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Analysis of high-dimensional data is currently a popular field of research, thanks to many applications e.g. in genetics (DNA data in genomewide association studies), spectrometry or web analysis. At the same time, the type of problems that…

Methodology · Statistics 2018-05-25 Jozef Jakubik

Gradient-based iterative optimization methods are the workhorse of modern machine learning. They crucially rely on careful tuning of parameters like learning rate and momentum. However, one typically sets them using heuristic approaches…

Machine Learning · Computer Science 2025-12-05 Dravyansh Sharma

Quantum confinement endows two-dimensional (2D) layered materials with exceptional physics and novel properties compared to their bulk counterparts. Although certain two- and few-layer configurations of graphene have been realized and…

In the spirit of Germain the most general objective stored elastic energy for a second gradient material is deduced using a literature result of Fortun\'e & Vall\'ee. Linear isotropic constitutive relations for stress and hyperstress in…

Mathematical Physics · Physics 2010-08-18 F. dell'Isola , G. Sciarra , S. Vidoli

Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristics of the data. Recently, the Gaussian Process Latent…

Machine Learning · Statistics 2010-07-14 Hannes Nickisch , Carl Edward Rasmussen

We introduce models for viscoelastic materials, both solids and fluids, based on logarithmic stresses to capture the elastic contribution to the material response. The matrix logarithm allows to link the measures of strain, that naturally…

Analysis of PDEs · Mathematics 2024-10-10 Gennaro Ciampa , Giulio G. Giusteri , Alessio G. Soggiu

This paper is concerned with sampling from probability distributions $\pi$ on $\mathbb{R}^d$ admitting a density of the form $\pi(x) \propto e^{-U(x)}$, where $U(x)=F(x)+G(Kx)$ with $K$ being a linear operator and $G$ being…

Optimization and Control · Mathematics 2024-05-28 Andreas Habring , Martin Holler , Thomas Pock

We investigate the connections between max-weight approaches and dual subgradient methods for convex optimisation. We find that strong connections exist and we establish a clean, unifying theoretical framework that includes both max-weight…

Optimization and Control · Mathematics 2015-08-27 Víctor Valls , Douglas J. Leith

Successful modern generalized gradient approximations (GGA's) are biased toward atomic energies. Restoration of the first-principles gradient expansion for exchange over a wide range of density gradients eliminates this bias. We introduce…

Various spatial-gradient extensions of standard viscoelastic rheologies of the Kelvin-Voigt, Maxwell's, and Jeffreys' types are analyzed in linear one-dimensional situations as far as the propagation of waves and their dispersion and…

Fluid Dynamics · Physics 2024-01-02 Tomáš Roubíček

We study compressible and incompressible nonlinear elasticity variational problems in a general context. Our main result gives a sufficient condition for an equilibrium to be a global energy minimizer, in terms of convexity properties of…

Analysis of PDEs · Mathematics 2020-11-04 Nassif Ghoussoub , Young-Heon Kim , Hugo Lavenant , Aaron Zeff Palmer

Bilayer plates are compound materials that exhibit large bending deformations when exposed to environmental changes that lead to different mechanical responses in the involved materials. In this article a new numerical method which is…

Numerical Analysis · Mathematics 2020-04-02 Sören Bartels , Christian Palus

Cardiac muscle tissue during relaxation is commonly modelled as a hyperelastic material with strongly nonlinear and anisotropic stress response. Adapting the behavior of such a model to experimental or patient data gives rise to a parameter…

Tissues and Organs · Quantitative Biology 2016-03-16 Gabriel Balaban , Martin S. Alnæs , Joakim Sundnes , Marie E. Rognes

A model of saturated hyperelastic porous solids at large strains is formulated and analysed. The material response is assumed to be of a viscoelastic Kelvin-Voigt type and inertial effects are considered, too. The flow of the diffusant is…

Analysis of PDEs · Mathematics 2022-10-13 Tomas Roubicek , Ulisse Stefanelli

We consider mechanics of composite materials in which thin inclusions are modeled by lower-dimensional manifolds. By successively applying the dimensional reduction to junctions and intersections within the material, a geometry of…

Numerical Analysis · Mathematics 2019-03-06 Wietse M. Boon , Jan M. Nordbotten

This article develops duality principles applicable to the non-linear Kirchhoff-Love model of plates. The results are obtained through standard tools of convex analysis, functional analysis, calculus of variations and duality theory. The…

Optimization and Control · Mathematics 2021-09-07 Fabio Silva Botelho

In these lecture notes we present different methods and concepts developed in statistical physics to analyze gradient descent dynamics in high-dimensional non-convex landscapes. Our aim is to show how approaches developed in physics, mainly…

Disordered Systems and Neural Networks · Physics 2023-11-13 Tony Bonnaire , Davide Ghio , Kamesh Krishnamurthy , Francesca Mignacco , Atsushi Yamamura , Giulio Biroli

We investigate the emergence of non-linear diffusivity in kinetically constrained, one-dimensional symmetric exclusion processes satisfying the gradient condition. Recent developments introduced new gradient dynamics based on the Bernstein…

Probability · Mathematics 2025-04-18 G. S. Nahum

We derive geometrically linearized theories for incompressible materials from nonlinear elasticity theory in the small displacement regime. Our nonlinear stored energy densities may vary on the same (small) length scale as the typical…

Analysis of PDEs · Mathematics 2020-04-24 Martin Jesenko , Bernd Schmidt

We investigate the geometry of polycrystals, showing that for polycrystals formed of convex grains the interior grains are polyhedral, while for polycrystals with general grain geometry the set of triple points is small. Then we investigate…

Materials Science · Physics 2015-10-13 John M. Ball , Carsten Carstensen