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This paper presents a novel multi-scale method for elliptic partial differential equations with arbitrarily rough coefficients. In the spirit of numerical homogenization, the method constructs problem-adapted ansatz spaces with uniform…

Numerical Analysis · Mathematics 2024-08-05 Philip Freese , Moritz Hauck , Tim Keil , Daniel Peterseim

In this article, we introduce and investigate polynomial curvelets on spheres, which form a class of Parseval frames for $L^2(\mathbb{S}^{d-1})$, $d \geq 3$. The proposed construction offers a directionally sensitive multiscale…

Classical Analysis and ODEs · Mathematics 2026-03-16 Frederic Schoppert

Relativistic domain walls are studied in the framework of a polynomial approximation to the field interpolating between different vacua and forming the domain wall. In this approach we can calculate evolution of a core and of a width of the…

High Energy Physics - Theory · Physics 2010-11-01 H. Arodz

A new domain decomposition method is introduced for the heterogeneous 2-D and 3-D Helmholtz equations. Transmission conditions based on the perfectly matched layer (PML) are derived that avoid artificial reflections and match incoming and…

Numerical Analysis · Mathematics 2013-06-24 Christiaan C. Stolk

A computational framework that leverages data from self-consistent field theory simulations with deep learning to accelerate the exploration of parameter space for block copolymers is presented. This is a substantial two-dimensional…

Materials Science · Physics 2023-07-04 Yao Xuan , Kris T. Delaney , Hector D. Ceniceros , Glenn H. Fredrickson

We investigate the problem of finding inner ap-proximations of positive semidefinite (PSD) cones. We developa novel decomposition framework of the PSD cone by meansof conical combinations of smaller dimensional sub-cones. Weshow that many…

Optimization and Control · Mathematics 2021-10-01 Tianqi Zheng , James Guthrie , Enrique Mallada

Recently A. Eremenko and P. Yuditskii found explicit solutions of the best polynomial approximation problems of sgn(x) over two intervals in terms of conformal mappings onto special comb domains. We give analogous solutions for the best…

Complex Variables · Mathematics 2014-11-26 A. Lukashov , D. Prokhorov

Frames in a separable quaternionic Hilbert space were introduced and studied in [17] to have more applications. In this paper, we extend the study of frames in quaternionic Hilbert spaces and introduce different types of duals of a frame in…

Functional Analysis · Mathematics 2018-03-16 S. K. Sharma , Ghanshyam Singh , Soniya Sahu

The article is devoted to the study of mappings that satisfy the so-called inverse Poletsky inequality. We consider mappings of quasiextremal distance domains, domains with a locally quasiconformal boundary, and domains which are regular in…

Complex Variables · Mathematics 2023-10-03 M. V. Androschuk , O. P. Dovhopiatyi , N. S. Ilkevych , E. A. Sevost'yanov

In this work, we consider the approximation capabilities of shallow neural networks in weighted Sobolev spaces for functions in the spectral Barron space. The existing literature already covers several cases, in which the spectral Barron…

Machine Learning · Computer Science 2024-11-07 Ahmed Abdeljawad , Thomas Dittrich

Given a sequence of Marcinkiewicz-Zygmund inequalities in $L_2$ on a usual compact space $\mathcal M$, Gr\"ochenig introduced the weighted least squares polynomials and the least squares quadrature from pointwise samples of a function, and…

Numerical Analysis · Mathematics 2021-01-12 Wanting Lu , Heping Wang

The paper is devoted to discretization of integral norms of functions from a given finite dimensional subspace. We use recent general results on sampling discretization to derive a new Marcinkiewicz type discretization theorem for the…

Numerical Analysis · Mathematics 2020-05-14 Vladimir Temlyakov

This work provides theoretical foundations for kernel methods in the hyperspherical context. Specifically, we characterise the native spaces (reproducing kernel Hilbert spaces) and the Sobolev spaces associated with kernels defined over…

Machine Learning · Statistics 2022-11-18 Simon Hubbert , Emilio Porcu , Chris. J. Oates , Mark Girolami

We present and study approximate notions of dimensional and margin complexity, which correspond to the minimal dimension or norm of an embedding required to approximate, rather then exactly represent, a given hypothesis class. We show that…

Machine Learning · Computer Science 2020-03-10 Pritish Kamath , Omar Montasser , Nathan Srebro

In convex geometry, the Blaschke surface area measure on the boundary of a convex domain can be interpreted in terms of the complexity of approximating polyhedra. In response to a question raised by D. Barrett, this approach is formulated…

Complex Variables · Mathematics 2016-05-03 Purvi Gupta

We consider the problem of approximating a semialgebraic set with a sublevel-set of a polynomial function. In this setting, it is standard to seek a minimum volume outer approximation and/or maximum volume inner approximation. As there is…

Optimization and Control · Mathematics 2022-05-30 James Guthrie

In this paper, we investigate the local-in-time well-posedness for the two-dimensional Prandtl equations in weighted Sobolev spaces under the Oleinik's monotonicity condition.Due to the loss of tangential derivative caused by vertical…

Analysis of PDEs · Mathematics 2018-11-30 Jincheng Gao , Daiwen Huang , Zheng-an Yao

In a cylindrical space-time domain with a convex, spatial base, we establish a local Lipschitz estimate for weak solutions to parabolic systems with Uhlenbeck structure up to the lateral boundary, provided homogeneous Dirichlet data are…

Analysis of PDEs · Mathematics 2021-10-19 Verena Bögelein , Frank Duzaar , Naian Liao , Christoph Scheven

In this paper we consider a family of algorithms for approximate implicitization of rational parametric curves and surfaces. The main approximation tool in all of the approaches is the singular value decomposition, and they are therefore…

Numerical Analysis · Mathematics 2016-05-30 Oliver J. D. Barrowclough , Tor Dokken

Real-world data typically contain repeated and periodic patterns. This suggests that they can be effectively represented and compressed using only a few coefficients of an appropriate basis (e.g., Fourier, Wavelets, etc.). However, distance…

Machine Learning · Statistics 2014-05-26 Michail Vlachos , Nikolaos Freris , Anastasios Kyrillidis
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