Zobrazeno 1 - 10
of 215
pro vyhledávání: '"Schaefer, Florian"'
Autor:
Schäfer, Florian
Estimating nonlinear functionals of probability distributions from samples is a fundamental statistical problem. The "plug-in" estimator obtained by applying the target functional to the empirical distribution of samples is biased. Resampling methods
Externí odkaz:
http://arxiv.org/abs/2408.05826
Autor:
Zimmer, Bettina, Niebuur, Bart-Jan, Schaefer, Florian, Coupette, Fabian, Tänzel, Victor, Schilling, Tanja, Kraus, Tobias
Carbon black (CB)-elastomers can serve as low-cost, highly deformable sensor materials, but hardly any work exists on their structure-property relationships. We report on flow-induced anisotropy, considering CB-silicone films generated via doctor bla
Externí odkaz:
http://arxiv.org/abs/2407.20318
Vecchia approximation has been widely used to accurately scale Gaussian-process (GP) inference to large datasets, by expressing the joint density as a product of conditional densities with small conditioning sets. We study fixed-domain asymptotic pro
Externí odkaz:
http://arxiv.org/abs/2401.15813
Publikováno v:
Physical Review Fluids, 9, 094606 (2024)
Reynolds-averaged Navier--Stokes (RANS) closure must be sensitive to the flow physics, including nonlocality and anisotropy of the effective eddy viscosity. Recent approaches used forced direct numerical simulations to probe these effects, including
Externí odkaz:
http://arxiv.org/abs/2310.08763
Autor:
Cao, Ruijia, Schäfer, Florian
A key numerical difficulty in compressible fluid dynamics is the formation of shock waves. Shock waves feature jump discontinuities in the velocity and density of the fluid and thus preclude the existence of classical solutions to the compressible Eu
Externí odkaz:
http://arxiv.org/abs/2308.14127
Dense kernel matrices resulting from pairwise evaluations of a kernel function arise naturally in machine learning and statistics. Previous work in constructing sparse approximate inverse Cholesky factors of such matrices by minimizing Kullback-Leibl
Externí odkaz:
http://arxiv.org/abs/2307.11648
Publikováno v:
Journal of Computational Physics, 499, 112721 (2024)
The macroscopic forcing method (MFM) of Mani and Park and similar methods for obtaining turbulence closure operators, such as the Green's function-based approach of Hamba, recover reduced solution operators from repeated direct numerical simulations
Externí odkaz:
http://arxiv.org/abs/2306.13625
The theory of greedy low-rank learning (GLRL) aims to explain the impressive generalization capabilities of deep learning. It proves that stochastic gradient-based training implicitly regularizes neural networks towards low-rank solutions through a g
Externí odkaz:
http://arxiv.org/abs/2306.11250
We propose a new approach for sampling and Bayesian computation that uses the score of the target distribution to construct a transport from a given reference distribution to the target. Our approach is an infinite-dimensional Newton method, involvin
Externí odkaz:
http://arxiv.org/abs/2305.09792
In recent years, there has been widespread adoption of machine learning-based approaches to automate the solving of partial differential equations (PDEs). Among these approaches, Gaussian processes (GPs) and kernel methods have garnered considerable
Externí odkaz:
http://arxiv.org/abs/2304.01294