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Consider the $(2+1)$D Discrete Gaussian (ZGFF, integer-valued Gaussian free field) model in an $L\times L$ box above a hard floor. Bricmont, El-Mellouki and Fr\"ohlich (1986) established that, at low enough temperature, this random surface…

Probability · Mathematics 2025-09-05 Joseph Chen , Eyal Lubetzky

Planned wide-field weak lensing surveys are expected to reduce the statistical errors on the shear field to unprecedented levels. In contrast, systematic errors like those induced by the convolution with the point spread function (PSF) will…

Cosmology and Nongalactic Astrophysics · Physics 2012-10-23 M. Gentile , F. Courbin , G. Meylan

Computing accurate splines of degree greater than three is still a challenging task in today's applications. In this type of interpolation, high-order derivatives are needed on the given mesh. As these derivatives are rarely known and are…

Numerical Analysis · Mathematics 2023-03-20 A. Pepin , S. Léger , N. Beaudoin

In the present paper, using S.L. Sobolev's method, interpolation spline that minimizes the expression $\int_0^1(\varphi^{(m)}(x)+\omega^2\varphi^{(m-2)}(x))^2dx$ in the $K_2(P_m)$ space are constructed. Explicit formulas for the…

Numerical Analysis · Mathematics 2014-10-21 Abdullo R. Hayotov

Shepard method is a fast algorithm that has been classically used to interpolate scattered data in several dimensions. This is an important and well-known technique in numerical analysis founded in the main idea that data that is far away…

Numerical Analysis · Mathematics 2024-12-04 David Levin , José M. Ramón , Juan Ruiz-Alvarez , Dionisio F. Yáñez

For $h>0$ and positive integers $m$, $d$, such that $m>d/2$, we study non-stationary interpolation at the points of the scaled grid $h\mathbb{Z}^d$ via the Mat\'{e}rn kernel $\Phi_{m,d}$---the fundamental solution of $(1-\Delta)^m$ in…

Numerical Analysis · Mathematics 2020-09-04 Aurelian Bejancu

We examine the necessity of interpolation in overparameterized models, that is, when achieving optimal predictive risk in machine learning problems requires (nearly) interpolating the training data. In particular, we consider simple…

Machine Learning · Statistics 2022-06-17 Chen Cheng , John Duchi , Rohith Kuditipudi

An interpolation method to evaluate magnetic fields given unstructured, scattered magnetic data is presented. The method is based on the reconstruction of the global magnetic field using a superposition of orthogonal functions. The…

Computational Physics · Physics 2023-03-15 Minglei Yang , Diego del-Castillo-Negrete , Guannan Zhang , Matthew Beidler

The task of reconstructing smooth signals from streamed data in the form of signal samples arises in various applications. This work addresses such a task subject to a zero-delay response; that is, the smooth signal must be reconstructed…

Machine Learning · Computer Science 2023-08-22 Emilio Ruiz-Moreno , Luis Miguel López-Ramos , Baltasar Beferull-Lozano

In this paper a spline based integral approximation is utilized to propose a sequence of approximations to the error function that converge at a significantly faster manner than the default Taylor series. The approximations can be improved…

General Mathematics · Mathematics 2022-07-27 Roy M. Howard

We study the properties of points in $[0,1]^d$ generated by applying Hilbert's space-filling curve to uniformly distributed points in $[0,1]$. For deterministic sampling we obtain a discrepancy of $O(n^{-1/d})$ for $d\ge2$. For random…

Methodology · Statistics 2014-06-19 Zhijian He , Art B. Owen

We derive closed-form expressions for the poles and zeros of approximate fractional integrator/differentiator filters, which correspond to spectral roll-off filters having any desired log-log slope to a controllable degree of accuracy over…

Computational Engineering, Finance, and Science · Computer Science 2016-06-21 Julius Orion Smith , Harrison Freeman Smith

Using the Moore--Penrose pseudoinverse, this work generalizes the gradient approximation technique called centred simplex gradient to allow sample sets containing any number of points. This approximation technique is called the…

Numerical Analysis · Mathematics 2020-06-02 Warren Hare , Gabriel Jarry--Bolduc , Chayne Planiden

Effective verification and validation techniques for modern scientific machine learning workflows are challenging to devise. Statistical methods are abundant and easily deployed, but often rely on speculative assumptions about the data and…

Machine Learning · Computer Science 2025-02-11 Tyler Chang , Andrew Gillette , Romit Maulik

Interpolators -- estimators that achieve zero training error -- have attracted growing attention in machine learning, mainly because state-of-the art neural networks appear to be models of this type. In this paper, we study minimum $\ell_2$…

Statistics Theory · Mathematics 2022-09-12 Trevor Hastie , Andrea Montanari , Saharon Rosset , Ryan J. Tibshirani

In this paper we construct Ritz-type projectors with boundary interpolation properties in finite dimensional subspaces of the usual Sobolev space and we provide a priori error estimates for them. The abstract analysis is exemplified by…

Numerical Analysis · Mathematics 2022-03-03 Espen Sande , Carla Manni , Hendrik Speleers

We introduce a novel type of approximation spaces for functions with values in a nonlinear manifold. The discrete functions are constructed by piecewise polynomial interpolation in a Euclidean embedding space, and then projecting pointwise…

Numerical Analysis · Mathematics 2018-03-20 Philipp Grohs , Hanne Hardering , Oliver Sander , Markus Sprecher

In 2002 A.\ Hartmann and X.\ Massaneda obtained necessary and sufficient conditions for interpolation sequences for classes of analytic functions in the unit disc such that $\log M(r,f)=O((1-r)^{-\rho})$, $0<r<1$, $\rho \in (0 , +\infty)$,…

Complex Variables · Mathematics 2014-01-07 Igor Chyzhykov , Iryna Sheparovych

The discrepancy function measures the deviation of the empirical distribution of a point set in $[0,1]^d$ from the uniform distribution. In this paper, we study the classical discrepancy function with respect to the BMO and exponential…

Number Theory · Mathematics 2016-08-25 Josef Dick , Aicke Hinrichs , Lev Markhasin , Friedrich Pillichshammer

We analyze the approximation by radial basis functions of a hypersingular integral equation on an open surface. In order to accommodate the homogeneous essential boundary condition along the surface boundary, scaled radial basis functions…

Numerical Analysis · Mathematics 2012-03-14 Norbert Heuer , Thanh Tran
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