English
Related papers

Related papers: Accuracy of time-dependent GGE under weak dissipat…

200 papers

We introduce a variational time discretization for the multi-dimensional gas dynamics equations, in the spirit of minimizing movements for curves of maximal slope. Each timestep requires the minimization of a functional measuring the…

Analysis of PDEs · Mathematics 2018-10-01 Fabio Cavalletti , Marc Sedjro , Michael Westdickenberg

We use a generalized Master equation (GME) formalism to describe the non-equilibrium time-dependent transport through a short quantum wire connected to semi-infinite biased leads. The contact strength between the leads and the wire are…

Mesoscale and Nanoscale Physics · Physics 2013-05-15 Kristinn Torfason , Andrei Manolescu , Valeriu Molodoveanu , Vidar Gudmundsson

In this work, we derived a transport equation based on a generalized equation of time-dependent generator coordinate method (TDGCM) under the Gaussian overlap approximation (GOA). The transport equation is obtained by using…

Nuclear Theory · Physics 2023-01-03 Fangyuan Wang , Yingxun Zhang , Zhipan Li

We observe dynamical fermionization, where the momentum distribution of a Tonks-Girardeau (T-G) gas of strongly interacting bosons in 1D evolves from bosonic to fermionic after its axial confinement is removed. The asymptotic momentum…

Quantum Gases · Physics 2020-03-30 Joshua M. Wilson , Neel Malvania , Yuan Le , Yicheng Zhang , Marcos Rigol , David S. Weiss

Safety-critical navigation applications require that estimation errors be reliably quantified and bounded. This can be challenging for linear dynamic systems if the process noise or measurement errors have uncertain time correlation. In…

Signal Processing · Electrical Eng. & Systems 2020-09-22 Omar Garcia Crespillo , Mathieu Joerger , Steve Langel

Starting from a quantum theory of dissipation for nuclear collective motion, the time-dependent generator coordinate method (TDGCM) is extended to allow for dissipation effects in the description of induced fission dynamics. The extension…

Nuclear Theory · Physics 2022-05-25 Jie Zhao , Tamara Nikšić , Dario Vretenar

Non-Gaussian displacement distributions are universal predictors of dynamic heterogeneity in slowly varying environments. Here, we explore heterogeneous dynamics in supercooled liquid using molecular dynamics simulations and show the…

Soft Condensed Matter · Physics 2023-08-29 Vinay Vaibhav , Suman Dutta

Latent autoregressive processes are a popular choice to model time varying parameters. These models can be formulated as nonlinear state space models for which inference is not straightforward due to the high number of parameters. Therefore…

Computation · Statistics 2019-11-01 Alexander Kreuzer , Claudia Czado

In this paper we present a deep learning method to predict the temporal evolution of dissipative dynamic systems. We propose using both geometric and thermodynamic inductive biases to improve accuracy and generalization of the resulting…

Machine Learning · Computer Science 2022-06-07 Quercus Hernández , Alberto Badías , Francisco Chinesta , Elías Cueto

According to the Green-Kubo theory of linear response, the conductivity of an electronically gapped liquid can be expressed in terms of the time correlations of the adiabatic charge flux, which is determined by the atomic velocities and…

Materials Science · Physics 2019-07-23 Federico Grasselli , Stefano Baroni

Systems with long-range interactions display a short-time relaxation towards Quasi Stationary States (QSS) whose lifetime increases with the system size. In the paradigmatic Hamiltonian Mean-field Model (HMF) out-of-equilibrium phase…

Statistical Mechanics · Physics 2016-02-15 Gabriele Martelloni , Gianluca Martelloni , Pierre de Buyl , Duccio Fanelli

Dependent generalized extreme value (dGEV) models have attracted much attention due to the dependency structure that often appears in real datasets. To construct a dGEV model, a natural approach is to assume that some parameters in the…

Methodology · Statistics 2017-03-06 Bo Ning , Peter Bloomfield

In recent years, there has been an explosion of machine learning techniques for turbulence closure modeling, though many rely on augmenting existing models. While this has proven successful in single-phase flows, it breaks down for…

Fluid Dynamics · Physics 2021-06-22 S. Beetham , J. Capecelatro

We derive the time-dependent two-component Gross--Pitaevskii (GP) equation as an effective description of the dynamics of a dilute two-component Bose gas near its ground state, which exhibits a two-component Bose-Einstein condensate, in the…

Mathematical Physics · Physics 2025-06-03 Jacky Chong , Jinyeop Lee , Zhiwei Sun

Our previous study [1] has demonstrated that the gauge theory is a proper framework for characterizing the local temporal and spatial interactions in inhomogeneous elastic media. However, in that study temporal interactions were interpreted…

Classical Physics · Physics 2025-06-10 Zhihai Xiang

We introduce a general framework for deriving effective dynamics from arbitrary time-dependent generators, based on a systematic operator cumulant expansion. Unlike traditional approaches, which typically assume periodic or adiabatic…

Mathematical Physics · Physics 2025-10-02 Leon Bello , Tal Rubin , Wentao Fan , Nathaniel Fisch , Hakan Türeci

We study the out-of-equilibrium properties of a classical integrable non-relativistic theory, with a time evolution initially prepared with a finite energy density in the thermodynamic limit. The theory considered here is the Non-Linear…

Statistical Mechanics · Physics 2020-07-08 Giuseppe Del Vecchio Del Vecchio , Alvise Bastianello , Andrea De Luca , Giuseppe Mussardo

The gradient flow with semi-implicit discretization (GFSI) is the most widely used algorithm for computing the ground state of Gross-Pitaevskii energy functional. Numerous numerical experiments have shown that the energy dissipation holds…

Numerical Analysis · Mathematics 2025-10-23 Zixu Feng , Lunxu Liu , Qinglin Tang

Being able to describe accurately the dynamics and steady-states of driven and/or dissipative but quantum correlated lattice models is of fundamental importance in many areas of science: from quantum information to biology. An efficient…

Quantum Physics · Physics 2021-05-19 Conor Mc Keever , Marzena H. Szymańska

The dependence on training data of the Gibbs algorithm (GA) is analytically characterized. By adopting the expected empirical risk as the performance metric, the sensitivity of the GA is obtained in closed form. In this case, sensitivity is…

Machine Learning · Computer Science 2023-06-22 Samir M. Perlaza , Iñaki Esnaola , Gaetan Bisson , H. Vincent Poor
‹ Prev 1 3 4 5 6 7 10 Next ›