To describe complex flow systems accurately, it is in many cases important to account for the properties of fluid flows on a microscopic scale. In this work, we focus on the description of liquid-vapor flow with a sha...
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We present a novel acceleration method for the solution of para-metric ODEs by single-step implicit solvers by means of greedy kernel-based surrogate models. In an offline phase, a set of trajectories is precomputed w...
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Coupled systems of porous media and free flow can be modelled by the Stokes equations in the free-flow domain, Darcy’s law in the porous medium, and an appropriate set of coupling conditions on the fluid–porous inte...
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Greedy kernel approximation algorithms are successful techniques for sparse and accurate data-based modelling and function approximation. Based on a recent idea of stabilization (Wenzel et al., A novel class of stabil...
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We introduce a Darcy-scale model to describe compressible multi-component flow in a fully saturated porous medium. In order to capture cross-diffusive effects between the different species correctly, we make use of th...
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Modern simulation scenarios require real-time or many query responses from a simulation model. This is the driving force for increased efforts in model order reduction for high dimensional dynamical systems or partial...
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ISBN:
(纸本)9788493964030
Modern simulation scenarios require real-time or many query responses from a simulation model. This is the driving force for increased efforts in model order reduction for high dimensional dynamical systems or partial differential equations. This demand for fast simulation models is even more critical for parametrized problems. Several snapshot-based methods for basis construction exist for parametrized model order reduction, e.g. proper orthogonal decomposition (POD) or reduced basis (RB) methods. An often faced problem is that the produced reduced models for a given accuracy tolerance are still of too high dimension. This is especially the case for evolution problems where the model shows high variability during time evolution. We will present an approach to gain control over the online complexity of a reduced model by an adaptive time domain partitioning. Thereby we can prescribe simultaneously a desired error tolerance and a limiting size of the dimension of the reduced model. This leads to fast and accurate reduced models. The method will be applied to an advection problem.
In this study, I compute the static dipole polarizability of main-group elements using the finite-field method combined with relativistic coupled-cluster and configuration-interaction simulations. The computational re...
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In this study, I compute the static dipole polarizability of main-group elements using the finite-field method combined with relativistic coupled-cluster and configuration-interaction simulations. The computational results closely align with the values recommended in the 2018 Table of static dipole polarizabilities of neutral elements [Mol. Phys. 117, 1200 (2019)]. Additionally, I investigate the influence of relativistic effects and electron correlation on atomic dipole polarizabilities. Specifically, three types of relativistic effects impacting dipole polarizabilities are studied: scalar-relativistic, spin-orbit coupling, and fully relativistic Dirac-Coulomb effects. The results indicate that scalar-relativistic effects are predominant for atoms in groups 1 and 2, with minimal influence from spin-orbit coupling effects. Conversely, for elements in groups 13–18, scalar-relativistic effects are less significant, while spin-orbit coupling significantly affects elements starting from the fourth row in groups 13 and 14 and from the fifth row in groups 15–18. In each category of relativistic effects, the impact of electron correlation is evaluated. The results show that electron correlation significantly influences dipole polarizability calculations, particularly for atoms from groups 1, 2, 13, and 14, but is less significant for atoms from groups 15–18. This study provides a comprehensive and consistent data set of dipole polarizabilities and contributes to a systematic understanding of the roles of relativistic and electron-correlation effects in atomic dipole polarizabilities, serving as a valuable reference for future research.
Abstract This work is concerned with derivation of fully offline/online decomposable efficient a-posteriori error estimators for reduced parameterized nonlinear kernel-based systems. The dynamical systems under consid...
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Abstract This work is concerned with derivation of fully offline/online decomposable efficient a-posteriori error estimators for reduced parameterized nonlinear kernel-based systems. The dynamical systems under consideration consist of a nonlinear, time- and parameter-dependent kernel expansion representing the system's inner dynamics as well as time- and parameter-affine inputs, initial conditions and outputs. The estimators are established for a reduction technique originally proposed in Phillips et al. (2003) and are an extension of the estimators derived in Wirtz and Haasdonk (2012) to the fully time-dependent, parameterized setting. Key features for the efficient error estimation are to use local Lipschitz constants provided by a certain class of kernels and an iterative scheme to balance computation cost against estimation sharpness. Together with the affinely time/parameter-dependent system components a full offline/online decomposition for both the reduction process and the error estimators is possible. Some experimental results for synthetic systems illustrate the efficient evaluation of the derived error estimators for different parameters.
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