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We present a new methodology for studying non-Hamiltonian nonlinear systems based on an information theoretic extension of a renormalization group technique using a modified maximum entropy principle. We obtain a rigorous dimensionally…

Computational Physics · Physics 2013-06-28 M. Schmuck , M. Pradas , S. Kalliadasis , G. A. Pavliotis

The dynamics of thin, non-circular droplets evaporating in the diffusion-limited regime are examined. The challenging non-rectilinear mixed-boundary problem this poses is solved using a novel asymptotic approach and an asymptotic expansion…

Fluid Dynamics · Physics 2023-05-03 Alexander W. Wray , Matthew R. Moore

By considering any one-dimensional time-homogeneous solvable diffusion process,this paper develops a complete analytical framework for computing the distribution of the last hitting time, to any level, and its joint distribution with the…

Probability · Mathematics 2025-11-12 Giuseppe Campolieti , Yaode Sui

In this paper we consider in detail the connection between the problem of a polymer in a random medium and that of a quantum particle in a random potential. We are interested in a system of finite volume where the polymer is known to be…

Disordered Systems and Neural Networks · Physics 2009-10-31 Yohannes Shiferaw , Yadin Y. Goldschmidt

A multispecies, collisionless plasma is modeled by the Vlasov-Poisson system. Assuming that the electric field decays with sufficient rapidity as $t \to\infty$, we show that the velocity characteristics and spatial averages of the particle…

Analysis of PDEs · Mathematics 2022-01-25 Stephen Pankavich

We consider directed polymers in a random potential given by a deterministic profile with a strong maximum at the origin taken with random sign at each integer time. We study two main objects based on paths in this random potential. First,…

Probability · Mathematics 2009-09-15 Yuri Bakhtin , Konstantin Khanin

We formulate a finite-particle method of mass transport that accounts for general mixed boundary conditions. The particle method couples a geometrically-exact treatment of advection; Wasserstein gradient-flow dynamics; and a…

Numerical Analysis · Mathematics 2025-03-05 Anna Pandolfi , Ignacio Romero , Michael Ortiz

We propose a new estimator to measure directed dependencies in time series. The dimensionality of data is first reduced using a new non-uniform embedding technique, where the variables are ranked according to a weighted sum of the amount of…

Methodology · Statistics 2020-12-02 Payam Shahsavari Baboukani , Carina Graversen , Emina Alickovic , Jan Østergaard

In recent work, Li et al.\ (Comm.\ Math.\ Sci., 7:81-107, 2009) developed a diffuse-domain method (DDM) for solving partial differential equations in complex, dynamic geometries with Dirichlet, Neumann, and Robin boundary conditions. The…

Numerical Analysis · Mathematics 2015-05-18 Karl Yngve Lervåg , John Lowengrub

We study vectors chosen at random from a compact convex polytope in $\mathbb{R}^n$ given by a finite number of linear constraints. We determine which projections of these random vectors are asymptotically normal as $n\to\infty$. Marginal…

Probability · Mathematics 2025-03-18 Fabrice Gamboa , Martin Venker

Electromagnetic waves propagating, at finite speeds, in conventional wave-guiding structures are reflected by discontinuities and decay in lossy regions. In this Letter, we drastically modify this typical guided-wave behavior by combining…

Optics · Physics 2018-09-12 S. Ali Hassani Gangaraj , Francesco Monticone

In this paper we consider the unconstrained minimization problem of a smooth function in ${\mathbb{R}}^n$ in a setting where only function evaluations are possible. We design a novel randomized derivative-free algorithm --- the stochastic…

Optimization and Control · Mathematics 2019-05-08 El Houcine Bergou , Eduard Gorbunov , Peter Richtárik

We consider the estimation of Dirichlet Process Mixture Models (DPMMs) in distributed environments, where data are distributed across multiple computing nodes. A key advantage of Bayesian nonparametric models such as DPMMs is that they…

Machine Learning · Statistics 2017-09-20 Ruohui Wang , Dahua Lin

We obtain new transport-entropy inequalities and, as a by-product, new deviation estimates for the laws of two kinds of discrete stochastic approximation schemes. The first one refers to the law of an Euler like discretization scheme of a…

Probability · Mathematics 2013-02-01 Max Fathi , Noufel Frikha

In this paper, we describe a novel application of sigma-point methods to continuous-discrete filtering. In principle, the nonlinear continuous- discrete filtering problem can be solved exactly. In practice, the solution contains terms that…

Computation · Statistics 2015-06-15 Simon Lyons , Simo Särkkä , Amos Storkey

We introduce a novel generative model for the representation of joint probability distributions of a possibly large number of discrete random variables. The approach uses measure transport by randomized assignment flows on the statistical…

Machine Learning · Statistics 2025-01-15 Bastian Boll , Daniel Gonzalez-Alvarado , Stefania Petra , Christoph Schnörr

This paper concerns the estimation of sums of functions of observable and unobservable variables. Lower bounds for the asymptotic variance and a convolution theorem are derived in general finite- and infinite-dimensional models. An explicit…

Statistics Theory · Mathematics 2007-06-13 Cun-Hui Zhang

Systems describing the long-range interaction between individuals have attracted a lot of attention in the last years, in particular in relation with living systems. These systems are quadratic, written under the form of transport equations…

Analysis of PDEs · Mathematics 2023-06-13 Marie Doumic , Sophie Hecht , Benoit Perthame , Diane Peurichard

Unlike an equilibrium gas, the distribution of active particles can be very sensitive to what happens at the boundaries of their container. Experiments and simulations have previously highlighted the possibility of exploiting this behavior…

The polymer model framework is a classical tool from statistical mechanics that has recently been used to obtain approximation algorithms for spin systems on classes of bounded-degree graphs; examples include the ferromagnetic Potts model…

Data Structures and Algorithms · Computer Science 2022-03-29 Andreas Galanis , Leslie Ann Goldberg , James Stewart
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