Computer Physics Communications最新文献

In this work we report on a new version of FeynCalc, a Mathematica package widely used in the particle physics community for manipulating quantum field theoretical expressions and calculating Feynman diagrams. Highlights of the new version include greatly improved capabilities for doing multiloop calculations, including topology identification and minimization, optimized tensor reduction, rewriting of scalar products in terms of inverse denominators, detection of equivalent or scaleless loop integrals, derivation of Symanzik polynomials, Feynman parametric as well as graph representation for master integrals and initial support for handling differential equations and iterated integrals. In addition to that, the new release also features completely rewritten routines for color algebra simplifications, inclusion of symmetry relations between arguments of Passarino–Veltman functions, tools for determining matching coefficients and quantifying the agreement between numerical results, improved export to

Computational modeling and simulation of cellular blood flow is highly desired for understanding blood microcirculation and blood-related diseases such as thrombosis and tumor, but it remains a challenging task primarily because blood in microvessels should be described as a dense suspension of different types of deformable cells. The focus of the present work is on the development of a particle-based and GPU-accelerated numerical method that is able to quickly simulate the various behaviors of deformable cells in three-dimensional arbitrarily complex geometries. We employ a two-fluid model to describe blood flow, incorporating the deformation and aggregation of cells. A smoothed dissipative particle dynamics is used to solve the two-fluid model, and a discrete microstructure model is applied for the cell deformation, as well as a Morse potential model for the cell aggregation. The heterogeneous CPU-GPU environment is established, where each GPU thread is dedicated to a particle, and the CPU is mainly responsible for loading and exporting data. Five test cases are conducted against analytical theory, experimental data, and previous numerical results, for pure fluid, cell deformation, cell aggregation, cell suspension and the cellular flow in a complex network, respectively. It is shown that the methodology can accurately predict various behaviors of cells, and the GPU is well suited for particle-based modeling. Especially for cellular blood flow, where calculating cellular forces is a compute-intensive and time-consuming task, the GPU offers exceptional parallel capabilities, significantly enhancing the simulation efficiency. The speedup is about 3.5 times faster than the CPU parallelization with 96 cores for the pure fluid, and this acceleration nearly reaches 20 times when cells are included in the simulations. Particularly, the calculations for deformation and aggregation forces demonstrate a substantial speedup, achieving the improvements of up to 120 and 640 times, respectively, compared to their serial counterparts. The present methodology can effectively integrate various behaviors of cells, and has the potential in simulating very large microvascular networks at organ levels.

We describe RCWA4D, an electromagnetic solver for layered structures with incommensurate periodicities. Our method is based on an extension of the rigorous coupled wave analysis. We illustrate our method on the example of twisted bilayer photonic crystal and show that various properties of such structures can be reliably simulated. The method can be generalized to multi-layer structures in general in which each layer is periodic or quasi-periodic.

Recent advancements have established tensor network states (TNS) as formidable tools for exploring the complex realm of strongly-correlated many-particle systems in both one and two dimensions. To tackle the challenges presented by strongly-correlated fermion systems, various fermion tensor network states (f-TNS) methodologies have been developed. However, implementing f-TNS methods poses substantial challenges due to their particularly complex nature, making development efforts significantly difficult. This complexity is further exacerbated by the lack of underlying software packages that facilitate the development of f-TNS. Previously, we developed TNSPackage, a software package designed for TNS methods [1]. Initially, this package was only capable of handling spin and boson models. To confront the challenges presented by f-TNS, TNSPackage has undergone significant enhancements in its latest version, incorporating support for both symmetry and fermion tensors. This updated version provides a uniform interface for the consistent management of tensors across boson, fermion, and various symmetry types, maintaining its user-friendly and versatile nature. This greatly facilitates the development of programs based on f-TNS. The new TNSP framework consists of two principal components: a low-level tensor package named TAT, which supports sophisticated tensor operations, and a high-level interface package called tetragono that is built upon TAT. The tetragono package is designed to significantly simplify the development of complex physical models on square lattices. The TNSPackage framework enables users to implement a wide range of physical models with greater ease, without the need to pay close attention to the underlying implementation details.

In this paper we propose and benchmark an innovative implementation of the Random Sequential Addition (or adsorption) (Rsa) algorithm. It provides Mpi parallelization and is designed to generate a high number of spheres aiming for maximal compactness, without introducing any bias. Although parallelization of such an algorithm has been successfully undertaken with shared memory (and in particular with Gpu), this is seemingly the first available implementation with distributed memory (Mpi). Our implementation successfully generated more than 12 billions of spheres over 131,072 Mpi processes in 16 seconds in dimension $d=3$.

Interactive notebooks are a precious tool for creating graphical user interfaces and teaching materials. Python and Jupyter are becoming increasingly popular in this context, with Jupyter widgets at the core of the interactive functionalities. However, while packages and libraries which offer a broad range of general-purpose widgets exist, there is limited development of specialized widgets for computational physics, chemistry and materials science. This deficiency implies significant time investments for the development of effective Jupyter notebooks for research and education in these domains. Here, we present custom Jupyter widgets that we have developed to target the needs of these communities. These widgets constitute high-quality interactive graphical components and can be employed, for example, to visualize and manipulate data, or to explore different visual representations of concepts, clarifying the relationships existing between them. In addition, we discuss with one example how similar functionality can be exposed in the form of JupyterLab extensions, modifying the JupyterLab interface for an enhanced user experience when working with applications within the targeted scientific domains.

Direct and large-eddy simulations of turbulence are often solved using explicit temporal schemes. However, this imposes very small time-steps because the eigenvalues of the (linearized) dynamical system, re-scaled by the time-step, must lie inside the stability region. In practice, fast and accurate estimations of the spectral radii of both the discrete convective and diffusive terms are therefore needed. This is virtually always done using the so-called CFL condition. On the other hand, the large heterogeneity and complexity of modern supercomputing systems are nowadays hindering the efficient cross-platform portability of CFD codes. In this regard, our leitmotiv reads: relying on a minimal set of (algebraic) kernels is crucial for code portability and maintenance! In this context, this work focuses on the computation of eigenbounds for the above-mentioned convective and diffusive matrices which are needed to determine the time-step à la CFL. To do so, a new inexpensive method, that does not require to re-construct these time-dependent matrices, is proposed and tested. It just relies on a sparse-matrix vector product where only vectors change on time. Hence, both implementation in existing codes and cross-platform portability are straightforward. The effectiveness and robustness of the method are demonstrated for different test cases on both structured Cartesian and unstructured meshes. Finally, the method is combined with a self-adaptive temporal scheme, leading to significantly larger time-steps compared with other more conventional CFL-based approaches.

In pursuit of detecting dark matter signals, the Large Hadron Collider (LHC) at CERN has conducted proton-proton collisions to probe for these elusive particles, whose existence has been supported by astronomical observations. Despite extensive efforts by the CMS and ATLAS experiments, the direct detection of dark matter signals remains elusive. The current approaches employed for analyzing dark matter signatures utilize the cut-and-count method based on conventional techniques. This study introduces an alternative method for exploring dark matter signatures by utilizing fine-tuning of pre-trained models, such as ResNet-50, on 2D histograms generated from a combination of signal + background samples and background-only samples. By utilizing various signal-to-background ratios as benchmarks, an accuracy of about 90% for a signal-to-background ratio of 0.008 is achieved. This approach not only offers a more refined search for dark matter signals but also presents an efficient and effective means of analysis using machine learning techniques.

The library provides a set of C++/Python functions for computing cross sections of ultraperipheral collisions of high energy particles under the equivalent photons approximation. Cross sections are represented through multiple integrals over the phase space. The integrals are calculated through recurrent application of algorithms for one dimensional integration. The paper contains an introduction to the theory of ultraperipheral collisions, discusses the library approach and provides a few examples of calculations.

Light absorption and radiative recombination are two critical processes in optoelectronic materials that characterize the energy conversion efficiency. The absorption and radiative coefficients are thus key properties for device optimization and design. Here, we develop a python package named pyArc that allows rigorous computation of absorption and radiative coefficients from first principles. By integrating several interpolation strategies to augment k-point sampling in reciprocal space, our code is accurate yet highly efficient. In addition to evaluation of the coefficients, our code is capable of intuitive analysis of carrier distribution, facilitating a deeper understanding of the microscopic mechanisms underlying the radiative coefficients. Utilizing GaAs as a prototypical example, we demonstrate how to employ our package to compute absorption and radiative coefficients and to investigate the key features in the electronic structure that give rise to these coefficients.

Program summary

Program Title: PyArc

CPC Library link to program files: https://doi.org/10.17632/5 × 9g9bvhcv.1

Licensing provisions: MIT license

Programming language: Python 3

Nature of problem: Light absorption and radiative recombination processes in semiconductors critically impact the energy conversion efficiency of optoelectronic devices. Developing a method to calculate coefficients of the two processes based on first-principles theory is essential, which not only can help to obtain the key properties of those semiconductor materials and guide the device design, but also can unveil the underlying microscopic mechanisms.

Solution method: PyArc, written in the Python language, implements first-principles methodologies for the computation of absorption and radiative coefficients of semiconductors based on Fermi's golden rule. This package takes the electronic eigenvalues and dipole matrix elements of a material computed from first-principles codes such as VASP as input. Dense k-point sampling for the Brillouin zone is achieved through efficient interpolation schemes implemented in our code to acquire well converged results. The functionality of cross-sectional visualization of carrier distribution in our code provides intuitive insights into the fundamental mechanism beneath the charge-carrier radiative recombination process.