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In this paper we introduce a nonlinear version of the notion of Anzellotti's pairing between divergence--measure vector fields and functions of bounded variation, motivated by possible applications to evolutionary quasilinear problems. As a…

Functional Analysis · Mathematics 2019-05-23 Graziano Crasta , Virginia De Cicco

We introduce a family of pairings between a bounded divergence-measure vector field and a function $u$ of bounded variation, depending on the choice of the pointwise representative of $u$. We prove that these pairings inherit from the…

Analysis of PDEs · Mathematics 2019-10-15 Graziano Crasta , Virginia De Cicco , Annalisa Malusa

We analyze some properties of the measures in the dual of the space $BV$, by considering (signed) Radon measures satisfying a perimeter bound condition, which means that the absolute value of the measure of a set is controlled by the…

Analysis of PDEs · Mathematics 2025-01-09 Giovanni E. Comi , Gian Paolo Leonardi

A new notion of pairing between measure vector fields with divergence measure and scalar functions, which are not required to be weakly differentiable, is introduced. In particular, in the case of essentially bounded divergence-measure…

Functional Analysis · Mathematics 2026-02-12 Giovanni Eugenio Comi , Virginia De Cicco , Giovanni Scilla

In this paper we prove that the Anzellotti pairing can be regarded as a relaxed functional with respect to the weak* convergence in the space BV of functions of bounded variation. The crucial tool is a preliminary integral representation of…

Functional Analysis · Mathematics 2024-10-11 Graziano Crasta , Virginia De Cicco

We introduce a family of (nonlinear) pairing measures that ensure the validity of the divergence rule for composite functions $\boldsymbol{B}(x,u(x))$, where $\boldsymbol{B}(\cdot,t)$ is a bounded divergence-measure vector field, and $u$ is…

Functional Analysis · Mathematics 2026-04-16 Graziano Crasta , Virginia De Cicco , Annalisa Malusa

We prove a quantitative mixing estimate for the Cauchy problem for transport along divergence-free vector fields with bounded variation. By developing a framework that quantifies Ambrosio's regularisation scheme, we derive the first…

Analysis of PDEs · Mathematics 2025-04-07 Lucas Huysmans , Ayman Rimah Said

By employing the differential structure recently developed by N. Gigli, we first give a notion of functions of bounded variation ($BV$) in terms of suitable vector fields on a complete and separable metric measure space $(\mathbb{X},d,\mu)$…

Differential Geometry · Mathematics 2021-09-23 Vito Buffa , Giovanni Eugenio Comi , Michele Miranda

Take a set of balls in $\mathbb R^d$. We find a subset of pairwise disjoint balls whose combined perimeter controls the perimeter of the union of the original balls. This can be seen as a boundary version of the Vitali covering lemma. We…

Classical Analysis and ODEs · Mathematics 2025-07-22 Julian Weigt

Markov Chain Monte Carlo methods for sampling from complex distributions and estimating normalization constants often simulate samples from a sequence of intermediate distributions along an annealing path, which bridges between a tractable…

Machine Learning · Computer Science 2024-02-07 Rob Brekelmans , Frank Nielsen

Dielectric properties of material mixtures are of importance in diagnostics, characterization and design of systems in various engineering fields. In this Letter, we propose a peculiar dielectric mixture expression, which is based on the…

Materials Science · Physics 2007-05-23 Enis Tuncer

In this note we study advection diffusion equations associated to incompressible $W^{1,p}$ velocity fields with $p>2$. We present new estimates on the energy dissipation rate and we discuss applications to the study of upper bounds on the…

Analysis of PDEs · Mathematics 2021-03-17 Elia Bruè , Quoc-Hung Nguyen

Following a Maz'ya-type approach, we adapt the theory of rough traces of functions of bounded variation ($BV$) in the context of doubling metric measure spaces supporting a Poincar\'e inequality. This eventually allows for an integration by…

Metric Geometry · Mathematics 2021-07-20 Vito Buffa , Michele Miranda

The molecular motion in heterogeneous media displays anomalous diffusion by the mean-squared displacement $\langle X^2(t) \rangle = 2 D t^\alpha$. Motivated by experiments reporting populations of the anomalous diffusion parameters $\alpha$…

Biological Physics · Physics 2025-10-09 Yann Lanoiselée , Gianni Pagnini , Agnieszka Wyłomańska

We study the rates of convergence of the posterior distribution for Bayesian density estimation with Dirichlet mixtures of normal distributions as the prior. The true density is assumed to be twice continuously differentiable. The bandwidth…

Statistics Theory · Mathematics 2009-09-29 Subhashis Ghosal , Aad van der Vaart

In this paper, we prove that there are certain relations among representation densities and provide an efficient way to compute representation densities by using these relations. As an application, we compute some arithmetic intersection…

Number Theory · Mathematics 2022-08-02 Sungyoon Cho

This article deals with the regularity of the entropy solutions of scalar conservation laws with discontinuous flux. It is well-known [Adimurthi et al., Comm. Pure Appl. Math. 2011] that the entropy solution for such equation does not admit…

Analysis of PDEs · Mathematics 2022-05-23 Shyam Sundar Ghoshal , Stephane Junca , Akash Parmar

The Sutherland approximation to the van der Waals forces is applied to the derivation of a self-consistent Vlasov-type field in a liquid filling a half space, bordering vacuum. The ensuing Vlasov equation is then derived, and solved to…

Soft Condensed Matter · Physics 2015-09-02 V. Molinari , B. D. Ganapol , D. Mostacci

We propose a conjecture on the density of arithmetic points in the deformation space of representations of the \'etale fundamental group in positive characteristic. This? conjecture has applications to \'etale cohomology theory, for example…

Algebraic Geometry · Mathematics 2025-04-16 Hélène Esnault , Moritz Kerz

Diffusion Maps framework is a kernel based method for manifold learning and data analysis that defines diffusion similarities by imposing a Markovian process on the given dataset. Analysis by this process uncovers the intrinsic geometric…

Machine Learning · Statistics 2015-11-20 Moshe Salhov , Amit Bermanis , Guy Wolf , Amir Averbuch
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