Search Results for author:
Found 4 papers, 1 papers with code
Probabilistic Performance-Pattern Decomposition (PPPD): analysis framework and applications to stochastic mechanical systems
Since the early 1900s, numerous research efforts have been devoted to developing quantitative solutions to stochastic mechanical systems.
Neural Network Applications in Earthquake Prediction (1994-2019): Meta-Analytic Insight on their Limitations
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.
A novel active learning-based Gaussian process metamodelling strategy for estimating the full probability distribution in forward UQ analysis
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.
One neuron is more informative than a deep neural network for aftershock pattern forecasting
We reformulate the 2017 results in probabilistic terms using logistic regression (i. e., one neural network node) and obtain AUC = 0. 85 using 2 free parameters versus the 13, 451 parameters used by DeVries et al. (2018).
Geophysics
