High-accuracy Casimir-Polder force calculations using the Discontinuous Galerkin Time-Domain method

• URL: http://arxiv.org/abs/2306.16939v2
• Date: Fri, 1 Dec 2023 07:55:27 GMT
• Title: High-accuracy Casimir-Polder force calculations using the Discontinuous Galerkin Time-Domain method
• Authors: Philip Tr{\o}st Kristensen, Bettina Beverungen, Francesco Intravaia, Kurt Busch
• Abstract summary: We describe a numerical time-domain approach for high-accuracy calculations of Casimir-Polder forces near micro-structured materials. We find average relative errors as low as a few parts in a million. As an application example, we investigate the anisotropy-induced repulsive behavior of the Casimir-Polder force near a sharp gold wedge described by a hydrodynamic Drude model.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We describe a numerical time-domain approach for high-accuracy calculations of Casimir-Polder forces near micro-structured materials. The use of a time-domain formulation enables the investigation of a broad range of materials described by advanced material models, including nonlocal response functions. We validate the method by a number of example calculations for which we thoroughly investigate the convergence properties of the method, and comparing to analytical reference calculations, we find average relative errors as low as a few parts in a million. As an application example, we investigate the anisotropy-induced repulsive behavior of the Casimir-Polder force near a sharp gold wedge described by a hydrodynamic Drude model.

Related papers

• Numerical evaluation of Casimir forces using the discontinuous Galerkin time-domain method [0.0]
  We present a time-domain scheme for computing Casimir forces within the Maxwell stress tensor formalism.<n>The approach enables accurate evaluation of Casimir--Lifshitz interactions for a wide range of geometries and material properties at finite temperature.
  arXiv  Detail & Related papers  (2026-03-04T09:40:07Z)
• Efficient Covariance Estimation for Sparsified Functional Data [51.69796254617083]
  proposed Random-knots (Random-knots-Spatial) and B-spline (Bspline-Spatial) estimators of the covariance function are computationally efficient.<n>Asymptotic pointwise of the covariance are obtained for sparsified individual trajectories under some regularity conditions.
  arXiv  Detail & Related papers  (2025-11-23T00:50:33Z)
• Solving quantum impurity problems on the L-shaped Kadanoff-Baym contour [0.0]
  We extend the recently developed Grassmann time-evolving matrix product operator (GTEMPO) method to solve quantum impurity problems directly on the Kadanoff-Baym contour. The accuracy of this method is numerically demonstrated against exact solutions in the noninteracting case, and against existing calculations on the real- and imaginary-time axes.
  arXiv  Detail & Related papers  (2024-04-08T11:21:06Z)
• Gaussian process regression and conditional Karhunen-Lo\'{e}ve models for data assimilation in inverse problems [68.8204255655161]
  We present a model inversion algorithm, CKLEMAP, for data assimilation and parameter estimation in partial differential equation models. The CKLEMAP method provides better scalability compared to the standard MAP method.
  arXiv  Detail & Related papers  (2023-01-26T18:14:12Z)
• Multielement polynomial chaos Kriging-based metamodelling for Bayesian inference of non-smooth systems [0.0]
  This paper presents a surrogate modelling technique based on domain partitioning for Bayesian parameter inference of highly nonlinear engineering models. The developed surrogate model combines in a piecewise function an array of local Polynomial Chaos based Kriging metamodels constructed on a finite set of non-overlapping of the input space. The efficiency and accuracy of the proposed approach are validated through two case studies, including an analytical benchmark and a numerical case study.
  arXiv  Detail & Related papers  (2022-12-05T13:22:39Z)
• A blob method method for inhomogeneous diffusion with applications to multi-agent control and sampling [0.6562256987706128]
  We develop a deterministic particle method for the weighted porous medium equation (WPME) and prove its convergence on bounded time intervals. Our method has natural applications to multi-agent coverage algorithms and sampling probability measures.
  arXiv  Detail & Related papers  (2022-02-25T19:49:05Z)
• Numerical Simulations of Noisy Quantum Circuits for Computational Chemistry [51.827942608832025]
  Near-term quantum computers can calculate the ground-state properties of small molecules. We show how the structure of the computational ansatz as well as the errors induced by device noise affect the calculation.
  arXiv  Detail & Related papers  (2021-12-31T16:33:10Z)
• Accuracy of the typicality approach using Chebyshev polynomials [0.0]
  Trace estimators allow to approximate thermodynamic equilibrium observables with astonishing accuracy. Here we report an approach which employs Chebyshev an alternative approach describing the exponential expansion of space weights. This method turns out to be also very accurate in general, but shows systematic inaccuracies at low temperatures.
  arXiv  Detail & Related papers  (2021-04-27T14:23:36Z)
• Leveraging Global Parameters for Flow-based Neural Posterior Estimation [90.21090932619695]
  Inferring the parameters of a model based on experimental observations is central to the scientific method. A particularly challenging setting is when the model is strongly indeterminate, i.e., when distinct sets of parameters yield identical observations. We present a method for cracking such indeterminacy by exploiting additional information conveyed by an auxiliary set of observations sharing global parameters.
  arXiv  Detail & Related papers  (2021-02-12T12:23:13Z)
• The Variational Method of Moments [65.91730154730905]
  conditional moment problem is a powerful formulation for describing structural causal parameters in terms of observables. Motivated by a variational minimax reformulation of OWGMM, we define a very general class of estimators for the conditional moment problem. We provide algorithms for valid statistical inference based on the same kind of variational reformulations.
  arXiv  Detail & Related papers  (2020-12-17T07:21:06Z)
• SLEIPNIR: Deterministic and Provably Accurate Feature Expansion for Gaussian Process Regression with Derivatives [86.01677297601624]
  We propose a novel approach for scaling GP regression with derivatives based on quadrature Fourier features. We prove deterministic, non-asymptotic and exponentially fast decaying error bounds which apply for both the approximated kernel as well as the approximated posterior.
  arXiv  Detail & Related papers  (2020-03-05T14:33:20Z)
• Fast approximations in the homogeneous Ising model for use in scene analysis [61.0951285821105]
  We provide accurate approximations that make it possible to numerically calculate quantities needed in inference. We show that our approximation formulae are scalable and unfazed by the size of the Markov Random Field. The practical import of our approximation formulae is illustrated in performing Bayesian inference in a functional Magnetic Resonance Imaging activation detection experiment, and also in likelihood ratio testing for anisotropy in the spatial patterns of yearly increases in pistachio tree yields.
  arXiv  Detail & Related papers  (2017-12-06T14:24:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.