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Surface metrology is the area of engineering concerned with the study of geometric variation in surfaces. This paper explores the potential for modern techniques from spatial statistics to act as generative models for geometric variation in…

Applications · Statistics 2022-10-04 Chris. J. Oates , Wilfrid S. Kendall , Liam Fleming

This paper develops fundamental mathematical theory for second-order Filippov systems. These are discontinuous ordinary differential equations with solutions defined in the sense of Filippov, and whose first Lie derivatives vary…

Dynamical Systems · Mathematics 2026-03-12 D. J. W. Simpson

The classical problem of indentation on an elastic substrate has found new applications in the field of the Atomic Force Microscopy. However, linearly elastic indentation models are not sufficiently accurate to predict the…

Soft Condensed Matter · Physics 2022-12-14 Yangkun Du , Peter Stewart , Nicholas A Hill , Huabing Yin , Raimondo Penta , Jakub Kory , Xiaoyu Luo , Raymond Ogden

We prove metric rigidity for complete manifolds supporting solutions of certain second order differential systems, thus extending classical works on a characterization of space-forms. In the route, we also discover new characterizations of…

Differential Geometry · Mathematics 2009-03-06 Stefano Pigola , Michele Rimoldi

Indentation tests are classical tools to determine material properties. For biological samples such as cysts of cells, however, the observed force-displacement relation cannot be expected to follow predictions for simple materials. Here, by…

Soft Condensed Matter · Physics 2025-12-12 Shiheng Zhao , Pierre A. Haas

Stochastic interpolants offer a robust framework for continuously transforming samples between arbitrary data distributions, holding significant promise for generative modeling. Despite their potential, rigorous finite-time convergence…

Machine Learning · Computer Science 2025-08-12 Yuhao Liu , Rui Hu , Yu Chen , Longbo Huang

In this paper two new classes of stationary random simplicial tessellations, the so-called $\beta$- and $\beta'$-Delaunay tessellations, are introduced. Their construction is based on a space-time paraboloid hull process and generalizes…

Probability · Mathematics 2021-11-19 Anna Gusakova , Zakhar Kabluchko , Christoph Thäle

Stochastic contraction analysis is a recently developed tool for studying the global stability properties of nonlinear stochastic systems, based on a differential analysis of convergence in an appropriate metric. To date, stochastic…

Optimization and Control · Mathematics 2013-04-02 Quang-Cuong Pham , Jean-Jacques Slotine

We present a novel space-time isogeometric discretization of the acoustic wave equation in second-order formulation that is intrinsically unconditionally stable. The method relies on a variational framework inspired by [Walkington 2014],…

Numerical Analysis · Mathematics 2025-06-19 Matteo Ferrari , Ilaria Perugia

We consider the first-hitting time of a tempered $\beta$-stable subordinator, also called inverse tempered stable (ITS) subordinator. The density function of the ITS subordinator is obtained, for the index of stability $\beta \in (0,1)$.…

Probability · Mathematics 2014-10-08 A. Kumar , P. Vellaisamy

A square-tiled surface (STS) is a (finite, possibly branched) cover of the standard square-torus with possible branching over exactly 1 point. Alternately, STSs can be viewed as finitely many axis-parallel squares with sides glued in…

Geometric Topology · Mathematics 2026-02-12 Nick Fitzhugh , Aaron Schondorf , Sunrose Shrestha , Sebastian Vander Ploeg Fallon , Thomas Zeng

We consider a stationary face-to-face tessellation $X$ of $\mathbb{R}^d$ and introduce several percolation models by colouring some of the faces black in a consistent way. Our main model is cell percolation, where cells are declared black…

Probability · Mathematics 2013-12-24 Günter Last , Eva Ochsenreither

First-order stochastic methods are the state-of-the-art in large-scale machine learning optimization owing to efficient per-iteration complexity. Second-order methods, while able to provide faster convergence, have been much less explored…

Machine Learning · Statistics 2017-12-01 Naman Agarwal , Brian Bullins , Elad Hazan

Most results on Stochastic Gradient Descent (SGD) in the convex and smooth setting are presented under the form of bounds on the ergodic function value gap. It is an open question whether bounds can be derived directly on the last iterate…

Optimization and Control · Mathematics 2025-07-21 Guillaume Garrigos , Daniel Cortild , Lucas Ketels , Juan Peypouquet

Hartle's model provides the most widely used analytic framework to describe isolated compact bodies rotating slowly in equilibrium up to second order in perturbations in the context of General Relativity. Apart from some explicit…

General Relativity and Quantum Cosmology · Physics 2015-05-20 Borja Reina , Raül Vera

This paper is the first part in a 2 part study of an elementary functorial construction from the category of finite non-abelian groups to a category of singular compact, oriented 2-manifolds. After a desingularization process this…

Geometric Topology · Mathematics 2013-10-16 Mark Herman , Jonathan Pakianathan , Ergun Yalcin

We introduce and consider the notion of stable degeneracies of translation invariant energy functions for finite Ising models. By this term we mean the lack of injectivity that cannot be lifted by changing the interaction. We show that…

Mathematical Physics · Physics 2016-03-23 Andreas Knauf

The STIT tessellation process was introduced and examined by Mecke, Nagel and Wei{\ss}; many of its main characteristics are contained in a paper published by Nagel and Wei{\ss} in 2005. In a paper published in 2010, Mecke introduced…

Probability · Mathematics 2011-06-02 Eike Biehler

The 2-matrix model has been introduced to study Ising model on random surfaces. Since then, the link between matrix models and combinatorics of discrete surfaces has strongly tightened. This manuscript aims to investigate these deep links…

High Energy Physics - Theory · Physics 2007-09-20 N. Orantin

Our work is part of the close link between continuous-time dissipative dynamical systems and optimization algorithms, and more precisely here, in the stochastic setting. We aim to study stochastic convex minimization problems through the…

Optimization and Control · Mathematics 2025-02-21 Rodrigo Maulen-Soto , Jalal Fadili , Hedy Attouch , Peter Ochs