Computer Physics Communications最新文献

{"title":"COLOSS: Complex-scaled Optical and couLOmb Scattering Solver","authors":"Junzhe Liu, Jin Lei, Zhongzhou Ren","doi":"10.1016/j.cpc.2025.109568","DOIUrl":"10.1016/j.cpc.2025.109568","url":null,"abstract":"<div><div>We introduce COLOSS, a program designed to address the scattering problem using a bound-state technique known as complex scaling. In this method, the oscillatory boundary conditions of the wave function are transformed into exponentially decaying ones, accommodating the long-range Coulomb interaction. The program implements the general local optical potential and the Perey-Buck non-local optical potential, with all potential parameters included in a well-designed input format for ease of use. The design offers users direct access to compute <em>S</em>-matrices and cross-sections for scattering processes involving a projectile of any spin interacting with a spin-0 target. We provide thorough discussions on the precision of Lagrange functions and their benefits in evaluating matrix elements. Additionally, COLOSS incorporates two distinct rotation methods, making it adaptable to potentials without analytical expressions. Comparative results demonstrate that COLOSS achieves high accuracy when compared with the direct integration method, Numerov, underscoring its utility and effectiveness in scattering calculations.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> COLOSS</div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/ph4m98rpv2.1</span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span>https://github.com/jinleiphys/COLOSS</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> GPLv3</div><div><em>Programming language:</em> Fortran</div><div><em>Nature of problem:</em> The study of elastic scattering between nuclei is a fundamental problem in nuclear physics, key to understanding nuclear interactions and structure. Traditional methods for solving the Schrödinger equation in such contexts often require imposing boundary conditions at large distances, which can be computationally challenging and prone to inaccuracies, especially for reactions involving strong Coulomb interactions and complex potentials. The complex scaling method offers a robust alternative by transforming the scattered wave function from an oscillatory to an exponentially decaying form, thus eliminating the need for boundary conditions. However, implementing this method requires careful numerical handling and validation of the analytic properties of the involved potentials, such as the Woods-Saxon function, on the complex plane. Additionally, ensuring numerical stability and accuracy across different rotational techniques and integration methods is crucial. This study addresses these challenges by developing a program that leverages the complex scaling method, providing a flexible and accurate tool for calculating elastic scattering between nuclei. The program's ability to handle various optical model potentials and its validation against established methods like Numerov underscores its utility and reliability in nuclear physics research.</div><div><e","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"311 ","pages":"Article 109568"},"PeriodicalIF":7.2,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ICPINN: Integral conservation physics-informed neural networks based on adaptive activation functions for 3D blood flow simulations","authors":"Youqiong Liu ,&nbsp;Li Cai ,&nbsp;Yaping Chen ,&nbsp;Qixing Chen","doi":"10.1016/j.cpc.2025.109569","DOIUrl":"10.1016/j.cpc.2025.109569","url":null,"abstract":"<div><div>Blood flow modeling can improve our understanding of vascular pathologies, assist in designing more effective drug delivery systems, and aid in developing safe and effective medical devices. Physics-informed neural networks (PINN) have been used to simulate blood flow by encoding the nonlinear Navier–Stokes equations and training data into the neural network. However, noninvasive, real-time and accurate acquisition of hemodynamics data remains a challenge for current invasive detection and simulation algorithms. In this paper, we propose an integral conservation physics-informed neural networks (ICPINN) with adaptive activation functions to accurately predict the velocity, pressure, and wall shear stress (WSS) based on patient-specific vessel geometries without relying on any simulation data. To achieve unsupervised learning, loss function incorporates mass flow rate residuals derived from the mass conservation law, significantly enhancing the precision and effectiveness of the predictions. Moreover, a detailed comparative analysis of various weighting coefficient selection strategies and activation functions is performed, which ultimately identifies the optimal configuration for 3D blood flow simulations that achieves the lowest relative error. Numerical results demonstrate that the proposed ICPINN framework enables accurate prediction of blood flow in realistic cardiovascular geometry, and that mass flow rate is essential for complex structures, such as bifurcations, U-bend, stenosis, and aneurysms, offering potential applications in medical diagnostics and treatment planning.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"311 ","pages":"Article 109569"},"PeriodicalIF":7.2,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accurate simulation of the anisotropic dendrite crystal growth by the 3DVar data assimilation","authors":"Fenglian Zheng,&nbsp;Xufeng Xiao","doi":"10.1016/j.cpc.2025.109571","DOIUrl":"10.1016/j.cpc.2025.109571","url":null,"abstract":"<div><div>The growth phenomenon of dendritic crystals is a common occurrence in nature, forming a structure similar to tree branches during its evolution. However, in practical computations, model parameters and initial conditions may have observational errors, which cause large errors in numerical simulation results. To improve the accuracy and efficiency of numerical simulation, this study uses a three-dimensional variational (3DVar) data assimilation algorithm. We consider using the phase-field dendritic crystal growth (PF-DCG) model as the governing equation for numerical simulation. Through the optimization problem of 3DVar, we will incorporate the observed solutions from experimental data into the process of solving numerical solutions to modify them, thereby achieving the goal of data assimilation. This study mainly evaluates two different categories of problems: initial observational errors and model parameter errors. In the numerical experiment section, we obtain the numerical solution by using the operator splitting method (OSM) and explore the effectiveness of this method and investigate the influence of various factors such as adjustment factors, spatio-temporal sampling rates, and parameter perturbation ratios on the effectiveness of data assimilation. The experimental results show that this method can effectively assimilate the observation data, thus accurately simulating the growth process of dendritic crystals.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"311 ","pages":"Article 109571"},"PeriodicalIF":7.2,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-order implicit solver in conservative formulation for tokamak plasma transport equations","authors":"Andrei Ludvig-Osipov ,&nbsp;Dmytro Yadykin ,&nbsp;Pär Strand","doi":"10.1016/j.cpc.2025.109570","DOIUrl":"10.1016/j.cpc.2025.109570","url":null,"abstract":"<div><div>An efficient numerical scheme for solving transport equations for tokamak plasmas within an integrated modelling framework is presented. The plasma transport equations are formulated as diffusion-advection equations in two coordinates (one temporal and one spatial) featuring stiff non-linearities. The presented numerical scheme aims to minimise computational costs, which are associated with repeated calls of numerically expensive physical models in a processes of time stepping and non-linear convergence within an integrated modelling framework. The spatial discretisation is based on the 4th order accurate Interpolated Differential Operator in Conservative Formulation, the time-stepping method is the 2nd order accurate implicit Runge-Kutta scheme, and an under-relaxed Picard iteration is used for accelerating non-linear convergence. Temporal and spatial accuracies of the scheme allow for coarse grids, and the implicit time-stepping method together with the non-linear convergence approach contributes to robust and fast non-linear convergence. The spatial discretisation method enforces conservation in spatial coordinate up to the machine precision. The numerical scheme demonstrates accurate, stable and fast non-linear convergence in numerical tests using analytical stiff transport model. In particular, the 2nd order accuracy in time stepping significantly improves the overall convergence properties and the accuracy of simulating transient processes in comparison to the 1st order schemes.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"311 ","pages":"Article 109570"},"PeriodicalIF":7.2,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PyFR v2.0.3: Towards industrial adoption of scale-resolving simulations","authors":"Freddie D. Witherden ,&nbsp;Peter E. Vincent ,&nbsp;Will Trojak ,&nbsp;Yoshiaki Abe ,&nbsp;Amir Akbarzadeh ,&nbsp;Semih Akkurt ,&nbsp;Mohammad Alhawwary ,&nbsp;Lidia Caros ,&nbsp;Tarik Dzanic ,&nbsp;Giorgio Giangaspero ,&nbsp;Arvind S. Iyer ,&nbsp;Antony Jameson ,&nbsp;Marius Koch ,&nbsp;Niki Loppi ,&nbsp;Sambit Mishra ,&nbsp;Rishit Modi ,&nbsp;Gonzalo Sáez-Mischlich ,&nbsp;Jin Seok Park ,&nbsp;Brian C. Vermeire ,&nbsp;Lai Wang","doi":"10.1016/j.cpc.2025.109567","DOIUrl":"10.1016/j.cpc.2025.109567","url":null,"abstract":"<div><div>PyFR is an open-source cross-platform computational fluid dynamics framework based on the high-order Flux Reconstruction approach, specifically designed for undertaking high-accuracy scale-resolving simulations in the vicinity of complex engineering geometries. Since the initial release of PyFR v0.1.0 in 2013, a range of new capabilities have been added to the framework, with a view to enabling industrial adoption. In this work, we provide details of these enhancements as released in PyFR v2.0.3, including improvements to cross-platform performance (new backends, extensions of the DSL, new matrix multiplication providers, improvements to the data layout, use of task graphs) and improvements to numerical stability (modal filtering, anti-aliasing, artificial viscosity, entropy filtering), as well as the addition of prismatic, tetrahedral and pyramid shaped elements, improved domain decomposition support for mixed element grids, improved handling of curved element meshes, the addition of an adaptive time-stepping capability, the addition of incompressible Euler and Navier-Stokes solvers, improvements to file formats and the development of a plugin architecture. We also explain efforts to grow an engaged developer and user community and provided a range of examples that show how our user base is applying PyFR to solve a wide range of fundamental, applied and industrial flow problems. Finally, we demonstrate the accuracy of PyFR v2.0.3 for a supersonic Taylor-Green vortex case, with shocks and turbulence, and provided latest performance and scaling results on up to 1024 AMD Instinct MI250X accelerators of Frontier at ORNL (each with two GCDs) and up to 2048 Nvidia GH200 GPUs of Alps at CSCS. We note that absolute performance of PyFR accounting for the totality of both hardware and software improvements has, conservatively, increased by almost 50× over the last decade.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> PyFR</div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/vmgh4kfjk6.1</span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span>https://github.com/PyFR/PyFR</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> BSD 3-clause</div><div><em>Programming language:</em> Python (generating C/OpenMP, CUDA, OpenCL, HIP, Metal)</div><div><em>Nature of problem:</em> Accurate and efficient scale-resolving simulation of industrial flows.</div><div><em>Solution method:</em> Massively parallel cross-platform implementation of high-order accurate Flux Reconstruction schemes.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"311 ","pages":"Article 109567"},"PeriodicalIF":7.2,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy network for state estimation with random sensors and sparse labels","authors":"Yash Kumar ,&nbsp;Tushar ,&nbsp;Souvik Chakraborty","doi":"10.1016/j.cpc.2025.109566","DOIUrl":"10.1016/j.cpc.2025.109566","url":null,"abstract":"<div><div>State estimation is imperative while dealing with high-dimensional dynamical systems due to the unavailability of complete measurements. It plays a pivotal role in gaining insights, executing control, or optimizing design tasks. However, many deep learning approaches are constrained by the requirement for high-resolution labels and fixed sensor locations, limiting their practical applicability. To address these limitations, we propose a novel approach featuring an implicit optimization layer and a physics-based loss function capable of learning from sparse labels. This approach operates by minimizing the energy of neural network predictions, thereby accommodating varying sensor counts and locations. Our methodology is validated through the application of these models to two high-dimensional fluid problems: Burgers' equation and Flow Past Cylinder. Notably, our model exhibits robustness against noise in measurements, underscoring its effectiveness in practical scenarios.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"311 ","pages":"Article 109566"},"PeriodicalIF":7.2,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"New version of ZKCM, a C++ multiprecision matrix library usable for numerical studies of quantum information","authors":"Akira SaiToh","doi":"10.1016/j.cpc.2025.109564","DOIUrl":"10.1016/j.cpc.2025.109564","url":null,"abstract":"<div><div>Recent improvements in the ZKCM and ZKCM_QC libraries are presented in this announcement. ZKCM was released as a C++ library for multiprecision matrix computation and ZKCM_QC was developed as its extension for matrix-product-state (MPS) simulation of quantum circuits. Their parallel processing extensions using OpenMP and CUDA were briefly reported in a previous contribution [A. SaiToh, to appear in Proc. CCP2023]. Here, their most recent developments are reported, which include the employments of advanced FFT and Moore-Penrose inverse routines.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"311 ","pages":"Article 109564"},"PeriodicalIF":7.2,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive mesh refinement algorithm for CESE schemes on unstructured quadrilateral meshes","authors":"Lisong Shi ,&nbsp;Chaoxiong Zhang ,&nbsp;Chih-Yung Wen","doi":"10.1016/j.cpc.2025.109565","DOIUrl":"10.1016/j.cpc.2025.109565","url":null,"abstract":"<div><div>This study introduces the development of space-time Conservation Element and Solution Element (CESE) methods tailored for adaptive unstructured quadrilateral meshes. An efficient algorithm is then proposed to manage the mesh adaptation process for these staggered schemes, utilizing a unique cell-tree-vertex data structure. This structure accelerates the construction of conservation elements and simplifies the interconnection of computational cells, enabling a flexible approach for handling adaptive mesh refinement in complex computational domains. The integration of second-order <em>a</em>-<em>α</em>, Courant number-insensitive, and upwind CESE schemes with this adaptation algorithm is demonstrated. Numerical simulations of compressible inviscid flows are conducted to validate the global conservation property, ensure second-order accuracy across interfaces at different refinement levels, and evaluate the effectiveness of the extended schemes and adaptation algorithm.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"311 ","pages":"Article 109565"},"PeriodicalIF":7.2,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143511373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling of heterogeneous catalytic reactions with the simulation tool PICLas","authors":"S. Lauterbach,&nbsp;S. Fasoulas,&nbsp;M. Pfeiffer","doi":"10.1016/j.cpc.2025.109560","DOIUrl":"10.1016/j.cpc.2025.109560","url":null,"abstract":"<div><div>The gas-surface interaction model of the open-source gas and plasma simulation tool PICLas has been extended for the simulation of catalytic reactions. A variety of reaction mechanisms have been implemented, including multiple adsorption models, desorption, the Eley-Rideal and the Langmuir-Hinshelwood mechanism. Modeling is based upon macroscopic reaction data and parameters derived from experiments or ab-initio quantum calculations. The implementation has been validated through a comparison to analytical reaction rates. Simulations of the carbon monoxide and oxygen reaction network on a Pd(111) surface are performed and compared to experimental data obtained by temperature-programmed desorption spectra and molecular beam measurements. The results show good agreement with the measurement data.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"311 ","pages":"Article 109560"},"PeriodicalIF":7.2,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143511374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Functional analytic derivation and CP2K implementation of the SCCS model based on the solvent-aware interface","authors":"Ziwei Chai,&nbsp;Sandra Luber","doi":"10.1016/j.cpc.2025.109563","DOIUrl":"10.1016/j.cpc.2025.109563","url":null,"abstract":"<div><div>In the self-consistent continuum solvation (SCCS) approach (<em>J. Chem. Phys.</em> 136, 064102 (2012)), the analytical expressions of the local solute-solvent interface functions determine the interface function and dielectric function values at a given real space position based solely on the electron density at that position, completely disregarding the surrounding electron density distribution. Therefore, the low electron density areas inside the solute will be identified by the algorithm as regions where implicit solvent exists, resulting in the emergence of non-physical implicit solvent regions within the solute and even potentially leading to the divergence catastrophe of Kohn-Sham SCF calculations. We present a new and efficient SCCS implementation based on the solvent-aware interface (<em>J. Chem. Theory Comput.</em> 15, 3, 1996–2009 (2019)) which addresses this issue by utilizing a solute-solvent interface function based on convolution of electron density in the CP2K software package, which is based on the mixed Gaussian and plane waves (GPW) approach. Starting with the foundational formulas of SCCS, we have rigorously derived the contributions of the newly defined electrostatic energy to the Kohn-Sham potential and the analytical forces. This comprehensive derivation, which to the best of our knowledge is not available in the current literature, utilizes the updated versions of the solute-solvent interface function and the dielectric function, tailored to align with the specifics of the GPW implementation. Our implementation has been tested to successfully eliminate non-physical implicit solvent regions within the solute and achieve good SCF convergence, as demonstrated by test results for both bulk and surface models, namely liquid H₂O, titanium dioxide, and platinum.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"311 ","pages":"Article 109563"},"PeriodicalIF":7.2,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}