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Green's function plays a significant role in both theoretical analysis and numerical computing of partial differential equations (PDEs). However, in most cases, Green's function is difficult to compute. The troubles arise in the following…

Machine Learning · Computer Science 2022-04-29 Guochang Lin , Fukai Chen , Pipi Hu , Xiang Chen , Junqing Chen , Jun Wang , Zuoqiang Shi

Many boundary element integral equation kernels are based on the Green's functions of the Laplace and Helmholtz equations in three dimensions. These include, for example, the Laplace, Helmholtz, elasticity, Stokes, and Maxwell's equations.…

Computational Physics · Physics 2016-08-24 Ross Adelman , Nail A. Gumerov , Ramani Duraiswami

We discuss conformally covariant differential operators, which under local rescalings of the metric, \delta_\sigma g^{\mu\nu} = 2 \sigma g^{\mu\nu}, transform according to \delta_\sigma \Delta = r \Delta \sigma + (s-r) \sigma \Delta for…

High Energy Physics - Theory · Physics 2009-10-30 J. Erdmenger

It is often said that control and estimation problems are in duality. Recently, in (Aubin-Frankowski,2021), we found new reproducing kernels in Linear-Quadratic optimal control by focusing on the Hilbert space of controlled trajectories,…

Optimization and Control · Mathematics 2022-10-14 Pierre-Cyril Aubin-Frankowski , Alain Bensoussan

In this paper, we analyze Gaussian processes using statistical mechanics. Although the input is originally multidimensional, we simplify our model by considering the input as one-dimensional for statistical mechanical analysis. Furthermore,…

Statistical Mechanics · Physics 2025-05-05 Jun Tsuzurugi

Problems involving boundary conditions on corrugated surfaces are relevant to understand nature, since, at some scale, surfaces manifest corrugations that have to be taken into account. In introductory level electromagnetism courses, a very…

Physics Education · Physics 2022-05-23 Alexandre P. da Costa , Lucas Queiroz , Edson C. M. Nogueira , Danilo T. Alves

In the present survey we present some of the recent results concerning the geometry of nodal lines of random Gaussian eigenfunctions (in case of spectral degeneracies) or wavepackets and related issues. The most fundamental example, where…

Mathematical Physics · Physics 2011-03-02 Igor Wigman

The paper covers the design and analysis of experiments to discriminate between two Gaussian process models, such as those widely used in computer experiments, kriging, sensor location and machine learning. Two frameworks are considered.…

Methodology · Statistics 2022-11-22 Elham Yousefi , Luc Pronzato , Markus Hainy , Werner G. Müller , Henry P. Wynn

A classical problem of free-space Green's function $G_{0\Lambda}$ representations of the Helmholtz equation is studied in various quasi-periodic cases, i.e., when an underlying periodicity is imposed in less dimensions than is the dimension…

Mathematical Physics · Physics 2007-05-23 Alexander Moroz

Several classic problems for particles diffusing outside an arbitrary configuration of non-overlapping partially reactive spherical traps in three dimensions are revisited. For this purpose, we describe the generalized method of separation…

Computational Physics · Physics 2021-10-14 Denis S. Grebenkov

The smoothing spline is one of the most popular curve-fitting methods, partly because of empirical evidence supporting its effectiveness and partly because of its elegant mathematical formulation. However, there are two obstacles that…

Statistics Theory · Mathematics 2012-09-11 Yu Ryan Yue , Daniel Simpson , Finn Lindgren , Håvard Rue

We construct an expression for the Green function of a differential operator satisfying nonlocal, homogeneous boundary conditions starting from the fundamental solution of the differential operator. This also provides the solution to the…

Analysis of PDEs · Mathematics 2020-05-22 Vanik E. Mkrtchian , Carsten Henkel

Gaussian processes are an effective model class for learning unknown functions, particularly in settings where accurately representing predictive uncertainty is of key importance. Motivated by applications in the physical sciences, the…

Machine Learning · Statistics 2023-04-19 Viacheslav Borovitskiy , Alexander Terenin , Peter Mostowsky , Marc Peter Deisenroth

To target challenges in differentiable optimization we analyze and propose strategies for derivatives of the Mat\'ern kernel with respect to the smoothness parameter. This problem is of high interest in Gaussian processes modelling due to…

Numerical Analysis · Mathematics 2022-01-04 Oana Marin , Christopher Geoga , Michel Schanen

We consider a modification of the covariance function in Gaussian processes to correctly account for known linear constraints. By modelling the target function as a transformation of an underlying function, the constraints are explicitly…

Machine Learning · Statistics 2017-09-20 Carl Jidling , Niklas Wahlström , Adrian Wills , Thomas B. Schön

The use of Gaussian process models is typically limited to datasets with a few tens of thousands of observations due to their complexity and memory footprint. The two most commonly used methods to overcome this limitation are 1) the…

Machine Learning · Statistics 2020-01-16 Vincent Adam , Stefanos Eleftheriadis , Nicolas Durrande , Artem Artemev , James Hensman

Gaussian processes provide probabilistic surrogates for various applications including classification, uncertainty quantification, and optimization. Using a gradient-enhanced covariance matrix can be beneficial since it provides a more…

Optimization and Control · Mathematics 2023-07-13 André L. Marchildon , David W. Zingg

We develop a new structure of the Green's function of a second-order elliptic operator in divergence form in a 2D bounded domain. Based on this structure and the theory of rearrangement of functions, we construct concentrated…

Analysis of PDEs · Mathematics 2022-09-27 Daomin Cao , Jie Wan

We study the one-dimensional Laplace operator with point interactions on the real line identified with two copies of the half-line $[0,\infty)$. All possible boundary conditions that define generators of $C_0$-semigroups on…

Functional Analysis · Mathematics 2020-12-14 Amru Hussein , Delio Mugnolo

Differential equations are important mechanistic models that are integral to many scientific and engineering applications. With the abundance of available data there has been a growing interest in data-driven physics-informed models.…

Machine Learning · Computer Science 2025-02-04 Oliver Hamelijnck , Arno Solin , Theodoros Damoulas
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