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While there is substantial need for dependence models in higher dimensions, most existing models quickly become rather restrictive and barely balance parsimony and flexibility. Hierarchical constructions may improve on that by grouping…

Methodology · Statistics 2013-10-11 Eike Christian Brechmann

Random coupled parabolic partial differential models are solved numerically using random cosine Fourier transform together with non Gaussian random numerical integration that capture the highly oscillatory behavior of the involved…

Numerical Analysis · Mathematics 2025-01-28 M. -C. Casabán , R. Company , V. N. Egorova , L. Jódar

We describe the implementation of a new module which can be used to simulate physical systems in which the motion of particles is affected by stochastic forces. Such forces are expected to be present in turbulent circumstellar disks or…

Earth and Planetary Astrophysics · Physics 2022-05-16 Hanno Rein , Nick Choksi

Several problems in magnetically confined fusion, such as the computation of exterior vacuum fields or the decomposition of the total magnetic field into separate contributions from the plasma and the external sources, are best formulated…

Numerical Analysis · Mathematics 2019-11-25 Dhairya Malhotra , Antoine J. Cerfon , Michael O'Neil , Evan Toler

Additive regression models have a long history in multivariate nonparametric regression. They provide a model in which each regression function depends only on a single explanatory variable allowing to obtain estimators at the optimal…

Methodology · Statistics 2015-09-16 Graciela Boente , Alejandra Martinez

An algorithm is proposed that enables the imposition of shape constraints on regression curves, without requiring the constraints to be written as closed-form expressions, nor assuming the functional form of the loss function. This…

Methodology · Statistics 2019-04-08 Kenyon Ng , Berwin A. Turlach , Kevin Murray

We derive an analytic expression for the scalar one-loop pentagon and hexagon functions which is convenient for subsequent numerical integration. These functions are of relevance in the computation of next-to-leading order radiative…

High Energy Physics - Phenomenology · Physics 2013-12-02 T. Binoth , G. Heinrich , N. Kauer

Challenges in multi-fidelity modeling relate to accuracy, uncertainty estimation and high-dimensionality. A novel additive structure is introduced in which the highest fidelity solution is written as a sum of the lowest fidelity solution…

Machine Learning · Computer Science 2021-04-09 Wei W. Xing , Akeel A. Shah , Peng Wang , Shandian Zhe Qian Fu , Robert. M. Kirby

Recently, we have proposed a new diffusive representation for fractional derivatives and, based on this representation, suggested an algorithm for their numerical computation. From the construction of the algorithm, it is immediately…

Numerical Analysis · Mathematics 2022-04-12 Kai Diethelm

Computational methods for fractional differential equations exhibit essential instability. Even a minor modification of the coefficients or other entry data may switch good results to the divergent. The goal of this paper is to suggest the…

Numerical Analysis · Mathematics 2021-12-20 P. B. Dubovski , J. A. Slepoi

Colloids have a striking relevance in a wide spectrum of industrial formulations, spanning from personal care products to protective paints. Their behaviour can be easily influenced by extremely weak forces, which disturb their…

Soft Condensed Matter · Physics 2018-06-14 Daniel Corbett , Alejandro Cuetos , Matthew Dennison , Alessandro Patti

We performed a benchmark study on a series of dihydrogen bond complexes and constructed a set of reference bond distances and interaction energies. The test set was employed to assess the performance of several wave-function correlated and…

Chemical Physics · Physics 2015-01-22 E. Fabiano , L. A. Constantin , F. Della Sala

The problem of calculation of correlation functions in the six-vertex model with domain wall boundary conditions is addressed by considering a particular nonlocal correlation function, called row configuration probability. This correlation…

Mathematical Physics · Physics 2012-07-20 F. Colomo , A. G. Pronko

Multibody dynamics simulators are an important tool in many fields, including learning and control for robotics. However, many existing dynamics simulators suffer from inaccuracies when dealing with constrained mechanical systems due to…

Robotics · Computer Science 2023-11-07 Jan Brüdigam , Stefan Sosnowski , Zachary Manchester , Sandra Hirche

A classical density functional theory is applied to study solvation of solutes in water. An approx- imate form of the excess functional is proposed for water. This functional requires the knowledge of pure solvent direct correlation…

Chemical Physics · Physics 2014-09-01 Guillaume Jeanmairet

If we wish to integrate a function $h|\Omega\subset\Re^{n}\to\Re$ along a single $T$-level surface of a function $\psi |\Omega\subset\Re^{n}\to\Re$, then a number of different methods for extracting finite elements appropriate to the…

Numerical Analysis · Mathematics 2021-04-01 Navdeep Dahiya , Martin Mueller , Anthony Yezzi

We put forward a functional renormalisation group approach for the direct computation of real time correlation functions, also applicable at finite temperature and density. We construct a general class of regulators that preserve the…

High Energy Physics - Phenomenology · Physics 2017-07-07 Jan M. Pawlowski , Nils Strodthoff

Critical points of energy functionals, which are of broad interest, for instance, in physics and chemistry, in solid and quantum mechanics, in material science, or in general diffusion-reaction models arise as solutions to the associated…

Numerical Analysis · Mathematics 2025-10-20 Pascal Heid , Thomas P. Wihler

The conjugate function method is an algorithm for numerical computation of conformal mappings for simply and multiply connected domains. In this paper, the conjugate function method is extended to cover conformal mappings between Riemannian…

Numerical Analysis · Mathematics 2024-04-22 Harri Hakula , Antti Rasila

Causal models bring many benefits to decision-making systems (or agents) by making them interpretable, sample-efficient, and robust to changes in the input distribution. However, spurious correlations can lead to wrong causal models and…

Machine Learning · Computer Science 2020-12-09 Sergei Volodin , Nevan Wichers , Jeremy Nixon