Zobrazeno 1 - 10
of 94
pro vyhledávání: '"Glaubitz, Jan"'
Autor:
Glaubitz, Jan, Ranocha, Hendrik, Winters, Andrew R., Schlottke-Lakemper, Michael, Öffner, Philipp, Gassner, Gregor
There is a pressing demand for robust, high-order baseline schemes for conservation laws that minimize reliance on supplementary stabilization. In this work, we respond to this demand by developing new baseline schemes within a nodal discontinuous Ga
Externí odkaz:
http://arxiv.org/abs/2406.14557
We introduce a novel construction procedure for one-dimensional summation-by-parts (SBP) operators. Existing construction procedures for FSBP operators of the form $D = P^{-1} Q$ proceed as follows: Given a boundary operator $B$, the norm matrix $P$
Externí odkaz:
http://arxiv.org/abs/2405.08770
Formulating dynamical models for physical phenomena is essential for understanding the interplay between the different mechanisms and predicting the evolution of physical states. However, a dynamical model alone is often insufficient to address these
Externí odkaz:
http://arxiv.org/abs/2404.14328
Bayesian hierarchical models can provide efficient algorithms for finding sparse solutions to ill-posed inverse problems. The models typically comprise a conditionally Gaussian prior model for the unknown which is augmented by a generalized gamma hyp
Externí odkaz:
http://arxiv.org/abs/2402.16623
Autor:
Ranocha, Hendrik, Winters, Andrew R., Schlottke-Lakemper, Michael, Öffner, Philipp, Glaubitz, Jan, Gassner, Gregor J.
Publikováno v:
Journal of Computational Physics, 2024
We use the framework of upwind summation-by-parts (SBP) operators developed by Mattsson (2017, doi:10.1016/j.jcp.2017.01.042) and study different flux vector splittings in this context. To do so, we introduce discontinuous-Galerkin-like interface ter
Externí odkaz:
http://arxiv.org/abs/2311.13888
Many applications rely on solving time-dependent partial differential equations (PDEs) that include second derivatives. Summation-by-parts (SBP) operators are crucial for developing stable, high-order accurate numerical methodologies for such problem
Externí odkaz:
http://arxiv.org/abs/2306.16314
Autor:
Glaubitz, Jan, Gelb, Anne
Publikováno v:
SIAM-ASA J Uncertain Quantif 12(2), 2024
We present a hierarchical Bayesian learning approach to infer jointly sparse parameter vectors from multiple measurement vectors. Our model uses separate conditionally Gaussian priors for each parameter vector and common gamma-distributed hyper-param
Externí odkaz:
http://arxiv.org/abs/2303.16954
Autor:
Glaubitz, Jan, Reeger, Jonah A.
Publikováno v:
Bit Numer Math 63, 6 (2023)
Quadrature formulas (QFs) based on radial basis functions (RBFs) have become an essential tool for multivariate numerical integration of scattered data. Although numerous works have been published on RBF-QFs, their stability theory can still be consi
Externí odkaz:
http://arxiv.org/abs/2301.12998
Summation-by-parts (SBP) operators allow us to systematically develop energy-stable and high-order accurate numerical methods for time-dependent differential equations. Until recently, the main idea behind existing SBP operators was that polynomials
Externí odkaz:
http://arxiv.org/abs/2301.12996
Autor:
Xiao, Yao, Glaubitz, Jan
Publikováno v:
J Sci Comput 96, 4 (2023)
Recovering temporal image sequences (videos) based on indirect, noisy, or incomplete data is an essential yet challenging task. We specifically consider the case where each data set is missing vital information, which prevents the accurate recovery o
Externí odkaz:
http://arxiv.org/abs/2206.12745