A normalized gradient flow method with attractive–repulsive splitting for computing ground states of Bose–Einstein condensates with higher-order interaction

Ruan, Xinran

doi:10.1016/j.jcp.2018.04.038

Cited by 5 publications

(2 citation statements)

References 45 publications

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“…Figure 5.5 shows the isosurface of the numerical ground state densities with ε = 10 −4 , A = 20 and different values of β and δ. The ground state densities shown in Figure 5.5 are consistent with the results shown in [40]. Again we observe that the ground state densities become less concentrated as β or δ increases.…”

Section: Numerical Examples In High Dimensionssupporting

confidence: 85%

“…δ 1, introduces high nonlinearity, which may result in inefficiency and instability of the numerical methods proposed for the δ = 0 case. For instance, the regularized Newton method would become extremely slow when δ is large, which will be shown later via numerical experiments, while the normalized gradient flow method could even fail as shown in [40], even if a convex-concave splitting of the HOI term is adapted to significantly improve the robustness of the method.…”

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confidence: 98%

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Computing Ground States of Bose--Einstein Condensates with Higher Order Interaction via a Regularized Density Function Formulation

Bao¹,

Ruan²

2019

SIAM J. Sci. Comput.

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We propose and analyze a new numerical method for computing the ground state of the modified Gross-Pitaevskii equation for modeling the Bose-Einstein condensate with a higher order interaction by adapting the density function formulation and the accelerated projected gradient method. By reformulating the energy functional E(φ) with φ, the wave function, in terms of the density ρ = |φ| 2 , the original non-convex minimization problem for defining the ground state is then reformulated to a convex minimization problem. In order to overcome the semi-smoothness of the function √ ρ in the kinetic energy part, a regularization is introduced with a small parameter 0 < ε 1. Convergence of the regularization is established when ε → 0. The regularized convex optimization problem is discretized by the second order finite difference method. The convergence rates in terms of the density and energy of the discretization are established. The accelerated projected gradient method is adapted for solving the discretized optimization problem. Numerical results are reported to demonstrate the efficiency and accuracy of the proposed numerical method. Our results show that the proposed method is much more efficient than the existing methods in the literature, especially in the strong interaction regime.

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Section: Numerical Examples In High Dimensionssupporting

confidence: 85%

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confidence: 98%