13 citations · 13 across the 1 of their papers we have counts for
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Euclid preparation: IX. EuclidEmulator2 -- Power spectrum emulation with massive neutrinos and self-consistent dark energy perturbations
Euclid Collaboration, M. Knabenhans, J. Stadel +131
We present a new, updated version of the EuclidEmulator (called EuclidEmulator2), a fast and accurate predictor for the nonlinear correction of the matter power spectrum. Percent-l…
Bayesian model inversion using stochastic spectral embedding
P. -R. Wagner, S. Marelli, B. Sudret
In this paper we propose a new sampling-free approach to solve Bayesian model inversion problems that is an extension of the previously proposed spectral likelihood expansions (SLE…
Stochastic spectral embedding
S. Marelli, P. -R. Wagner, C. Lataniotis +1
Constructing approximations that can accurately mimic the behavior of complex models at reduced computational costs is an important aspect of uncertainty quantification. Despite th…
Machine learning applied to simulations of collisions between rotating, differentiated planets
Miles Timpe, Maria Han Veiga, Mischa Knabenhans +2
In the late stages of terrestrial planet formation, pairwise collisions between planetary-sized bodies act as the fundamental agent of planet growth. These collisions can lead to e…