116 citations · 352 across the 16 of their papers we have counts for
5 papers · 1 filter
Neural active manifolds: nonlinear dimensionality reduction for uncertainty quantification
Andrea Zanoni, Gianluca Geraci, Matteo Salvador +2
We present a new approach for nonlinear dimensionality reduction, specifically designed for computationally expensive mathematical models. We leverage autoencoders to discover a on…
Deep Learning without Global Optimization by Random Fourier Neural Networks
Owen Davis, Gianluca Geraci, Mohammad Motamed
We introduce a new training algorithm for deep neural networks that utilize random complex exponential activation functions. Our approach employs a Markov Chain Monte Carlo samplin…
Approximation Error and Complexity Bounds for ReLU Networks on Low-Regular Function Spaces
Owen Davis, Gianluca Geraci, Mohammad Motamed
In this work, we consider the approximation of a large class of bounded functions, with minimal regularity assumptions, by ReLU neural networks. We show that the approximation erro…
Global sensitivity analysis in Monte Carlo radiation transport
Kayla Clements, Gianluca Geraci, Aaron J Olson +1
We consider Global Sensitivity Analysis for Monte Carlo radiation transport applications. GSA is usually combined with Uncertainty Quantification, where the latter quantifies the v…
A polynomial chaos approach for uncertainty quantification of Monte Carlo transport codes
Gianluca Geraci, Kayla Clements, Aaron J Olson
In this contribution, we discuss the construction of Polynomial Chaos surrogates for Monte Carlo radiation transport applications via non-intrusive spectral projection. This contri…