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Yo Nakamura

Publications and source records attributed to Yo Nakamura.

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Three-dimensional orbital-free density functional theory description of nuclear pasta in the inner crust of neutron stars

Background: In the bottom layer of the inner crust of neutron stars, various crystalline structures are expected to emerge that are collectively called ``nuclear pasta.'' It is desirable to know properties of nuclear pasta in a wide variety of conditions for astrophysical applications. However, three-dimensional fully-microscopic calculations require huge computational effort that makes it still challenging to carry out systematic calculations. Purpose: In this paper, we propose an efficient method to calculate various nuclear pasta configurations in a non-empirical manner, based on three-dimensional orbital-free density functional theory (OF-DFT). We demonstrate the feasibility of the proposed approach by applying it to densities across the inner crust of neutron stars. Methods: As a first application of OF-DFT for nuclear pasta, we employ the second-order extended Thomas-Fermi (ETF) expansion of Skyrme energy density functional (EDF) to construct an EDF that depends only on neutron and proton number densities. Based on the variational principle, we derive Euler-Lagrange equations to determine optimal neutron and proton density distributions and solve them self-consistently. In this work, we call this approach the self-consistent ETF (SC-ETF) method. Results: We perform three-dimensional SC-ETF calculations with various box sizes. We successfully obtain various pasta structures, depending on given average nucleon number densities, consistent with earlier studies. Moreover, we find other exotic structures, such as bending and/or connected rods, slabs with a hole, etc., underlining the advantage of the self-consistent formalism. Conclusions: We demonstrate that the SC-ETF method proposed in this study, which can be regarded as a realization of OF-DFT, is a promising tool that can efficiently describe complex pasta structures without empirical assumptions on geometric shapes.

nucl-th

Physics-informed neural network applied to surface-tension-driven liquid film flows

A physics-informed neural network (PINN), which has been recently proposed by Raissi et al [J. Comp. Phys. 378, pp. 686-707 (2019)], is applied to the partial differential equation (PDE) of liquid film flows. The PDE considered is the time evolution of the thickness distribution $h(x,t)$ owing to the Laplace pressure, which involves 4th-order spatial derivative and 4th-order nonlinear term. Even for such a PDE, it is confirmed that the PINN can predict the solutions with sufficient accuracy. Nevertheless, some improvements are needed in training convergence and accuracy of the solutions. The precision of floating-point numbers is a critical issue for the present PDE. When the calculation is executed with a single precision floating-point number, the optimization is terminated due to the loss of significant digits. Calculation of the automatic differentiation (AD) dominates the computational time required for training, and becomes exponentially longer with increasing order of derivatives. By splitting the original 4th-order one-variable PDE into 2nd-order two-variable PDEs, the computational time for each training iteration is greatly reduced. The sampling density of training data also significantly affects training convergence. For the problem considered in this study, mproved convergence was obtained by allowing the sampling density of training data to be greater in earlier time ranges, where the rapid flattening of the thickness occurs.

physics.flu-dyn