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Related papers: A topological approach to the Cahn-Hilliard equati…

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We study the asymptotic limit of the Cahn-Hilliard equation on an evolving surface with prescribed velocity. The method of formally matched asymptotic expansions is extended to account for the movement of the domain. We consider various…

Analysis of PDEs · Mathematics 2016-07-20 David O'Connor , Bjorn Stinner

The Cahn--Hilliard equation is a widely used model that describes amongst others phase separation processes of binary mixtures or two-phase flows. In the recent years, different types of boundary conditions for the Cahn--Hilliard equation…

Numerical Analysis · Mathematics 2021-03-04 Stefan Metzger

We propose a simple nonlocal energy functional that is suitable for the continuum field characterization of nonperiodic and localized textures. The phenomenological functional is based on the pairwise direction-dependent interaction of…

Soft Condensed Matter · Physics 2012-02-06 Andreas M. Menzel

Inspired by topological data analysis techniques, we introduce persistent homology observables and apply them in a geometric analysis of the dynamics of quantum field theories. As a prototype application, we consider data from a…

Quantum Gases · Physics 2021-09-23 Daniel Spitz , Jürgen Berges , Markus K. Oberthaler , Anna Wienhard

We provide numerical constructions of one-dimensional hyperuniform many-particle distributions that exhibit unusual clustering and asymptotic local number density fluctuations growing more slowly than the volume of an observation window but…

Statistical Mechanics · Physics 2013-05-29 Chase E. Zachary , Salvatore Torquato

We find analytical solutions to the Cahn-Hilliard equation for the dynamics of an interface in a system with a conserved order parameter (Model B). We show that, although steady-state solutions of Model B are unphysical in the far-field,…

Soft Condensed Matter · Physics 2009-10-30 R. M. L. Evans , M. E. Cates

Hyperuniformity, whereby the static structure factor (or density correlator) obeys $S(q)\sim q^{\varsigma}$ with $\varsigma> 0$, emerges at criticality in systems having multiple absorbing states, such as periodically sheared suspensions.…

Statistical Mechanics · Physics 2023-10-27 Xiao Ma , Johannes Pausch , Michael E. Cates

We address the problem of estimating topological features from data in high dimensional Euclidean spaces under the manifold assumption. Our approach is based on the computation of persistent homology of the space of data points endowed with…

Machine Learning · Statistics 2023-01-23 Ximena Fernández , Eugenio Borghini , Gabriel Mindlin , Pablo Groisman

We study the two-dimensional snake-like pattern that arises in phase separation of alloys described by spinodal decomposition in the Cahn-Hilliard model. These are somewhat universal patterns due to an overlay of eigenfunctions of the…

Probability · Mathematics 2017-11-20 Luigi Amedeo Bianchi , Dirk Blömker , Philipp Wacker

In this paper we give a metric construction of a tree which correctly identifies connected components of superlevel sets of $\mathbb{R}$-valued continuous functions $f$ on $X$ and show that it is possible to retrieve the $H_0$-persistent…

Algebraic Topology · Mathematics 2022-04-11 Daniel Perez

Statistical Topology emerged since topological aspects continue to gain importance in many areas of physics. It is most desirable to study topological invariants and their statistics in schematic models that facilitate the identification of…

Mathematical Physics · Physics 2023-03-22 Thomas Guhr

The manifold hypothesis, which assumes that data lies on or close to an unknown manifold of low intrinsic dimension, is a staple of modern machine learning research. However, recent work has shown that real-world data exhibits distinct…

Machine Learning · Computer Science 2023-06-16 Julius von Rohrscheidt , Bastian Rieck

The topological patterns exhibited by many real-world networks motivate the development of topology-based methods for assessing the similarity of networks. However, extracting topological structure is difficult, especially for large and…

Machine Learning · Computer Science 2022-03-15 Tananun Songdechakraiwut , Bryan M. Krause , Matthew I. Banks , Kirill V. Nourski , Barry D. Van Veen

The existence of characteristic structure, or shape, in complex data sets has been recognized as increasingly important for mathematical data analysis. This realization has motivated the development of new tools such as persistent homology…

Computer Vision and Pattern Recognition · Computer Science 2016-07-12 Sofya Chepushtanova , Michael Kirby , Chris Peterson , Lori Ziegelmeier

Persistent homology is a topological data analysis tool that has been widely generalized, extending its scope beyond the field of topology. Among its extensions, steady and ranging persistence were developed to study a wide variety of graph…

Algebraic Topology · Mathematics 2026-05-15 Yann-Situ Gazull

Persistent Homology (PH) is a fundamental tool in computational topology, designed to uncover the intrinsic geometric and topological features of data across multiple scales. Originating within the broader framework of Topological Data…

Algebraic Topology · Mathematics 2025-05-13 Aurelie Jodelle Kemme , Collins Amburo Agyingi

Complex networks encountered in biology are often characterized by significant structural diversity. Whether it be differences in the three-dimensional structure of allosteric proteins, or the variation among the micro-scale structures of…

Physics and Society · Physics 2020-08-19 Jason W. Rocks , Andrea J. Liu , Eleni Katifori

Consistent couplings among a set of scalar fields, two types of one-forms and a system of two-forms are investigated in the light of the Hamiltonian BRST cohomology, giving a four-dimensional nonlinear gauge theory. The emerging…

High Energy Physics - Theory · Physics 2009-11-07 C. Bizdadea , E. M. Cioroianu , S. O. Saliu

We present theoretical and experimental results probing the rich topological structure of arbitrarily disordered finite tight binding Hamiltonians with chiral symmetry. We extend the known classification by considering the topological…

Mesoscale and Nanoscale Physics · Physics 2025-05-19 Maxine M. McCarthy , D. M. Whittaker

We extend the notion of the distance to a measure from Euclidean space to probability measures on general metric spaces as a way to do topological data analysis in a way that is robust to noise and outliers. We then give an efficient way to…

Computational Geometry · Computer Science 2014-10-09 Mickael Buchet , Frederic Chazal , Steve Y. Oudot , Donald R. Sheehy