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We provide estimates for the convolution product of an arbitrary number of "resurgent functions", that is holomorphic germs at the origin of $C$ that admit analytic continuation outside a closed discrete subset of $C$ which is stable under…

Dynamical Systems · Mathematics 2014-04-22 David Sauzin

The Frequency Response Functions (FRFs) are the most widely used functions to characterise the dynamic behaviour of structures. The natural frequencies and damping behaviour can be easily and quickly detected from a Bode diagram. The modal…

Classical Physics · Physics 2024-04-09 Dario Di Maio

We provide adaptive inference methods, based on $\ell_1$ regularization, for regular (semi-parametric) and non-regular (nonparametric) linear functionals of the conditional expectation function. Examples of regular functionals include…

Machine Learning · Statistics 2022-10-25 Victor Chernozhukov , Whitney Newey , Rahul Singh

We introduce and study a mathematical framework for a broad class of regularization functionals for ill-posed inverse problems: Regularization Graphs. Regularization graphs allow to construct functionals using as building blocks linear…

Optimization and Control · Mathematics 2022-09-28 Kristian Bredies , Marcello Carioni , Martin Holler

Interpolation inequalities play an essential role in Analysis with fundamental consequences in Mathematical Physics, Nonlinear Partial Differential Equations (PDEs), Markov Processes, etc., and have a wide range of applications in various…

Analysis of PDEs · Mathematics 2021-10-19 Jean Dolbeault

Complex systems may morph between structures with different dimensionality and degrees of freedom. As a tool for their modelling, nonlinear embeddings are introduced that encompass objects with different dimensionality as a continuous…

Cellular Automata and Lattice Gases · Physics 2020-07-07 Vladimir García-Morales

We prove the existence of nonradial classical solutions to the 2D incompressible Euler equations with compact support. More precisely, for any positive integer $k$, we construct compactly supported stationary Euler flows of class…

Analysis of PDEs · Mathematics 2024-06-10 Alberto Enciso , Antonio J. Fernández , David Ruiz

The master equation and, more generally, Markov processes are routinely used as models for stochastic processes. They are often justified on the basis of randomization and coarse-graining assumptions. Here instead, we derive n-th order…

Statistical Mechanics · Physics 2012-09-27 Julian Lee , Steve Pressé

We consider a class of nonconvex energy functionals that lies in the framework of the peridynamics model of continuum mechanics. The energy densities are functions of a nonlocal strain that describes deformation based on pairwise…

Analysis of PDEs · Mathematics 2023-06-28 Tadele Mengesha , James M. Scott

A nonlinear master equation is derived, reflecting properly the entropy of open quantum systems. In contrast to linear alternatives, its equilibrium solution is exactly the canonical Gibbs density matrix. The corresponding nonlinear…

Quantum Physics · Physics 2021-02-08 Roumen Tsekov

Nonparametric estimators for the mean and the covariance functions of functional data are proposed. The setup covers a wide range of practical situations. The random trajectories are, not necessarily differentiable, have unknown regularity,…

Statistics Theory · Mathematics 2025-02-13 Steven Golovkine , Nicolas Klutchnikoff , Valentin Patilea

Virtually all questions that one can ask about the behavioral and structural complexity of a stochastic process reduce to a linear algebraic framing of a time evolution governed by an appropriate hidden-Markov process generator. Each type…

Chaotic Dynamics · Physics 2018-04-18 Paul M. Riechers , James P. Crutchfield

In this paper, we consider functionals based on moments and non-linear entropies which have a linear growth in time in case of source-type so-lutions to the fast diffusion or porous medium equations, that are also known as Barenblatt…

Analysis of PDEs · Mathematics 2015-01-16 Jean Dolbeault , Giuseppe Toscani

We investigate a linear operator associated with a functional equation that arises from studying some class of invariant measures under multidimensional transformations. By examining its iterates, we derive an explicit solution formula for…

Functional Analysis · Mathematics 2026-03-09 Oleksandr V. Maslyuchenko , Janusz Morawiec , Thomas Zürcher

We present a method of parameter estimation for large class of nonlinear systems, namely those in which the state consists of output derivatives and the flow is linear in the parameter. The method, which solves for the unknown parameter by…

Systems and Control · Electrical Eng. & Systems 2024-07-16 Simon Kuang , Xinfan Lin

Functional data that are nonnegative and have a constrained integral can be considered as samples of one-dimensional density functions. Such data are ubiquitous. Due to the inherent constraints, densities do not live in a vector space and,…

Statistics Theory · Mathematics 2016-01-13 Alexander Petersen , Hans-Georg Müller

Non-Markovian stochastic Schr\"odinger equations (NMSSE) are important tools in quantum mechanics, from the theory of open systems to foundations. Yet, in general, they are but formal objects: their solution can be computed numerically only…

Quantum Physics · Physics 2017-09-20 Antoine Tilloy

Paradoxically, while the assumptions of second-order stationarity and isotropy appear outdated in light of modern spatial data, they remain remarkably robust in practice, as nonstationary methods often provide marginal improvements in…

Methodology · Statistics 2025-11-07 Federico Blasi , Reinhard Furrer

We establish boundedness estimates for solutions of generalized porous medium equations of the form $$ \partial_t u+(-\mathfrak{L})[u^m]=0\quad\quad\text{in $\mathbb{R}^N\times(0,T)$}, $$ where $m\geq1$ and $-\mathfrak{L}$ is a linear,…

Analysis of PDEs · Mathematics 2023-02-03 Matteo Bonforte , Jørgen Endal

Large nonlinear recurrent neural networks with random couplings generate high-dimensional, potentially chaotic activity whose structure is of interest in neuroscience and other fields. A fundamental object encoding the collective structure…

Disordered Systems and Neural Networks · Physics 2026-05-06 David G. Clark