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We consider the problem of developing interpretable and computationally efficient matrix decomposition methods for matrices whose entries have bounded support. Such matrices are found in large-scale DNA methylation studies and many other…

Machine Learning · Computer Science 2024-10-25 Anjali N. Albert , Patrick Flaherty , Aaron Schein

Noise fundamentally limits the performance and predictive capabilities of classical and quantum dynamical systems by degrading stability and obscuring intrinsic dynamical characteristics. Characterizing such noise accurately is essential…

Quantum Physics · Physics 2025-08-07 Adva Baratz , Loris Maria Cangemi , Assaf Hamo , Sivan Refaely-Abramson , Amikam Levy

Entropy is useful in statistical problems as a measure of irreversibility, randomness, mixing, dispersion, and number of microstates. However, there remains ambiguity over the precise mathematical formulation of entropy, generalized beyond…

Statistical Mechanics · Physics 2023-08-21 Vladimir Zhdankin

Principal component analysis (PCA) is widely used for feature extraction and dimensionality reduction, with documented merits in diverse tasks involving high-dimensional data. Standard PCA copes with one dataset at a time, but it is…

Machine Learning · Computer Science 2019-01-30 Jia Chen , Gang Wang , Georgios B. Giannakis

Non-intrusive methods have been used since two decades to derive reduced-order models for geometrically nonlinear structures, with a particular emphasis on the so-called STiffness Evaluation Procedure (STEP), relying on the static…

Computational Engineering, Finance, and Science · Computer Science 2022-05-26 Alessandra Vizzaccaro , Arthur Givois , Pierluigi Longobardi , Yichang Shen , Jean-François Deü , Loïc Salles , Cyril Touzé , Olivier Thomas

A new variational method for studying the equilibrium states of an interacting particles system has been proposed. The statistical description of the system is realized by means of a density matrix. This method is used for description of…

General Physics · Physics 2014-12-19 Boris Bondarev

We introduce a new overlapping Domain Decomposition Method (DDM) to solve the fully nonlinear Monge-Amp\`ere equation. While DDMs have been extensively studied for linear problems, their application to fully nonlinear partial differential…

Numerical Analysis · Mathematics 2023-06-05 Yassine Boubendir , Jake Brusca , Brittany Froese Hamfeldt , Tadanaga Takahashi

Unsupervised machine learning lacks ground truth by definition. This poses a major difficulty when designing metrics to evaluate the performance of such algorithms. In sharp contrast with supervised learning, for which plenty of quality…

Machine Learning · Computer Science 2023-03-20 Raúl Lara-Cabrera , Ángel González-Prieto , Diego Pérez-López , Diego Trujillo , Fernando Ortega

A two-dimensional inviscid and diffusive Oldroyd-B model was investigated by [T. M. Elgindi, F. Rousset, Commun. Pure Appl. Math. 68 (2015), 2005--2021] where the global existence and uniqueness of the strong solution were established for…

Analysis of PDEs · Mathematics 2022-12-05 Yuanzhi Tu , Yinghui Wang , Huanyao Wen

Nonlinear contraction theory is a comparatively recent dynamic control system design tool based on an exact differential analysis of convergence, in essence converting a nonlinear stability problem into a linear time-varying stability…

Pattern Formation and Solitons · Physics 2007-05-23 Winfried Lohmiller , Jean-Jacques E. Slotine

We propose a "decomposition method" to prove non-asymptotic bound for the convergence of empirical measures in various dual norms. The main point is to show that if one measures convergence in duality with sufficiently regular observables,…

Probability · Mathematics 2018-02-13 Benoît Kloeckner

Partial differential equations (PDEs) that fit scientific data can represent physical laws with explainable mechanisms for various mathematically-oriented subjects, such as physics and finance. The data-driven discovery of PDEs from…

Machine Learning · Computer Science 2023-05-29 Yingtao Luo , Qiang Liu , Yuntian Chen , Wenbo Hu , Tian Tian , Jun Zhu

Composite materials/structures are advancing in product efficiency, cost-effectiveness and the development of superior specific properties. There are increasing demands in their applications to load-carrying structures in aerospace, wind…

Applied Physics · Physics 2020-03-05 Bing Wang , Shuncong Zhong , Tung-Lik Lee , Kevin S Fancey , Jiawei Mi

We present a technique to perform dimensionality reduction on data that is subject to uncertainty. Our method is a generalization of traditional principal component analysis (PCA) to multivariate probability distributions. In comparison to…

Machine Learning · Computer Science 2019-10-14 Jochen Görtler , Thilo Spinner , Dirk Streeb , Daniel Weiskopf , Oliver Deussen

Extensive research papers of three-dimensional computational techniques are widely used for the investigation of human brain pathophysiology. Eddy current analyzing could provide an indication of conductivity change within a biological…

Medical Physics · Physics 2019-06-05 Wuliang Yin , Mingyang Lu , Jiawei Tang , Qian Zhao , Zhijie Zhang , Kai Li , Yan Han , Anthony Peyton

We propose a two-sample test for high-dimensional means that requires neither distributional nor correlational assumptions, besides some weak conditions on the moments and tail properties of the elements in the random vectors. This…

Methodology · Statistics 2019-04-17 Kaijie Xue , Fang Yao

Experimental design is a classical statistics problem and its aim is to estimate an unknown $m$-dimensional vector $\beta$ from linear measurements where a Gaussian noise is introduced in each measurement. For the combinatorial experimental…

Machine Learning · Statistics 2024-12-06 Mohit Singh , Weijun Xie

Future complementary metal oxide semiconductor (CMOS) scaling for advanced integrated circuit (IC) technologies may well depend on "More than Moore" (MtM) approaches using heterogeneous integration of semiconductor-based devices. In order…

Materials Science · Physics 2012-04-09 J. Stopford , A. Henry , D. Allen , N. Bennett , D. Manessis , L. Boettcher , J. Wittge , A. N. Danilewsky , P. J. McNally

A class of energy-transport equations without electric field under mixed Dirichlet-Neumann boundary conditions is analyzed. The system of degenerate and strongly coupled parabolic equations for the particle density and temperature arises in…

Analysis of PDEs · Mathematics 2013-10-15 Nicola Zamponi , Ansgar Jüngel

Accurate and efficient plasma models are essential to understand and control experimental devices. Existing magnetohydrodynamic or kinetic models are nonlinear, computationally intensive, and can be difficult to interpret, while often only…

Plasma Physics · Physics 2020-03-04 Alan A. Kaptanoglu , Kyle D. Morgan , Chris J. Hansen , Steven L. Brunton
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