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math.NA2024★ 3 cited
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…
math.NA2024
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…