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To study and develop wall-functions for low-Reynolds-number models, a model linear equation is introduced. This equation simulates major mathematical peculiarities of the low-Reynolds-number model including a near wall sub-layer and…

Computational Physics · Physics 2007-05-23 S. V. Utyuzhnikov

We generalize a recently introduced traffic model, where the statistical weights are associated with whole trajectories, to the case of two-way flow. An interaction between the two lanes is included which describes a slowing down when two…

Statistical Mechanics · Physics 2009-10-31 Vladislav Popkov , Ingo Peschel

We consider a one-body spin-less electron spectral problem for a resonance scattering system constructed of a quantum well weakly connected to a noncompact exterior reservoir, where the electron is free. The simplest kind of the resonance…

Mathematical Physics · Physics 2009-02-26 B. Pavlov

We introduce a generalization of Glimm's random choice method, which provides us with an approximation of entropy solutions to quasilinear hyperbolic system of balance laws. The flux-function and the source term of the equations may depend…

Analysis of PDEs · Mathematics 2007-05-23 John M. Hong , Philippe G. LeFloch

One of the major problems for maximum likelihood estimation in the well-established directional models is that the normalising constants can be difficult to evaluate. A new general method of "score matching estimation" is presented here on…

Statistics Theory · Mathematics 2016-04-29 Kanti V Mardia , John T Kent , Arnab K Laha

Optimization with constraints is a typical problem in quantum physics and quantum information science that becomes especially challenging for high-dimensional systems and complex architectures like tensor networks. Here we use ideas of…

Quantum Physics · Physics 2021-11-18 Ilia A. Luchnikov , Mikhail E. Krechetov , Sergey N. Filippov

We study the problem of network regression, where one is interested in how the topology of a network changes as a function of Euclidean covariates. We build upon recent developments in generalized regression models on metric spaces based on…

Machine Learning · Statistics 2024-06-19 Alex G. Zalles , Kai M. Hung , Ann E. Finneran , Lydia Beaudrot , César A. Uribe

Finite-volume numerical method for study shallow water flows over an arbitrary bed profile in the presence of external force is proposed. This method uses the quasi-two-layer model of hydrodynamic flows over a stepwise boundary with…

Fluid Dynamics · Physics 2011-08-22 K. V. Karelsky , A. S. Petrosyan , A. G. Slavin

We consider an application of pooled stepped chutes where the transport in each pooled step is described by the shallow--water equations. Such systems can be found for example at large dams in order to release overflowing water. We analyze…

Analysis of PDEs · Mathematics 2010-10-27 Graziano Guerra , Michael Herty , Francesca Marcellini

In this work, we present a novel approach for solving stochastic shape optimization problems. Our method is the extension of the classical stochastic gradient method to infinite-dimensional shape manifolds. We prove convergence of the…

Optimization and Control · Mathematics 2020-11-03 Caroline Geiersbach , Estefania Loayza-Romero , Kathrin Welker

The paper continues the analysis, started in [1] (Part I,arXiv:2302.04353), of the model open wave-guide problem defined by 2 semi-infinite, rectangular wave-guides meeting along a common perpendicular line. In Part I we reduce the solution…

Mathematical Physics · Physics 2025-11-11 Charles L. Epstein

When solving compressible multi-material flow problems, an unresolved challenge is the computation of advective fluxes across material interfaces that separate drastically different thermodynamic states and relations. A popular idea in this…

Computational Physics · Physics 2023-09-15 Wentao Ma , Xuning Zhao , Shafquat Islam , Aditya Narkhede , Kevin Wang

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

State-space smoothing has found many applications in science and engineering. Under linear and Gaussian assumptions, smoothed estimates can be obtained using efficient recursions, for example Rauch-Tung-Striebel and Mayne-Fraser algorithms.…

Optimization and Control · Mathematics 2016-09-27 A. Y. Aravkin , J. V. Burke , L. Ljung , A. Lozano , G. Pillonetto

Empirical modelling often aims for the simplest model consistent with the data. A new technique is presented which quantifies the consistency of the model dynamics as a function of location in state space. As is well-known, traditional…

Chaotic Dynamics · Physics 2009-11-10 Patrick E. McSharry , Leonard A. Smith

We propose an approximate solver for multi-medium Riemann problems with materials described by a family of general Mie-Gr\"uneisen equations of state, which are widely used in practical applications. The solver provides the interface…

Numerical Analysis · Mathematics 2018-04-24 Li Chen , Ruo Li , Chengbao Yao

We consider an isothermal gas flowing through a straight pipe and study the effects of a two-way electronic valve on the flow. The valve is either open or closed according to the pressure gradient and is assumed to act without any time or…

Analysis of PDEs · Mathematics 2017-06-28 Andrea Corli , Magdalena Figiel , Anna Futa , Massimiliano D. Rosini

We consider the problem of estimating the topology of multiple networks from nodal observations, where these networks are assumed to be drawn from the same (unknown) random graph model. We adopt a graphon as our random graph model, which is…

Machine Learning · Statistics 2022-02-14 Madeline Navarro , Santiago Segarra

In this paper, we study the problem of simulating a DMC channel from another DMC channel under an average-case and an exact model. We present several achievability and infeasibility results, with tight characterizations in special cases. In…

Information Theory · Computer Science 2016-12-02 Farzin Haddadpour , Mohammad Hossein Yassaee , Salman Beigi , Amin Gohari , Mohammad Reza Aref

Modern generative modeling methods have demonstrated strong performance in learning complex data distributions from clean samples. In many scientific and imaging applications, however, clean samples are unavailable, and only noisy or…

Machine Learning · Computer Science 2026-05-29 Willem Diepeveen , Oscar Leong