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POD--Galerkin reduced-order models (ROMs) for fluid-structure interaction problems (incompressible fluid and thin structure) are proposed in this paper. Both the high-fidelity and reduced-order methods are based on a Chorin-Temam…

Numerical Analysis · Mathematics 2017-11-30 Francesco Ballarin , Gianluigi Rozza , Yvon Maday

A first-principles approach to describe electron dynamics in open quantum systems driven far from equilibrium via external time-dependent stimuli is introduced. Within this approach, the driven Liouville von Neumann methodology is used to…

Mesoscale and Nanoscale Physics · Physics 2023-05-10 Annabelle Oz , Abraham Nitzan , Oded Hod , Juan E. Peralta

Reduced order models play an important role in the design, optimization and control of dynamical systems. In recent years, there has been an increasing interest in the application of data-driven techniques for model reduction that can…

Fluid Dynamics · Physics 2021-02-23 Carlos J. G. Rojas , Andreas Dengel , Mateus Dias Ribeiro

The simulation of quantum transport in a realistic, many-particle system is a nontrivial problem with no quantitatively satisfactory solution. While real-time propagation has the potential to overcome the shortcomings of conventional…

Mesoscale and Nanoscale Physics · Physics 2015-10-16 Justin E. Elenewski , Yanxiang Zhao , Hanning Chen

We show that the von Neumann's algorithm of reduction (i.e. the algorithm of calculating the density matrix of the observable subsystem from the density matrix of the closed quantum system) corresponds to the special approximation at which…

Quantum Physics · Physics 2007-05-23 N. K. Solovarov

The objective of this paper is to investigate how noisy and incomplete observations can be integrated in the process of building a reduced-order model. This problematic arises in many scientific domains where there exists a need for…

Machine Learning · Statistics 2018-05-18 Patrick Héas , Cédric Herzet

Leveraging recent work on data-driven methods for constructing a finite state space Markov process from dynamical systems, we address two problems for obtaining further reduced statistical representations. The first problem is to extract…

Chaotic Dynamics · Physics 2024-05-14 Ludovico Theo Giorgini , Andre N. Souza , Peter J. Schmid

This work proposes a method for model reduction of finite-volume models that guarantees the resulting reduced-order model is conservative, thereby preserving the structure intrinsic to finite-volume discretizations. The proposed…

Numerical Analysis · Computer Science 2018-07-04 Kevin Carlberg , Youngsoo Choi , Syuzanna Sargsyan

We consider the problem of finding the energy minimum of a complex quantum Hamiltonian by employing a non-Markovian bath prepared in a low energy state. The energy minimization problem is thus turned into a thermodynamic cooling protocol in…

Quantum Physics · Physics 2024-03-29 Alberto Imparato , Nicholas Chancellor , Gabriele De Chiara

Partial differential equations can be used to model many problems in several fields of application including, e.g., fluid mechanics, heat and mass transfer, and electromagnetism. Accurate discretization methods (e.g., finite element or…

Numerical Analysis · Mathematics 2022-03-18 Pierfrancesco Siena , Michele Girfoglio , Gianluigi Rozza

We present an efficient and robust numerical model for simulation of electrokinetic phenomena in porous networks over a wide range of applications including energy conversion, desalination, and lab-on-a-chip systems. Coupling between fluid…

Fluid Dynamics · Physics 2016-10-04 Shima Alizadeh , Ali Mani

We introduce optimization-based full-order and reduced-order formulations of fluid structure interaction problems. We study the flow of an incompressible Newtonian fluid which interacts with an elastic body: we consider an arbitrary…

Numerical Analysis · Mathematics 2024-12-30 Tommaso Taddei , Xuejun Xu , Lei Zhang

This paper considers the reduction of the Langevin equation arising from bio-molecular models. To facilitate the construction and implementation of the reduced models, the problem is formulated as a reduced-order modeling problem. The…

Numerical Analysis · Mathematics 2019-10-04 Lina Ma , Xiantao Li , Chun Liu

Methodological aspects of using the driven Liouville-von Neumann (DLvN) approach for simulating dynamical properties of molecular junctions are discussed. As a model system we consider a non-interacting resonant level uniformly coupled to a…

Mesoscale and Nanoscale Physics · Physics 2019-07-24 Inbal Oz , Oded Hod , Abraham Nitzan

A data-driven projection-based reduced-order model (ROM) for nonlinear thermal radiative transfer (TRT) problems is presented. The TRT ROM is formulated by (i) a hierarchy of low-order quasidiffusion (aka variable Eddington factor)…

Numerical Analysis · Mathematics 2024-09-24 Joseph M. Coale , Dmitriy Y. Anistratov

We present the Reduced Operator Approximation: a simple, physically transparent and computationally efficient method of modelling open quantum systems. It employs the Heisenberg picture of the quantum dynamics, which allows us to focus on…

Quantum Physics · Physics 2015-08-06 Agnieszka Werpachowska

Transport process in molecular chain in nonequilibrium stationary state is theoretically investigated. The molecule is interacting at its both ends with thermal baths which has different temperatures, while no dissipation mechanism is…

Statistical Mechanics · Physics 2015-05-20 Satoshi Tanaka , Kazuki Kanki , Tomio Petrosky

A linear non-modal mechanism for transient amplification of perturbation energy is known to trigger sub-critical transition to turbulence in many shear flows. Feedback control strategies for minimizing this transient energy growth can be…

Fluid Dynamics · Physics 2019-07-04 Aniketh Kalur , Maziar S. Hemati

A general method of quantum-to-classical reduction of quantum dynamics is described. The key aspect of our method is the similarity transformation of the Liouvillian, which provides a new perspective. In conventional studies of quantum…

Statistical Mechanics · Physics 2015-04-09 Norikazu Kamiya

We present a data-driven nonintrusive model order reduction method for dynamical systems with moving boundaries. The proposed method draws on the proper orthogonal decomposition, Gaussian process regression, and moving least squares…

Computational Engineering, Finance, and Science · Computer Science 2021-03-18 Zhan Ma , Wenxiao Pan