardo, Marco
Publikováno v:
Seismological Research Letters, 2020
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. Earthquake prediction, the Grail of Seismology, is, in this context of con
Externí odkaz:
http://arxiv.org/abs/1910.01178
Autor:
Wang, Ziqi, Broccardo, Marco
This paper proposes an active learning-based Gaussian process (AL-GP) metamodelling method to estimate the cumulative as well as complementary cumulative distribution function (CDF/CCDF) for forward uncertainty quantification (UQ) problems. Within th
Externí odkaz:
http://arxiv.org/abs/1908.10341
Autor:
Mignan, Arnaud, Broccardo, Marco
Publikováno v:
Nature, 574, 2019
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 spatial distribution of aftershocks using Deep Learning (DL) and stat
Externí odkaz:
http://arxiv.org/abs/1904.01983