Pub Date : 2024-06-26DOI: 10.1016/j.cpc.2024.109289
Michael Penwarden
{"title":"Comment on “Trans-Net: A transferable pretrained neural networks based on temporal domain decomposition for solving partial differential equations” by D. Zhang, Y. Li, and S. Ying","authors":"Michael Penwarden","doi":"10.1016/j.cpc.2024.109289","DOIUrl":"https://doi.org/10.1016/j.cpc.2024.109289","url":null,"abstract":"","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":null,"pages":null},"PeriodicalIF":7.2,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141480501","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}
Pub Date : 2024-06-21DOI: 10.1016/j.cpc.2024.109285
Francesco De Vanna , Giacomo Baldan
We present URANOS-2.0, the second major release of our massively parallel, GPU-accelerated solver for compressible wall flow applications. This latest version represents a significant leap forward in our initial tool, which was launched in 2023 (De Vanna et al. [1]), and has been specifically optimized to take full advantage of the opportunities offered by the cutting-edge pre-exascale architectures available within the EuroHPC JU. In particular, URANOS-2.0 emphasizes portability and compatibility improvements with the two top-ranked supercomputing architectures in Europe: LUMI and Leonardo. These systems utilize different GPU architectures, AMD and NVIDIA, respectively, which necessitates extensive efforts to ensure seamless usability across their distinct structures. In pursuit of this objective, the current release adheres to the OpenACC standard. This choice not only facilitates efficient utilization of the full potential inherent in these extensive GPU-based architectures but also upholds the principles of vendor neutrality, a distinctive characteristic of URANOS solvers in the CFD solvers' panorama. However, the URANOS-2.0 version goes beyond the goals of improving usability and portability; it introduces performance enhancements and restructures the most demanding computational kernels. This translates into a 2× speedup over the same architecture. In addition to its enhanced single-GPU performance, the present solver release demonstrates very good scalability in multi-GPU environments. URANOS-2.0, in fact, achieves strong scaling efficiencies of over 80% across 64 compute nodes (256 GPUs) for both LUMI and Leonardo. Furthermore, its weak scaling efficiencies reach approximately 95% and 90% on LUMI and Leonardo, respectively, when up to 256 nodes (1024 GPUs) are considered. These significant performance advancements position URANOS-2.0 as a state-of-the-art supercomputing platform tailored for compressible wall turbulence applications, establishing the solver as an integrated tool for various aerospace and energy engineering applications, which can span from direct numerical simulations, wall-resolved large eddy simulations, up to most recent wall-modeled large eddy simulations.
Program summary
Program title: Unsteady Robust All-around Navier-StOkes Solver (URANOS)
CPC Library link to program files:https://doi.org/10.17632/pw5hshn9k6.2
{"title":"URANOS-2.0: Improved performance, enhanced portability, and model extension towards exascale computing of high-speed engineering flows","authors":"Francesco De Vanna , Giacomo Baldan","doi":"10.1016/j.cpc.2024.109285","DOIUrl":"https://doi.org/10.1016/j.cpc.2024.109285","url":null,"abstract":"<div><p>We present URANOS-2.0, the second major release of our massively parallel, GPU-accelerated solver for compressible wall flow applications. This latest version represents a significant leap forward in our initial tool, which was launched in 2023 (De Vanna et al. <span>[1]</span>), and has been specifically optimized to take full advantage of the opportunities offered by the cutting-edge pre-exascale architectures available within the EuroHPC JU. In particular, URANOS-2.0 emphasizes portability and compatibility improvements with the two top-ranked supercomputing architectures in Europe: LUMI and Leonardo. These systems utilize different GPU architectures, AMD and NVIDIA, respectively, which necessitates extensive efforts to ensure seamless usability across their distinct structures. In pursuit of this objective, the current release adheres to the OpenACC standard. This choice not only facilitates efficient utilization of the full potential inherent in these extensive GPU-based architectures but also upholds the principles of vendor neutrality, a distinctive characteristic of URANOS solvers in the CFD solvers' panorama. However, the URANOS-2.0 version goes beyond the goals of improving usability and portability; it introduces performance enhancements and restructures the most demanding computational kernels. This translates into a 2× speedup over the same architecture. In addition to its enhanced single-GPU performance, the present solver release demonstrates very good scalability in multi-GPU environments. URANOS-2.0, in fact, achieves strong scaling efficiencies of over 80% across 64 compute nodes (256 GPUs) for both LUMI and Leonardo. Furthermore, its weak scaling efficiencies reach approximately 95% and 90% on LUMI and Leonardo, respectively, when up to 256 nodes (1024 GPUs) are considered. These significant performance advancements position URANOS-2.0 as a state-of-the-art supercomputing platform tailored for compressible wall turbulence applications, establishing the solver as an integrated tool for various aerospace and energy engineering applications, which can span from direct numerical simulations, wall-resolved large eddy simulations, up to most recent wall-modeled large eddy simulations.</p></div><div><h3>Program summary</h3><p><em>Program title:</em> Unsteady Robust All-around Navier-StOkes Solver (URANOS)</p><p><em>CPC Library link to program files:</em> <span>https://doi.org/10.17632/pw5hshn9k6.2</span><svg><path></path></svg></p><p><em>Developer's repository link:</em> <span>https://github.com/uranos-gpu/uranos-gpu</span><svg><path></path></svg>, <span>https://github.com/uranos-gpu/uranos-gpu/tree/v2.0</span><svg><path></path></svg></p><p><em>Licensing provisions:</em> BSD License 2.0</p><p><em>Programming language:</em> Modern Fortran, OpenACC, MPI</p><p><em>Nature of problem:</em> Solving the compressible Navier-Stokes equations in a three-dimensional Cartesian framework.</p><p><em>Solution method:</em> Convective terms ar","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":null,"pages":null},"PeriodicalIF":7.2,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S001046552400208X/pdfft?md5=7a6e04c9a2b65cdb6b3bf373bd81aed0&pid=1-s2.0-S001046552400208X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141480379","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}
Pub Date : 2024-06-20DOI: 10.1016/j.cpc.2024.109284
Jiaxing Zhao , Shuzhe Shi
The inverse power method is a numerical algorithm to obtain the eigenvectors of a matrix. In this work, we develop an iteration algorithm, based on the inverse power method, to numerically solve the Schrödinger equation that couples an arbitrary number of components. Such an algorithm can also be applied to the multi-body systems. To show the power and accuracy of this method, we also present an example of solving the Dirac equation under the presence of an external scalar potential and a constant magnetic field, with source code publicly available.
{"title":"A numerical algorithm for solving the coupled Schrödinger equations using inverse power method","authors":"Jiaxing Zhao , Shuzhe Shi","doi":"10.1016/j.cpc.2024.109284","DOIUrl":"https://doi.org/10.1016/j.cpc.2024.109284","url":null,"abstract":"<div><p>The inverse power method is a numerical algorithm to obtain the eigenvectors of a matrix. In this work, we develop an iteration algorithm, based on the inverse power method, to numerically solve the Schrödinger equation that couples an arbitrary number of components. Such an algorithm can also be applied to the multi-body systems. To show the power and accuracy of this method, we also present an example of solving the Dirac equation under the presence of an external scalar potential and a constant magnetic field, with source code publicly available.</p></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":null,"pages":null},"PeriodicalIF":7.2,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141438037","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}
Pub Date : 2024-06-14DOI: 10.1016/j.cpc.2024.109283
Guolin Wan , Yuhui Li , Ting Lai , Peixuan Li , Yongqian Zhu , Jingyu Yang , Yan-Fang Zhang , Jinbo Pan , Shixuan Du
Exploring magnetic configurations of magnets often involves utilizing the four-state method to obtain the magnetic interaction matrix, and Monte Carlo method to simulate spin textures and phase transition processes. However, computing the interaction matrix between magnetic atoms using the four-state method requires plenty of individual calculations. Despite manual simplifying the number of individual calculations based on material's symmetry is possible, there remains a necessity for an automated approach to streamline the process for high-throughput screening of magnetic materials. Meanwhile, the traditional sequential Monte Carlo simulation encounters challenges of low efficiency and long time consuming in dealing with large systems. Furthermore, the prior parallelism in the Heisenberg model was limited to parallel computation of the system's energy or run several replicas in parallel. Hence, in our pursuit of comprehensive parallelization for the Heisenberg model, we have introduced a novel adaptation of the checkerboard algorithm, enabling a fully parallelizable simulation of the Heisenberg model. To address these problems, we have developed Sym4state.jl, a program specifically designed to simplify the computation of magnetic interaction matrix and simulate spin textures under various environmental conditions. This program, available as a Julia package, can be freely accessed at https://github.com/A-LOST-WAPITI/Sym4state.jl.
Program summary
Program title: Sym4state.jl
CPC Library link to program files:https://doi.org/10.17632/s6dkmgrjfw.1
Nature of problem: Employing the four-state method to calculate magnetic interaction matrix for magnetic materials can be simplified based on material symmetry, however, there is a lack of automated approach to streamline the simplification. Additionally, the commonly used Metropolis method for simulating magnetic texture can only make parallel computation of the system's energy or run several replicas in parallel, which could hardly boost the performance when simulating the large-scale magnetic textures.
Solution method: We simplify the four-state method calculations by utilizing the principles of energy invariance under symmetry operations and time reversal operations. To enhance the efficiency of the Metropolis algorithm, we have designed a strategy to divide the entire 2D lattice into multiple domains. We then execute the Metropolis algorithm in parallel for each individual domain, thereby improving the overall computational efficiency.
Additional comments including restrictions and unusual features: While the
{"title":"Sym4state.jl: An efficient computation package for magnetic materials","authors":"Guolin Wan , Yuhui Li , Ting Lai , Peixuan Li , Yongqian Zhu , Jingyu Yang , Yan-Fang Zhang , Jinbo Pan , Shixuan Du","doi":"10.1016/j.cpc.2024.109283","DOIUrl":"10.1016/j.cpc.2024.109283","url":null,"abstract":"<div><p>Exploring magnetic configurations of magnets often involves utilizing the four-state method to obtain the magnetic interaction matrix, and Monte Carlo method to simulate spin textures and phase transition processes. However, computing the interaction matrix between magnetic atoms using the four-state method requires plenty of individual calculations. Despite manual simplifying the number of individual calculations based on material's symmetry is possible, there remains a necessity for an automated approach to streamline the process for high-throughput screening of magnetic materials. Meanwhile, the traditional sequential Monte Carlo simulation encounters challenges of low efficiency and long time consuming in dealing with large systems. Furthermore, the prior parallelism in the Heisenberg model was limited to parallel computation of the system's energy or run several replicas in parallel. Hence, in our pursuit of comprehensive parallelization for the Heisenberg model, we have introduced a novel adaptation of the checkerboard algorithm, enabling a fully parallelizable simulation of the Heisenberg model. To address these problems, we have developed <span>Sym4state.jl</span>, a program specifically designed to simplify the computation of magnetic interaction matrix and simulate spin textures under various environmental conditions. This program, available as a Julia package, can be freely accessed at <span>https://github.com/A-LOST-WAPITI/Sym4state.jl</span><svg><path></path></svg>.</p></div><div><h3>Program summary</h3><p><em>Program title:</em> Sym4state.jl</p><p><em>CPC Library link to program files:</em> <span>https://doi.org/10.17632/s6dkmgrjfw.1</span><svg><path></path></svg></p><p><em>Developer's repository link:</em> <span>https://github.com/A-LOST-WAPITI/Sym4state.jl</span><svg><path></path></svg></p><p><em>Licensing provisions:</em> MIT</p><p><em>Programming language:</em> Julia</p><p><em>Nature of problem:</em> Employing the four-state method to calculate magnetic interaction matrix for magnetic materials can be simplified based on material symmetry, however, there is a lack of automated approach to streamline the simplification. Additionally, the commonly used Metropolis method for simulating magnetic texture can only make parallel computation of the system's energy or run several replicas in parallel, which could hardly boost the performance when simulating the large-scale magnetic textures.</p><p><em>Solution method:</em> We simplify the four-state method calculations by utilizing the principles of energy invariance under symmetry operations and time reversal operations. To enhance the efficiency of the Metropolis algorithm, we have designed a strategy to divide the entire 2D lattice into multiple domains. We then execute the Metropolis algorithm in parallel for each individual domain, thereby improving the overall computational efficiency.</p><p><em>Additional comments including restrictions and unusual features:</em> While the","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":null,"pages":null},"PeriodicalIF":7.2,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141409777","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}
Pub Date : 2024-06-12DOI: 10.1016/j.cpc.2024.109281
Prakash Pandey , Sudhir K. Pandey
Exploring the topological physics of phonons is fundamentally important for understanding various practical applications. Here, we present a density-functional perturbation theory and finite displacement supercell based Python 3 software package called PH-NODE for efficiently computing phonon nodes present in real material through a first-principles approach. The present version of the code is interfaced with the WIEN2k, Elk, and ABINIT packages. In order to benchmark the code, six different types of materials are considered, which include (i) FeSi, a well-known double-Weyl point; (ii) LiCaAs, a half-Heusler single-type-I Weyl topological phonon (TP); and (iii) ScZn, coexisting nodal-line and nodal-ring TPs; (iv) TiS, six pairs of bulk Weyl nodes; (v) CdTe, type-II Weyl phonons; (vi) CsTe, coexisting TP and quadratic contact TP. In FeSi, the node points are found at and R high symmetric points. Also, there are 21 energy values at which the node points are situated, corresponding to the full Brillouin zone. For LiCaAs, the previously reported literature claims that there is a node point along the W-X high symmetry direction between the highest longitudinal acoustic and the lowest transverse optical branch, while in our DFT calculations, a gap of 0.17 meV is found. Furthermore, ScZn hosts six nodal-ring TPs phonons at the boundary planes of the Brillouin zone in the vicinity of the M high-symmetric point. In addition to this, straight-line TPs are also found along the Γ-X and Γ-R high symmetric directions. Moreover, for TiS, six Weyl node points (WP1, WP2, WP3, WP4, WP5 and WP6) are found along H-K high-symmetric direction. In CdTe, it is found that Weyl points are located along the X-W high-symmetry direction. In the case of CsTe, a TP and a quadratic contact TP are found along the Γ-X direction and at the R high-symmetry point, respectively. The results obtained from the PH-NODE code are in good agreement with the experimentally and theoretically reported data for each material.
Program summary
Program title: PH-NODE
CPC Library link to program files:https://doi.org/10.17632/sjydzn49nw.1
Licensing provisions: GNU General Public License 3.0
Programming language: Python 3
External routines/libraries: Math, Time, NumPy, SciPy
Nature of problem: Searching for the phonon-node points corresponding to the given number of phonon-branch using Nelder-Mead's simplex approach.
Solution method: We present a density-functional perturbation theory and finite displacement supercell based Python 3 software package called PH-NODE for efficiently computing phonon nodes present in real material
{"title":"PH-NODE: A DFPT and finite displacement supercell based python code for searching nodes in topological phononic materials","authors":"Prakash Pandey , Sudhir K. Pandey","doi":"10.1016/j.cpc.2024.109281","DOIUrl":"https://doi.org/10.1016/j.cpc.2024.109281","url":null,"abstract":"<div><p>Exploring the topological physics of phonons is fundamentally important for understanding various practical applications. Here, we present a density-functional perturbation theory and finite displacement supercell based Python 3 software package called PH-NODE for efficiently computing phonon nodes present in real material through a first-principles approach. The present version of the code is interfaced with the WIEN2k, Elk, and ABINIT packages. In order to benchmark the code, six different types of materials are considered, which include (i) FeSi, a well-known double-Weyl point; (ii) LiCaAs, a half-Heusler single-type-I Weyl topological phonon (TP); and (iii) ScZn, coexisting nodal-line and nodal-ring TPs; (iv) TiS, six pairs of bulk Weyl nodes; (v) CdTe, type-II Weyl phonons; (vi) CsTe, coexisting TP and quadratic contact TP. In FeSi, the node points are found at <span><math><mi>Γ</mi><mo>(</mo><mn>0</mn><mo>,</mo><mn>0</mn><mo>,</mo><mn>0</mn><mo>)</mo></math></span> and R<span><math><mo>(</mo><mn>0.5</mn><mo>,</mo><mn>0.5</mn><mo>,</mo><mn>0.5</mn><mo>)</mo></math></span> high symmetric points. Also, there are 21 energy values at which the node points are situated, corresponding to the full Brillouin zone. For LiCaAs, the previously reported literature claims that there is a node point along the W-X high symmetry direction between the highest longitudinal acoustic and the lowest transverse optical branch, while in our DFT calculations, a gap of 0.17 meV is found. Furthermore, ScZn hosts six nodal-ring TPs phonons at the boundary planes of the Brillouin zone in the vicinity of the M high-symmetric point. In addition to this, straight-line TPs are also found along the Γ-X and Γ-R high symmetric directions. Moreover, for TiS, six Weyl node points (WP1, WP2, WP3, WP4, WP5 and WP6) are found along H-K high-symmetric direction. In CdTe, it is found that Weyl points are located along the X-W high-symmetry direction. In the case of CsTe, a TP and a quadratic contact TP are found along the Γ-X direction and at the R high-symmetry point, respectively. The results obtained from the PH-NODE code are in good agreement with the experimentally and theoretically reported data for each material.</p></div><div><h3>Program summary</h3><p><em>Program title:</em> PH-NODE</p><p><em>CPC Library link to program files:</em> <span>https://doi.org/10.17632/sjydzn49nw.1</span><svg><path></path></svg></p><p><em>Licensing provisions:</em> GNU General Public License 3.0</p><p><em>Programming language:</em> Python 3</p><p><em>External routines/libraries:</em> Math, Time, NumPy, SciPy</p><p><em>Nature of problem:</em> Searching for the phonon-node points corresponding to the given number of phonon-branch using Nelder-Mead's simplex approach.</p><p><em>Solution method:</em> We present a density-functional perturbation theory and finite displacement supercell based Python 3 software package called PH-NODE for efficiently computing phonon nodes present in real material","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":null,"pages":null},"PeriodicalIF":7.2,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141434009","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}
Pub Date : 2024-06-12DOI: 10.1016/j.cpc.2024.109277
Ali Bavarchee
This article presents a novel machine learning approach for enhancing particle identification (PID) systems in high-energy physics (HEP) experiments. The proposed method utilizes a hybrid model that combines a deep neural network (DNN) and a random forest regressor (RFR), leveraging their complementary strengths. This approach achieves robust performance, leading to significantly improved particle discrimination and cleaner data for physics analysis. Our evaluation demonstrates a marked increase in PID system precision, highlighting the model's potential to optimize PID tasks in complex high-energy physics settings. By improving identification efficiency and reducing misidentification rates, this hybrid deep learning model offers valuable advancements for the field of particle physics.
{"title":"A hybrid deep learning model for optimizing particle identification systems","authors":"Ali Bavarchee","doi":"10.1016/j.cpc.2024.109277","DOIUrl":"10.1016/j.cpc.2024.109277","url":null,"abstract":"<div><p>This article presents a novel machine learning approach for enhancing particle identification (PID) systems in high-energy physics (HEP) experiments. The proposed method utilizes a hybrid model that combines a deep neural network (DNN) and a random forest regressor (RFR), leveraging their complementary strengths. This approach achieves robust performance, leading to significantly improved particle discrimination and cleaner data for physics analysis. Our evaluation demonstrates a marked increase in PID system precision, highlighting the model's potential to optimize PID tasks in complex high-energy physics settings. By improving identification efficiency and reducing misidentification rates, this hybrid deep learning model offers valuable advancements for the field of particle physics.</p></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0010465524002005/pdfft?md5=bc3127783ebc5ad20f1c938b23f60b2d&pid=1-s2.0-S0010465524002005-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141401481","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}
Pub Date : 2024-06-12DOI: 10.1016/j.cpc.2024.109279
Hareesh Chundayil , Vinay P. Majety , Armin Scrinzi
We present a new implementation of the hybrid antisymmetrized Coupled Channels (haCC) method in the framework of the tRecX (Scrinzi, 2022 [6]). The method represents atomic and molecular multi-electron functions by combining CI functions, Gaussian molecular orbitals, and a numerical single-electron basis. It is suitable for describing high harmonic generation and the strong-field dynamics of ionization. Fully differential photoemission spectra are computed by the tSurff method. The theoretical background of haCC is outlined and key improvements compared to its original formulation are highlighted. We discuss control of over-completeness resulting from the joint use of the numerical basis and Gaussian molecular orbitals by pseudo-inverses based on the Woodbury formula. Further new features of this tRecX release are the iSurff method, new input features, and the AMOS gateway interface. The mapping of haCC into the tRecX framework for solving the time-dependent Schrödinger equation is shown. Use, performance, and accuracy of haCC are discussed on the examples of high-harmonic generation and strong-field photo-emission by short laser pulses impinging on the Helium atom and on the linear molecules and CO.
Program summary
Program title: tRecX — time-dependent Recursive indeXing (tRecX=tSurff+irECS)
CPC Library link to program files:https://doi.org/10.17632/m9g2jc82sw.1
Journal Reference of previous version: A. Scrinzi, Comp. Phys. Comm., 270:108146, 2022.
Does the new version supersede the previous version: Yes
Reasons for the new version: Major new functionality: haCC — hybrid antisymmetrized coupled channels method
Summary of revisions: Main additions are haCC and iSurff. Code usage and compilation were improved.
Nature of problem: tRecX is a general solver for time-dependent Schrödinger-like problems, with applications mostly in strong field and attosecond physics. There are no technical restrictions on the spatial dimension of the problem with up to 6 spatial dimensions realized in the strong-field double ionization of Helium. Gaussian-based quantum chemical multi-electron atomic and molecular structure can be combined with the numerical basis. A selection of coordinate systems is available and any Hamiltonian involving up to second derivatives and arbitrary up to three dimensional potentials can be defined on input by simple scripts.
{"title":"The hybrid anti-symmetrized coupled channels method (haCC) for the tRecX code","authors":"Hareesh Chundayil , Vinay P. Majety , Armin Scrinzi","doi":"10.1016/j.cpc.2024.109279","DOIUrl":"https://doi.org/10.1016/j.cpc.2024.109279","url":null,"abstract":"<div><p>We present a new implementation of the hybrid antisymmetrized Coupled Channels (haCC) method in the framework of the tRecX (Scrinzi, 2022 <span>[6]</span>). The method represents atomic and molecular multi-electron functions by combining CI functions, Gaussian molecular orbitals, and a numerical single-electron basis. It is suitable for describing high harmonic generation and the strong-field dynamics of ionization. Fully differential photoemission spectra are computed by the tSurff method. The theoretical background of haCC is outlined and key improvements compared to its original formulation are highlighted. We discuss control of over-completeness resulting from the joint use of the numerical basis and Gaussian molecular orbitals by pseudo-inverses based on the Woodbury formula. Further new features of this tRecX release are the iSurff method, new input features, and the AMOS gateway interface. The mapping of haCC into the tRecX framework for solving the time-dependent Schrödinger equation is shown. Use, performance, and accuracy of haCC are discussed on the examples of high-harmonic generation and strong-field photo-emission by short laser pulses impinging on the Helium atom and on the linear molecules <span><math><msub><mrow><mi>N</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> and <em>CO</em>.</p></div><div><h3>Program summary</h3><p><em>Program title:</em> tRecX — time-dependent Recursive indeXing (tRecX=tSurff+irECS)</p><p><em>CPC Library link to program files:</em> <span>https://doi.org/10.17632/m9g2jc82sw.1</span><svg><path></path></svg></p><p><em>Developer's repository link:</em> <span>https://gitlab.physik.uni-muenchen.de/AG-Scrinzi/tRecX</span><svg><path></path></svg></p><p><em>Licensing provisions:</em> GNU General Public License 2</p><p><em>Programming language:</em> C++</p><p><em>External libraries:</em> Eigen, arpack, lapack, blas, boost, FFTW (optional)</p><p><em>Journal Reference of previous version:</em> A. Scrinzi, Comp. Phys. Comm., 270:108146, 2022.</p><p><em>Does the new version supersede the previous version:</em> Yes</p><p><em>Reasons for the new version:</em> Major new functionality: haCC — hybrid antisymmetrized coupled channels method</p><p><em>Summary of revisions:</em> Main additions are haCC and iSurff. Code usage and compilation were improved.</p><p><em>Nature of problem:</em> tRecX is a general solver for time-dependent Schrödinger-like problems, with applications mostly in strong field and attosecond physics. There are no technical restrictions on the spatial dimension of the problem with up to 6 spatial dimensions realized in the strong-field double ionization of Helium. Gaussian-based quantum chemical multi-electron atomic and molecular structure can be combined with the numerical basis. A selection of coordinate systems is available and any Hamiltonian involving up to second derivatives and arbitrary up to three dimensional potentials can be defined on input by simple scripts.</p><p><em>Solutio","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0010465524002029/pdfft?md5=4816c45cdf126acf37592d039f9ea41b&pid=1-s2.0-S0010465524002029-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141429339","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}
Pub Date : 2024-06-12DOI: 10.1016/j.cpc.2024.109280
Ulrich D. Jentschura , Ludovico T. Giorgini
The recursive Neville algorithm allows one to calculate interpolating functions recursively. Upon a judicious choice of the abscissas used for the interpolation (and extrapolation), this algorithm leads to a method for convergence acceleration. For example, one can use the Neville algorithm in order to successively eliminate inverse powers of the upper limit of the summation from the partial sums of a given, slowly convergent input series. Here, we show that, for a particular choice of the abscissas used for the extrapolation, one can replace the recursive Neville scheme by a simple one-step transformation, while also obtaining access to subleading terms for the transformed series after convergence acceleration. The matrix-based, unified formulas allow one to estimate the rate of convergence of the partial sums of the input series to their limit. In particular, Bethe logarithms for hydrogen are calculated to 100 decimal digits. Generalizations of the method to series whose remainder terms can be expanded in terms of inverse factorial series, or series with half-integer powers, are also discussed.
{"title":"Enhanced and generalized one–step Neville algorithm: Fractional powers and access to the convergence rate","authors":"Ulrich D. Jentschura , Ludovico T. Giorgini","doi":"10.1016/j.cpc.2024.109280","DOIUrl":"10.1016/j.cpc.2024.109280","url":null,"abstract":"<div><p>The recursive Neville algorithm allows one to calculate interpolating functions recursively. Upon a judicious choice of the abscissas used for the interpolation (and extrapolation), this algorithm leads to a method for convergence acceleration. For example, one can use the Neville algorithm in order to successively eliminate inverse powers of the upper limit of the summation from the partial sums of a given, slowly convergent input series. Here, we show that, for a particular choice of the abscissas used for the extrapolation, one can replace the recursive Neville scheme by a simple one-step transformation, while also obtaining access to subleading terms for the transformed series after convergence acceleration. The matrix-based, unified formulas allow one to estimate the rate of convergence of the partial sums of the input series to their limit. In particular, Bethe logarithms for hydrogen are calculated to 100 decimal digits. Generalizations of the method to series whose remainder terms can be expanded in terms of inverse factorial series, or series with half-integer powers, are also discussed.</p></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":null,"pages":null},"PeriodicalIF":7.2,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141393592","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}
Pub Date : 2024-06-11DOI: 10.1016/j.cpc.2024.109278
Claudia Fevola , Sebastian Mizera , Simon Telen
We reformulate the Landau analysis of Feynman integrals with the aim of advancing the state of the art in modern particle-physics computations. We contribute new algorithms for computing Landau singularities, using tools from polyhedral geometry and symbolic/numerical elimination. Inspired by the work of Gelfand, Kapranov, and Zelevinsky (GKZ) on generalized Euler integrals, we define the principal Landau determinant of a Feynman diagram. We illustrate with a number of examples that this algebraic formalism allows to compute many components of the Landau singular locus. We adapt the GKZ framework by carefully specializing Euler integrals to Feynman integrals. For instance, ultraviolet and infrared singularities are detected as irreducible components of an incidence variety, which project dominantly to the kinematic space. We compute principal Landau determinants for the infinite families of one-loop and banana diagrams with different mass configurations, and for a range of cutting-edge Standard Model processes. Our algorithms build on the Julia package Landau.jl and are implemented in the new open-source package PLD.jl available at https://mathrepo.mis.mpg.de/PLD/.
Program summary
Program title:PLD.jl
CPC Library link to program files:https://doi.org/10.17632/7h5644mm4n.1
Supplementary material: The repository includes the source code with documentation (PLD_code.zip), a jupyter notebook tutorial providing installation and usage instructions (PLD_notebook.zip), a database containing the output of our algorithm on 114 examples of Feynman integrals (PLD_database.zip).
Nature of problem: A fundamental challenge in scattering amplitude is to determine the values of complexified kinematic invariants for which an amplitude can develop singularities. Bjorken, Landau, and Nakanishi wrote a system of polynomial constraints, nowadays known as the Landau equations. This project aims to rigorously revisit the Landau analysis of the singularity locus of Feynman integrals with a practical view towards explicit computations.
Solution method: We define the principal Landau determinant (PLD), which is a variety inspired by the work of Gelfand, Kapranov, and Zelevinsky (GKZ). We conjecture that it provides a subset of the singularity locus, and we implement effective algorithms to compute its defining equation explicitly.
References: OSCAR [1], HomotopyContinuation.jl [2], Landau.jl [3]
{"title":"Principal Landau determinants","authors":"Claudia Fevola , Sebastian Mizera , Simon Telen","doi":"10.1016/j.cpc.2024.109278","DOIUrl":"10.1016/j.cpc.2024.109278","url":null,"abstract":"<div><p>We reformulate the Landau analysis of Feynman integrals with the aim of advancing the state of the art in modern particle-physics computations. We contribute new algorithms for computing Landau singularities, using tools from polyhedral geometry and symbolic/numerical elimination. Inspired by the work of Gelfand, Kapranov, and Zelevinsky (GKZ) on generalized Euler integrals, we define the principal Landau determinant of a Feynman diagram. We illustrate with a number of examples that this algebraic formalism allows to compute many components of the Landau singular locus. We adapt the GKZ framework by carefully specializing Euler integrals to Feynman integrals. For instance, ultraviolet and infrared singularities are detected as irreducible components of an incidence variety, which project dominantly to the kinematic space. We compute principal Landau determinants for the infinite families of one-loop and banana diagrams with different mass configurations, and for a range of cutting-edge Standard Model processes. Our algorithms build on the <span>Julia</span> package <span>Landau.jl</span> and are implemented in the new open-source package <span>PLD.jl</span> available at <span>https://mathrepo.mis.mpg.de/PLD/</span><svg><path></path></svg>.</p></div><div><h3>Program summary</h3><p><em>Program title:</em> <span>PLD.jl</span></p><p><em>CPC Library link to program files:</em> <span>https://doi.org/10.17632/7h5644mm4n.1</span><svg><path></path></svg></p><p><em>Developer's repository link:</em> <span>https://mathrepo.mis.mpg.de/PLD/</span><svg><path></path></svg></p><p><em>Licensing provisions:</em> Creative Commons by 4.0</p><p><em>Programming language:</em> <span>Julia</span></p><p><em>Supplementary material:</em> The repository includes the source code with documentation (PLD_code.zip), a jupyter notebook tutorial providing installation and usage instructions (PLD_notebook.zip), a database containing the output of our algorithm on 114 examples of Feynman integrals (PLD_database.zip).</p><p><em>Nature of problem:</em> A fundamental challenge in scattering amplitude is to determine the values of complexified kinematic invariants for which an amplitude can develop singularities. Bjorken, Landau, and Nakanishi wrote a system of polynomial constraints, nowadays known as the Landau equations. This project aims to rigorously revisit the Landau analysis of the singularity locus of Feynman integrals with a practical view towards explicit computations.</p><p><em>Solution method:</em> We define the principal Landau determinant (PLD), which is a variety inspired by the work of Gelfand, Kapranov, and Zelevinsky (GKZ). We conjecture that it provides a subset of the singularity locus, and we implement effective algorithms to compute its defining equation explicitly.</p><p><em>References:</em> OSCAR <span>[1]</span>, HomotopyContinuation.jl <span>[2]</span>, Landau.jl <span>[3]</span></p></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":null,"pages":null},"PeriodicalIF":7.2,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141410214","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号