English
Related papers

Related papers: Large-N dynamics of the spiked tensor model with r…

200 papers

Using the theory of Dirichlet forms we construct a large class of continuous semimartingales on an open domain $E \subset \mathbb{R}^d$, which are governed by rank-based, in addition to name-based, characteristics. Using the results of Baur…

Probability · Mathematics 2021-04-12 David Itkin , Martin Larsson

Random tensor models are generalizations of random matrix models which admit $1/N$ expansions. In this article we show that the topological recursion, a modern approach to matrix models which solves the loop equations at all orders, is also…

High Energy Physics - Theory · Physics 2018-11-27 Valentin Bonzom , Stephane Dartois

A key ingredient of hydrodynamical modeling of relativistic heavy ion collisions is thermal initial conditions, an input that is the consequence of a pre-thermal dynamics which is not completely understood yet. In the paper we employ a…

Nuclear Theory · Physics 2015-01-22 V. Yu. Naboka , S. V. Akkelin , Iu. A. Karpenko , Yu. M. Sinyukov

The Schwinger-Dyson Equations (SDEs) of matrix models are known to form (half) a Virasoro algebra and have become a standard tool to solve matrix models. The algebra generated by SDEs in tensor models (for random tensors in a suitable…

High Energy Physics - Theory · Physics 2015-06-11 Valentin Bonzom

We study in this paper the behavior of a periodically driven nonlinear mechanical system. Bifurcation diagrams are found which locate regions of quasiperiodic, periodic and chaotic behavior within the parameter space of the system. We also…

Chaotic Dynamics · Physics 2009-10-31 Randy Kobes , Junxian Liu , Slaven Peles

A large system of ordinary differential equations is approximated by a parabolic partial differential equation with dynamic boundary condition and a different one with Robin boundary condition. Using the theory of differential operators…

Functional Analysis · Mathematics 2014-03-26 András Bátkai , Ágnes Havasi , Róbert Horváth , Dávid Kunszenti-Kovács , Péter L. Simon

In this paper the multi-dimensional random walk models governed by distributed fractional order differential equations and multi-term fractional order differential equations are constructed. The scaling limits of these random walks to a…

Classical Analysis and ODEs · Mathematics 2007-05-23 Sabir Umarov , Stanly Steinberg

We develop a path integral framework for determining most probable paths in a class of systems of stochastic differential equations with piecewise-smooth drift and additive noise. This approach extends the Freidlin-Wentzell theory of large…

Dynamical Systems · Mathematics 2022-11-08 Kaitlin Hill , Jessica Zanetell , John A Gemmer

We introduce a prototype model in an attempt to capture some aspects of market dynamics simulating a trading mechanism. The model description starts with a discrete-space, continuous-time Markov process describing arrival and movement of…

Trading and Market Microstructure · Quantitative Finance 2013-04-04 N. Vvedenskaya , Y. Suhov , V. Belitsky

A geometrical model which captures the main ingredients governing atom-diatom collinear chemical reactions is proposed. This model is neither near-integrable nor hyperbolic, yet it is amenable to analysis using a combination of the recently…

Chaotic Dynamics · Physics 2018-04-10 L. Lerman , V. Rom-Kedar

This work is concerned with existence of weak solutions to discon- tinuous stochastic differential equations driven by multiplicative Gaus- sian noise and sliding mode control dynamics generated by stochastic differential equations with…

Optimization and Control · Mathematics 2015-04-27 Viorel Barbu , Stefano Bonaccorsi , Luciano Tubaro

In this paper, we introduce a three-component Schnakenberg model. Its key feature is that it has a solution consisting of N spikes that undergoes a Hopf bifurcation with respect to N distinct modes nearly simultaneously. This results in…

Pattern Formation and Solitons · Physics 2022-03-25 Shuangquan Xie , Theodore kolokolnikov , Yasumasa Nishiura

The spike trains are the main components of the information processing in the brain. To model spike trains several point processes have been investigated in the literature. And more macroscopic approaches have also been studied, using…

Neural and Evolutionary Computing · Computer Science 2015-06-09 Julien Chevallier , Maria J. Caceres , Marie Doumic , Patricia Reynaud-Bouret

Simulating stochastic differential equations (SDEs) in bounded domains, presents significant computational challenges due to particle exit phenomena, which requires accurate modeling of interior stochastic dynamics and boundary…

Machine Learning · Statistics 2025-07-23 Minglei Yang , Yanfang Liu , Diego del-Castillo-Negrete , Yanzhao Cao , Guannan Zhang

The past decades have seen increasing interest in modelling uncertainty by heterogeneous methods, combining probability and interval analysis, especially for assessing parameter uncertainty in engineering models. A unifying mathematical…

Probability · Mathematics 2022-08-15 Jelena Karakašević , Michael Oberguggenberger

We introduce microscopic and macroscopic stochastic traffic models including traffic accidents. The microscopic model is based on a Follow-the-Leader approach whereas the macroscopic model is described by a scalar conservation law with…

Probability · Mathematics 2021-11-19 Simone Göttlich , Thomas Schillinger

A Boussinesq model for the Benard convection under random influences is considered as a system of stochastic partial differential equations. This is a coupled system of stochastic Navier-Stokes equations and the transport equation for…

Probability · Mathematics 2009-05-12 Jinqiao Duan , Annie Millet

In this paper, we propose two new solution schemes to solve the stochastic strongly monotone variational inequality problems: the stochastic extra-point solution scheme and the stochastic extra-momentum solution scheme. The first one is a…

Optimization and Control · Mathematics 2021-07-20 Kevin Huang , Shuzhong Zhang

We introduce a guided stochastic sampling method that augments sampling from diffusion models with physics-based guidance derived from partial differential equation (PDE) residuals and observational constraints, ensuring generated samples…

Machine Learning · Computer Science 2026-05-28 Andrew Millard , Fredrik Lindsten , Zheng Zhao

In this paper, we introduce a three-component Gierer-Meinhardt model in the semi-strong interaction regime, characterized by an asymptotically large diffusivity ratio. A key feature of this model is that the interior spike can undergo Hopf…

Analysis of PDEs · Mathematics 2025-10-03 Chunyi Gai , Fahad Al Saadi