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We introduce a variational algorithm to simulate quantum many-body states based on a tree tensor network ansatz which releases the isometry constraint usually imposed by the real-space renormalization coarse-graining: This additional…

Statistical Mechanics · Physics 2014-10-08 Matthias Gerster , Pietro Silvi , Matteo Rizzi , Rosario Fazio , Tommaso Calarco , Simone Montangero

On doubling metric measure spaces endowed with a strongly local regular Dirichlet form, we show some characterisations of pointwise upper bounds of the heat kernel in terms of global scale-invariant inequalities that correspond respectively…

Analysis of PDEs · Mathematics 2013-11-15 Salahaddine Boutayeb , Thierry Coulhon , Adam Sikora

The use of covariance kernels is ubiquitous in the field of spatial statistics. Kernels allow data to be mapped into high-dimensional feature spaces and can thus extend simple linear additive methods to nonlinear methods with higher order…

Machine Learning · Statistics 2017-11-16 Jean-Francois Ton , Seth Flaxman , Dino Sejdinovic , Samir Bhatt

A method is presented for the computation of the one-loop effective action at finite temperature and density. The method is based on an expansion in the number of spatial covariant derivatives. It applies to general background field…

High Energy Physics - Theory · Physics 2016-08-15 C. García-Recio , L. L. Salcedo

This is first of series papers on new two-side Gaussian bounds for the heat kernel $H(x,y,t)$ on a complete manifold $(M,g)$. In this paper, on a complete manifold $M$ with $Ric(M)\geq 0$, we obtain new two-side Gaussian bounds for the heat…

Differential Geometry · Mathematics 2020-01-01 Xiangjin Xu

We investigate the equivalence of relative Faber-Krahn inequalities and the conjunction of Gaussian upper heat kernel bounds and volume doubling on large scales on graphs. For the normalizing measure, we obtain their equivalence up to…

Analysis of PDEs · Mathematics 2025-02-28 Christian Rose

Using the Wilson formulation of lattice gauge theories, a gauge invariant grid discretization of a one-particle Hamiltonian in the presence of an external electromagnetic field is proposed. This Hamiltonian is compared both with that…

Condensed Matter · Physics 2016-08-31 M. Governale , C. Ungarelli

Multivariate histograms are difficult to construct due to the curse of dimensionality. Motivated by $k$-d trees in computer science, we show how to construct an efficient data-adaptive partition of Euclidean space that possesses the…

Methodology · Statistics 2023-08-03 Guenther Walther , Qian Zhao

We prove a uniform generalized gaussian bound for the powers of a discrete convolution operator in one space dimension. Our bound is derived under the assumption that the Fourier transform of the coefficients of the convolution operator is…

Numerical Analysis · Mathematics 2021-11-23 Jean-François Coulombel , Grégory Faye

We present an analytical closed form expression, which gives a good approximate propagator for diffusion on the sphere. Our formula is the spherical counterpart of the Gaussian propagator for diffusion on the plane. While the analytical…

Statistical Mechanics · Physics 2016-10-05 Abhijit Ghosh , Joseph Samuel , Supurna Sinha

In this work we propose and analyze a Hessian-based adaptive sparse quadrature to compute infinite-dimensional integrals with respect to the posterior distribution in the context of Bayesian inverse problems with Gaussian prior. Due to the…

Numerical Analysis · Mathematics 2018-02-14 Peng Chen , Umberto Villa , Omar Ghattas

Constrained diffusions in convex polyhedral domains with a general oblique reflection field, and with a diffusion coefficient scaled by a small parameter, are considered. Using an interior Dirichlet heat kernel lower bound estimate for…

Probability · Mathematics 2013-08-19 Amarjit Budhiraja , Zhen-Qing Chen

Recently developed techniques have made it possible to quickly learn accurate probability density functions from data in low-dimensional continuous space. In particular, mixtures of Gaussians can be fitted to data very quickly using an…

Machine Learning · Computer Science 2013-01-18 Scott Davies , Andrew Moore

We develop two approaches to the problem of soft fragmentation of hadrons in a gauge theory for high energy processes. The first approach directly adapts the standard resummation of the parton distribution function's anomalous dimension…

High Energy Physics - Phenomenology · Physics 2020-07-15 Duff Neill , Felix Ringer

We establish a strong Gaussian approximation for high-dimensional non-degenerate U-statistics with diverging dimension. Under mild assumptions, we construct, on a sufficiently rich probability space, a Gaussian process that uniformly…

Statistics Theory · Mathematics 2026-03-12 Weijia Li , Leheng Cai , Qirui Hu

The exact evolution in time and space of a distribution of the temperature (or density of diffusing matter) in an isotropic homogeneous medium is determined where the initial distribution is described by a piecewise polynomial. In two…

General Physics · Physics 2024-11-26 Mark Andrews

We present a space-time extension of a conservative Cartesian cut-cell finite-volume method for two-phase diffusion problems with prescribed interface motion. The formulation follows a two-fluid approach: one scalar field is solved in each…

Computational Physics · Physics 2026-01-01 Louis Libat , Can Selçuk , Eric Chénier , Vincent Le Chenadec

We present a fully adaptive multiresolution scheme for spatially one-dimensional quasilinear strongly degenerate parabolic equations with zero-flux and periodic boundary conditions. The numerical scheme is based on a finite volume…

Numerical Analysis · Mathematics 2012-06-22 Raimund Bürger , Ricardo Ruiz Baier , Mauricio Sepúlveda , Kai Schneider

We present a Cartesian cut-cell finite-volume method for sharp-interface two-phase diffusion problems in static geometries. The formulation follows a two-fluid approach: independent diffusion equations are discretized in each phase on a…

Numerical Analysis · Mathematics 2026-01-07 Louis Libat , Can Selçuk , Eric Chénier , Vincent Le Chenadec

Optimal transport with quadratic cost provides a geometric framework for steering an ensemble, modeled by a probability law, with minimal effort. Yet ambient-space formulations become unwieldy in high dimensions, and sensing or actuation in…

Optimization and Control · Mathematics 2026-04-28 Kaito Ito , Anqi Dong
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