Pub Date : 2025-12-16DOI: 10.1016/j.cpc.2025.109991
Haritz Garai-Marin , María Blanco-Rey , Idoia G. Gurtubay , Jon Lafuente-Bartolome , Asier Eiguren
<div><div>We present <span>INTW</span>, a modular software environment designed for advanced electronic structure calculations. Developed in Fortran95, <span>INTW</span> is capable of reading self-consistent field (SCF) results, such as electron energies, wave functions, and potentials, generated by the <span>Quantum ESPRESSO</span> and <span>SIESTA</span> codes. Using these SCF results as input, <span>INTW</span> provides a suite of specialized subroutines and functions for the computation of various electron- and phonon-related physical properties, facilitating detailed analysis of material properties at the quantum level. <span>INTW</span> particularly stands out in its treatment of symmetry, fully exploiting it even when dealing with electron spinor wave functions. Furthermore, it can efficiently work with both localized basis set codes, such as <span>SIESTA</span>, and plane-wave codes like <span>Quantum ESPRESSO</span>. These capabilities make <span>INTW</span> unique, offering a versatile approach that effectively combines the use of symmetry with both localized basis sets and plane-wave methods.</div><div><strong>Program summary</strong></div><div><em>Program Title:</em> <span>INTW</span></div><div><em>CPC Library link to program files:</em> (to be added by Technical Editor)</div><div><em>Developer’s repository link:</em> <span><span>https://github.com/eiguren/intw</span><svg><path></path></svg></span></div><div><em>Code Ocean capsule:</em> (to be added by Technical Editor)</div><div><em>Licensing provisions:</em> GPL-3.0-or-later</div><div><em>Programming language:</em> Fortran95</div><div><em>Nature of problem:</em></div><div>Accessing advanced electronic structure problems, such as the anisotropic electron-phonon interaction on the Fermi surface, requires efficient treatment of the data generated by general-purpose codes such as <span>Quantum ESPRESSO</span> and <span>SIESTA</span>. Moreover, fully exploiting symmetry operations is challenging but offers significant efficiency gains and qualitative benefits. The problem is to provide a modular framework that enables such calculations in a flexible, symmetry-aware, and computationally efficient environment set of tools.</div><div><em>Solution method:</em></div><div>Electron and phonon states are calculated only in the irreducible Brillouin zone provided by <span>Quantum ESPRESSO</span> and <span>SIESTA</span>. <span>INTW</span> interfaces with these codes to generate electron (spinor) states and phonon induced (spinor) potentials at arbitrary momenta using symmetry operations. <span>INTW</span> efficiently calculates the nearest-neighbor overlap matrices for Wannier functions by exploiting symmetry. In <span>SIESTA</span>, phonons are calculated using the supercell method, although <span>INTW</span> computes only the irreducible atomic displacements needed to construct the force-constant matrix. The electron-phonon matrix elements are computed either (1) by Fourier interpolation of the
{"title":"INTW: A versatile modular environment for advanced treatment of electronic structure and electron-phonon related properties","authors":"Haritz Garai-Marin , María Blanco-Rey , Idoia G. Gurtubay , Jon Lafuente-Bartolome , Asier Eiguren","doi":"10.1016/j.cpc.2025.109991","DOIUrl":"10.1016/j.cpc.2025.109991","url":null,"abstract":"<div><div>We present <span>INTW</span>, a modular software environment designed for advanced electronic structure calculations. Developed in Fortran95, <span>INTW</span> is capable of reading self-consistent field (SCF) results, such as electron energies, wave functions, and potentials, generated by the <span>Quantum ESPRESSO</span> and <span>SIESTA</span> codes. Using these SCF results as input, <span>INTW</span> provides a suite of specialized subroutines and functions for the computation of various electron- and phonon-related physical properties, facilitating detailed analysis of material properties at the quantum level. <span>INTW</span> particularly stands out in its treatment of symmetry, fully exploiting it even when dealing with electron spinor wave functions. Furthermore, it can efficiently work with both localized basis set codes, such as <span>SIESTA</span>, and plane-wave codes like <span>Quantum ESPRESSO</span>. These capabilities make <span>INTW</span> unique, offering a versatile approach that effectively combines the use of symmetry with both localized basis sets and plane-wave methods.</div><div><strong>Program summary</strong></div><div><em>Program Title:</em> <span>INTW</span></div><div><em>CPC Library link to program files:</em> (to be added by Technical Editor)</div><div><em>Developer’s repository link:</em> <span><span>https://github.com/eiguren/intw</span><svg><path></path></svg></span></div><div><em>Code Ocean capsule:</em> (to be added by Technical Editor)</div><div><em>Licensing provisions:</em> GPL-3.0-or-later</div><div><em>Programming language:</em> Fortran95</div><div><em>Nature of problem:</em></div><div>Accessing advanced electronic structure problems, such as the anisotropic electron-phonon interaction on the Fermi surface, requires efficient treatment of the data generated by general-purpose codes such as <span>Quantum ESPRESSO</span> and <span>SIESTA</span>. Moreover, fully exploiting symmetry operations is challenging but offers significant efficiency gains and qualitative benefits. The problem is to provide a modular framework that enables such calculations in a flexible, symmetry-aware, and computationally efficient environment set of tools.</div><div><em>Solution method:</em></div><div>Electron and phonon states are calculated only in the irreducible Brillouin zone provided by <span>Quantum ESPRESSO</span> and <span>SIESTA</span>. <span>INTW</span> interfaces with these codes to generate electron (spinor) states and phonon induced (spinor) potentials at arbitrary momenta using symmetry operations. <span>INTW</span> efficiently calculates the nearest-neighbor overlap matrices for Wannier functions by exploiting symmetry. In <span>SIESTA</span>, phonons are calculated using the supercell method, although <span>INTW</span> computes only the irreducible atomic displacements needed to construct the force-constant matrix. The electron-phonon matrix elements are computed either (1) by Fourier interpolation of the ","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"320 ","pages":"Article 109991"},"PeriodicalIF":3.4,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880066","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 : 2025-12-16DOI: 10.1016/j.cpc.2025.109992
Navaneet Villodi, Prabhu Ramachandran
The accuracy of meshless methods like Smoothed Particle Hydrodynamics (SPH) is highly dependent on the quality of the particle distribution. Existing particle initialization techniques often struggle to simultaneously achieve adaptive resolution, handle intricate boundaries, and efficiently generate well-packed distributions inside and outside a boundary. This work presents a fast and robust particle initialization method that achieves these goals using standard SPH building blocks. Our approach enables simultaneous initialization of fluid and solid regions, supports arbitrary geometries, and achieves high-quality, quasi-uniform particle arrangements without complex procedures like surface bonding. Extensive results in both 2D and 3D demonstrate that the obtained particle distributions exhibit good boundary conformity, low spatial disorder, and minimal density variation, all with significantly reduced computational cost compared to existing approaches. This work paves the way for automated particle initialization to accurately model flow in and around bodies with meshless methods, particularly with SPH.
{"title":"Rapid variable resolution particle initialization for complex geometries","authors":"Navaneet Villodi, Prabhu Ramachandran","doi":"10.1016/j.cpc.2025.109992","DOIUrl":"10.1016/j.cpc.2025.109992","url":null,"abstract":"<div><div>The accuracy of meshless methods like Smoothed Particle Hydrodynamics (SPH) is highly dependent on the quality of the particle distribution. Existing particle initialization techniques often struggle to simultaneously achieve adaptive resolution, handle intricate boundaries, and efficiently generate well-packed distributions inside and outside a boundary. This work presents a fast and robust particle initialization method that achieves these goals using standard SPH building blocks. Our approach enables simultaneous initialization of fluid and solid regions, supports arbitrary geometries, and achieves high-quality, quasi-uniform particle arrangements without complex procedures like surface bonding. Extensive results in both 2D and 3D demonstrate that the obtained particle distributions exhibit good boundary conformity, low spatial disorder, and minimal density variation, all with significantly reduced computational cost compared to existing approaches. This work paves the way for automated particle initialization to accurately model flow in and around bodies with meshless methods, particularly with SPH.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"320 ","pages":"Article 109992"},"PeriodicalIF":3.4,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880485","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 : 2025-12-14DOI: 10.1016/j.cpc.2025.109969
Lucas Kotz , Aurore Courtoy , T.J. Hobbs , Pavel Nadolsky , Fredrick Olness , Maximiliano Ponce-Chavez , Varada Purohit
Fantômas is a C++ toolkit for exploring the parametrization dependence of parton distribution functions (PDFs) and other correlator functions in quantum chromodynamics (QCD). Fantômas facilitates the generation of adaptable polynomial parametrizations for PDFs, called metamorphs, to find best-fit PDF solutions and quantify the epistemic uncertainty associated with the parametrizations during their fitting. The method employs Bézier curves as universal approximators for a variety of PDF shapes. Integrated into the xFitter framework for the global QCD analysis, Fantômas provides a foundation for general models of PDFs, while reducing the computational time compared to the approaches utilizing traditional polynomial parametrizations as well as providing an interpretable alternative to neural-network-based models. This paper outlines the structure and practical usage of the Fantômas toolkit, including its inputs, outputs, and implementation within xFitter. It also provides a practical example of using Fantômas for uncertainty quantification as well as the combination of PDF fits into a single ensemble.
{"title":"Fantômas unconfined: global QCD fits with Bézier parameterizations","authors":"Lucas Kotz , Aurore Courtoy , T.J. Hobbs , Pavel Nadolsky , Fredrick Olness , Maximiliano Ponce-Chavez , Varada Purohit","doi":"10.1016/j.cpc.2025.109969","DOIUrl":"10.1016/j.cpc.2025.109969","url":null,"abstract":"<div><div><span>Fantômas</span> is a C++ toolkit for exploring the parametrization dependence of parton distribution functions (PDFs) and other correlator functions in quantum chromodynamics (QCD). <span>Fantômas</span> facilitates the generation of adaptable polynomial parametrizations for PDFs, called metamorphs, to find best-fit PDF solutions and quantify the epistemic uncertainty associated with the parametrizations during their fitting. The method employs Bézier curves as universal approximators for a variety of PDF shapes. Integrated into the <span>xFitter</span> framework for the global QCD analysis, <span>Fantômas</span> provides a foundation for general models of PDFs, while reducing the computational time compared to the approaches utilizing traditional polynomial parametrizations as well as providing an interpretable alternative to neural-network-based models. This paper outlines the structure and practical usage of the <span>Fantômas</span> toolkit, including its inputs, outputs, and implementation within <span>xFitter</span>. It also provides a practical example of using <span>Fantômas</span> for uncertainty quantification as well as the combination of PDF fits into a single ensemble.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"320 ","pages":"Article 109969"},"PeriodicalIF":3.4,"publicationDate":"2025-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880064","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 : 2025-12-13DOI: 10.1016/j.cpc.2025.109994
Hongfu Huang , Junhao Peng , Kaiqi Li , Jian Zhou , Zhimei Sun
Machine-learning interatomic potentials (MLIPs) such as neuroevolution potentials (NEP) combine quantum-mechanical accuracy with computational efficiency significantly accelerate atomistic dynamic simulations. Trained by derivative-free optimization, the normal NEP achieves good accuracy, but suffers from inefficiency due to the high-dimensional parameter search. To overcome this problem, we present a gradient-optimized NEP (GNEP) training framework employing explicit analytical gradients and the Adam optimizer. This approach greatly improves training efficiency and convergence speedily while maintaining accuracy. By applying GNEP to the training of Sb-Te material systems (datasets include crystalline, liquid, and disordered phases), the fitting time has been substantially reduced—often by orders of magnitude—compared to the NEP training framework. The fitted potentials are validated by DFT reference calculations, demonstrating satisfactory agreement in equation of state and radial distribution functions. These results confirm that GNEP retains high predictive accuracy and transferability while considerably improved computational efficiency.
{"title":"Efficient GPU-accelerated training of a neuroevolution potential with analytical gradients","authors":"Hongfu Huang , Junhao Peng , Kaiqi Li , Jian Zhou , Zhimei Sun","doi":"10.1016/j.cpc.2025.109994","DOIUrl":"10.1016/j.cpc.2025.109994","url":null,"abstract":"<div><div>Machine-learning interatomic potentials (MLIPs) such as neuroevolution potentials (NEP) combine quantum-mechanical accuracy with computational efficiency significantly accelerate atomistic dynamic simulations. Trained by derivative-free optimization, the normal NEP achieves good accuracy, but suffers from inefficiency due to the high-dimensional parameter search. To overcome this problem, we present a gradient-optimized NEP (GNEP) training framework employing explicit analytical gradients and the Adam optimizer. This approach greatly improves training efficiency and convergence speedily while maintaining accuracy. By applying GNEP to the training of Sb-Te material systems (datasets include crystalline, liquid, and disordered phases), the fitting time has been substantially reduced—often by orders of magnitude—compared to the NEP training framework. The fitted potentials are validated by DFT reference calculations, demonstrating satisfactory agreement in equation of state and radial distribution functions. These results confirm that GNEP retains high predictive accuracy and transferability while considerably improved computational efficiency.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"320 ","pages":"Article 109994"},"PeriodicalIF":3.4,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786469","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 : 2025-12-11DOI: 10.1016/j.cpc.2025.109989
Luciano G. Silvestri, Zachary A. Johnson, Michael S. Murillo
We present a systematic framework for shortening and automating molecular dynamics equilibration through improved position initialization methods and uncertainty quantification analysis, using the Yukawa one-component plasma as an exemplar system. Our comprehensive evaluation of seven initialization approaches (uniform random, uniform random with rejection, Halton and Sobol sequences, perfect and perturbed lattices, and a Monte Carlo pair distribution method) demonstrates that initialization significantly impacts equilibration efficiency, with microfield distribution analysis providing diagnostic insights into thermal behaviors. Our results establish that initialization method selection is relatively inconsequential at low coupling strengths, while physics-informed methods demonstrate superior performance at high coupling strengths, reducing equilibration time. We establish direct relationships between temperature stability and uncertainties in transport properties (diffusion coefficient and viscosity), comparing thermostating protocols including ON-OFF versus OFF-ON duty cycles, Berendsen versus Langevin thermostats, and thermostat coupling strengths. Our findings demonstrate that weaker thermostat coupling generally requires fewer equilibration cycles, and OFF-ON thermostating sequences outperform ON-OFF approaches for most initialization methods. The methodology implements temperature forecasting as a quantitative metric for system thermalization, enabling users to determine equilibration adequacy based on specified uncertainty tolerances in desired output properties, thus transforming equilibration from a heuristic process to a rigorously quantifiable procedure with clear termination criteria.
{"title":"Adaptive equilibration of molecular dynamics simulations","authors":"Luciano G. Silvestri, Zachary A. Johnson, Michael S. Murillo","doi":"10.1016/j.cpc.2025.109989","DOIUrl":"10.1016/j.cpc.2025.109989","url":null,"abstract":"<div><div>We present a systematic framework for shortening and automating molecular dynamics equilibration through improved position initialization methods and uncertainty quantification analysis, using the Yukawa one-component plasma as an exemplar system. Our comprehensive evaluation of seven initialization approaches (uniform random, uniform random with rejection, Halton and Sobol sequences, perfect and perturbed lattices, and a Monte Carlo pair distribution method) demonstrates that initialization significantly impacts equilibration efficiency, with microfield distribution analysis providing diagnostic insights into thermal behaviors. Our results establish that initialization method selection is relatively inconsequential at low coupling strengths, while physics-informed methods demonstrate superior performance at high coupling strengths, reducing equilibration time. We establish direct relationships between temperature stability and uncertainties in transport properties (diffusion coefficient and viscosity), comparing thermostating protocols including ON-OFF versus OFF-ON duty cycles, Berendsen versus Langevin thermostats, and thermostat coupling strengths. Our findings demonstrate that weaker thermostat coupling generally requires fewer equilibration cycles, and OFF-ON thermostating sequences outperform ON-OFF approaches for most initialization methods. The methodology implements temperature forecasting as a quantitative metric for system thermalization, enabling users to determine equilibration adequacy based on specified uncertainty tolerances in desired output properties, thus transforming equilibration from a heuristic process to a rigorously quantifiable procedure with clear termination criteria.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"320 ","pages":"Article 109989"},"PeriodicalIF":3.4,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880487","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 : 2025-12-11DOI: 10.1016/j.cpc.2025.109987
Esteban Cisneros–Garibay , Henry Le Berre , Dimitrios Adam , Spencer H. Bryngelson , Jonathan B. Freund
The cost of combustion simulations is often dominated by the evaluation of net production rates of chemical species and mixture thermodynamics (thermochemistry). Execution on computing accelerators (XPUs) such as graphics processing units (GPUs) can greatly reduce this cost. Established thermochemistry software is not readily portable to such devices, as it sacrifices valuable analytical forms that enable differentiation, sensitivity analysis, and implicit time integration. Symbolic abstractions are developed with corresponding transformations that enable computation on accelerators and automatic differentiation by avoiding premature specification of detail. The software package Pyrometheus is introduced as an implementation of these abstractions and their transformations for combustion thermochemistry. The formulation facilitates code generation from the symbolic representation of a specific thermochemical mechanism in multiple target languages, including Python, C++, and Fortran. The generated code processes array-valued expressions, but does not specify their semantics. The semantics are provided by compatible array libraries, including NumPy, Pytato, and Google JAX. Thus, the generated code retains a symbolic representation of the thermochemistry, which enables computation on accelerators and CPUs and facilitates automatic differentiation. The design and operation of the symbolic abstractions and their companion tool, Pyrometheus, are discussed throughout. Roofline demonstrations show that the computation of chemical source terms within MFC, a Fortran-based flow solver we link to Pyrometheus, is performant.
{"title":"Pyrometheus: Symbolic abstractions for XPU and automatically differentiated computation of combustion kinetics and thermodynamics","authors":"Esteban Cisneros–Garibay , Henry Le Berre , Dimitrios Adam , Spencer H. Bryngelson , Jonathan B. Freund","doi":"10.1016/j.cpc.2025.109987","DOIUrl":"10.1016/j.cpc.2025.109987","url":null,"abstract":"<div><div>The cost of combustion simulations is often dominated by the evaluation of net production rates of chemical species and mixture thermodynamics (thermochemistry). Execution on computing accelerators (XPUs) such as graphics processing units (GPUs) can greatly reduce this cost. Established thermochemistry software is not readily portable to such devices, as it sacrifices valuable analytical forms that enable differentiation, sensitivity analysis, and implicit time integration. Symbolic abstractions are developed with corresponding transformations that enable computation on accelerators and automatic differentiation by avoiding premature specification of detail. The software package Pyrometheus is introduced as an implementation of these abstractions and their transformations for combustion thermochemistry. The formulation facilitates code generation from the symbolic representation of a specific thermochemical mechanism in multiple target languages, including Python, C<strong>++</strong>, and Fortran. The generated code processes array-valued expressions, but does not specify their semantics. The semantics are provided by compatible array libraries, including NumPy, Pytato, and Google JAX. Thus, the generated code retains a symbolic representation of the thermochemistry, which enables computation on accelerators and CPUs and facilitates automatic differentiation. The design and operation of the symbolic abstractions and their companion tool, Pyrometheus, are discussed throughout. Roofline demonstrations show that the computation of chemical source terms within MFC, a Fortran-based flow solver we link to Pyrometheus, is performant.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"320 ","pages":"Article 109987"},"PeriodicalIF":3.4,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145836438","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 : 2025-12-10DOI: 10.1016/j.cpc.2025.109970
Miguel Correia , Mathieu Giroux , Sebastian Mizera
We introduce , a Mathematica package that automatizes the computation of singularities of Feynman integrals, based on new theoretical understanding of their analytic structure. Given a Feynman diagram, generates a list of potential singularities along with a candidate symbol alphabet. The package also provides a comprehensive set of tools for analyzing the analytic properties of Feynman integrals and related objects, such as cosmological and energy correlators. We showcase its capabilities by reproducing known results and predicting singularities and symbol alphabets of Feynman integrals at and beyond the high-precision frontier.
Program Summary
Program title: SOFIA (Singularities of Feynman Integrals Automatized)
CPC Library link to program files:https://doi.org/10.17632/3nnz2mr5wx.1
Supplementary material: The example file SOFIA_examples.nb in SOFIA GitHub [1].
Nature of problem: This paper makes a significant contribution to perturbative computations in particle physics and related fields, by introducing a novel computer package analyzing the singularity structure of these integrals. In practice, this information can be leveraged to derive differential equations, the solutions of which enable efficient computation of the Feynman integrals, a crucial step that has been a major bottleneck in high-precision QCD computations. We believe this paper pushes the field of Feynman integrals into a new direction. The key insights that enabled this work were based on applications of cutting-edge techniques from algebraic geometry. The versatility of the package we introduce means it can be also used in other applications, including computations of cosmological and energy-energy correlators, as well as post-Minkowski expansion of gravitational potentials.
Solution method: Based on new theoretical insights, it provides an easy-to-use open-source tool for multi-loop computations in perturbation theory. In the paper, we demonstrated it can be used in diverse applications, including perturbative Standard Model computations, computations of cosmological and energy-energy correlators, as well as post-Minkowski expansions of gravitational potentials. Given the high degree of automation and broad scope of applications, we think the paper would be a good fit in CPC.
{"title":"SOFIA: Singularities of Feynman integrals automatized","authors":"Miguel Correia , Mathieu Giroux , Sebastian Mizera","doi":"10.1016/j.cpc.2025.109970","DOIUrl":"10.1016/j.cpc.2025.109970","url":null,"abstract":"<div><div>We introduce <figure><img></figure>, a <span>Mathematica</span> package that automatizes the computation of singularities of Feynman integrals, based on new theoretical understanding of their analytic structure. Given a Feynman diagram, <figure><img></figure>generates a list of potential singularities along with a candidate symbol alphabet. The package also provides a comprehensive set of tools for analyzing the analytic properties of Feynman integrals and related objects, such as cosmological and energy correlators. We showcase its capabilities by reproducing known results and predicting singularities and symbol alphabets of Feynman integrals at and beyond the high-precision frontier.</div></div><div><h3>Program Summary</h3><div><em>Program title</em>: <span>SOFIA</span> (Singularities of Feynman Integrals Automatized)</div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/3nnz2mr5wx.1</span><svg><path></path></svg></span></div><div><em>Developer’s repository link</em>: <span><span>https://github.com/StrangeQuark007/SOFIA</span><svg><path></path></svg></span> [1]</div><div><em>Licensing provisions</em>: MIT license</div><div><em>Programming language</em>: <span>Mathematica</span> 13 or higher</div><div><em>Supplementary material</em>: The example file <span>SOFIA_examples.nb</span> in SOFIA GitHub <span><span>[1]</span></span>.</div><div><em>Nature of problem</em>: This paper makes a significant contribution to perturbative computations in particle physics and related fields, by introducing a novel computer package analyzing the singularity structure of these integrals. In practice, this information can be leveraged to derive differential equations, the solutions of which enable efficient computation of the Feynman integrals, a crucial step that has been a major bottleneck in high-precision QCD computations. We believe this paper pushes the field of Feynman integrals into a new direction. The key insights that enabled this work were based on applications of cutting-edge techniques from algebraic geometry. The versatility of the package we introduce means it can be also used in other applications, including computations of cosmological and energy-energy correlators, as well as post-Minkowski expansion of gravitational potentials.</div><div><em>Solution method</em>: Based on new theoretical insights, it provides an easy-to-use open-source tool for multi-loop computations in perturbation theory. In the paper, we demonstrated it can be used in diverse applications, including perturbative Standard Model computations, computations of cosmological and energy-energy correlators, as well as post-Minkowski expansions of gravitational potentials. Given the high degree of automation and broad scope of applications, we think the paper would be a good fit in CPC.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"320 ","pages":"Article 109970"},"PeriodicalIF":3.4,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880070","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 : 2025-12-10DOI: 10.1016/j.cpc.2025.109980
Sreenivasa chary Thatikonda , F.N. De Oliveira-Lopes , A. Mustonen , K. Pommois , D. Told , F. Jenko
The super simple Vlasov (ssV) code was developed to study instabilities, turbulence, and reconnection in weakly magnetized plasmas, such as the solar wind in the dissipation range and the edge of fusion plasmas. The ssV code overcomes the limitations of standard gyrokinetic theory by using a hybrid kinetic-gyrokinetic model that incorporates fully kinetic ions and gyrokinetic electrons. This hybrid kinetic-gyrokinetic model enables accurate modeling in regimes characterized by steep gradients and high-frequency dynamics. To achieve this, ssV implements a set of semi-Lagrangian numerical schemes, including Positive Flux Conservative (PFC), Flux Conservative fifth-order (FCV), FCV with Umeda limiters, and a Semi-Lagrangian Monotonicity-Preserving fifth-order scheme (SLMP5). Benchmark problems such as Landau damping, ion-acoustic waves, ion Bernstein waves, and kinetic Alfvén waves were employed to evaluate the schemes. The SLMP5 scheme consistently delivered the best overall accuracy and numerical stability performance. The code also addresses well-known electromagnetic gyrokinetic simulation issues, such as the Ampère cancellation problem, using carefully chosen velocity-space resolutions and accurate integral evaluation.
{"title":"Verification of a hybrid kinetic-gyrokinetic model using the advanced semi-Lagrange code ssV","authors":"Sreenivasa chary Thatikonda , F.N. De Oliveira-Lopes , A. Mustonen , K. Pommois , D. Told , F. Jenko","doi":"10.1016/j.cpc.2025.109980","DOIUrl":"10.1016/j.cpc.2025.109980","url":null,"abstract":"<div><div>The super simple Vlasov (ssV) code was developed to study instabilities, turbulence, and reconnection in weakly magnetized plasmas, such as the solar wind in the dissipation range and the edge of fusion plasmas. The ssV code overcomes the limitations of standard gyrokinetic theory by using a hybrid kinetic-gyrokinetic model that incorporates fully kinetic ions and gyrokinetic electrons. This hybrid kinetic-gyrokinetic model enables accurate modeling in regimes characterized by steep gradients and high-frequency dynamics. To achieve this, ssV implements a set of semi-Lagrangian numerical schemes, including Positive Flux Conservative (PFC), Flux Conservative fifth-order (FCV), FCV with Umeda limiters, and a Semi-Lagrangian Monotonicity-Preserving fifth-order scheme (SLMP5). Benchmark problems such as Landau damping, ion-acoustic waves, ion Bernstein waves, and kinetic Alfvén waves were employed to evaluate the schemes. The SLMP5 scheme consistently delivered the best overall accuracy and numerical stability performance. The code also addresses well-known electromagnetic gyrokinetic simulation issues, such as the Ampère cancellation problem, using carefully chosen velocity-space resolutions and accurate integral evaluation.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"320 ","pages":"Article 109980"},"PeriodicalIF":3.4,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786467","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 : 2025-12-09DOI: 10.1016/j.cpc.2025.109986
Feng Han , Jianguo Wang , Guoliang Peng , Xueting Shi
In this paper, a physics-informed multiresolution wavelet neural network (PIMWNN) method is proposed for solving partial differential equations (PDEs). This method uses the multiresolution wavelet neural network (MWNN) to approximate unknown functions, then substituting the MWNN into PDEs and training the MWNN by least-squares algorithm. We apply the proposed method to various problems, including stationary/nonstationary advection, diffusion and advection-diffusion problems, and linear/nonlinear time-dependent problems. Numerical experiments show that the PIMWNN method can achieve higher accuracy and faster speed than Physics Informed Neural Networks (PINNs). Moreover, the PIMWNN method can handle different boundary conditions easily and solve the time-dependent problems efficiently. The proposed method is expected to solve the spectral bias problem in network training. These characteristics show the great potential of the PIMWNN method used in the field of numerical solving methods for PDEs.
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Pub Date : 2025-12-09DOI: 10.1016/j.cpc.2025.109985
Nicholas Mueller, Santiago Badia
In this paper, we introduce GridapROMs, a Julia-based library for the numerical approximation of parameterized partial differential equations (PDEs) using a comprehensive suite of linear reduced order models (ROMs). The library is designed to be extendable and productive, leveraging an expressive high-level API built on the Gridap PDE solver backend, while achieving high performance through Julia’s just-in-time compiler and advanced lazy evaluation techniques. GridapROMs is PDE-agnostic, enabling its application to a wide range of problems, including linear, nonlinear, single-field, multi-field, steady, and unsteady equations. This work details the library’s key innovations, implementation principles, and core components, providing usage examples and demonstrating its capabilities by solving a fluid dynamics problem modeled by the Navier-Stokes equations in a 3D geometry. Program summaryProgram Title: GridapROMs.jl (version 1.0) CPC Library link to program files:https://doi.org/10.17632/h27nszy8bt.1Developer’s repository link:https://github.com/Gridap/GridapROMs.jlLicensing provisions: MIT license Programming language: Julia Nature of problem: Numerical simulation of parameterized PDEs, including linear, nonlinear, single-field, multi-field, steady, and unsteady problems. Classical full-order models are computationally expensive, requiring intensive computations for each parameter configuration. Solution method: GridapROMs approximates the parameter-to-solution map using linear reduced order models. It constructs a reduced basis from the tangent hyperplane to the solution manifold and applies a (Petrov-)Galerkin projection to the full-order equations. Nonaffine parameter dependencies in the residual and/or Jacobian are efficiently handled using hyper-reduction techniques.
{"title":"GridapROMs.jl: Efficient reduced order modelling in the Julia programming language","authors":"Nicholas Mueller, Santiago Badia","doi":"10.1016/j.cpc.2025.109985","DOIUrl":"10.1016/j.cpc.2025.109985","url":null,"abstract":"<div><div>In this paper, we introduce GridapROMs, a Julia-based library for the numerical approximation of parameterized partial differential equations (PDEs) using a comprehensive suite of linear reduced order models (ROMs). The library is designed to be extendable and productive, leveraging an expressive high-level API built on the Gridap PDE solver backend, while achieving high performance through Julia’s just-in-time compiler and advanced lazy evaluation techniques. GridapROMs is PDE-agnostic, enabling its application to a wide range of problems, including linear, nonlinear, single-field, multi-field, steady, and unsteady equations. This work details the library’s key innovations, implementation principles, and core components, providing usage examples and demonstrating its capabilities by solving a fluid dynamics problem modeled by the Navier-Stokes equations in a 3D geometry. <strong>Program summary</strong> <em>Program Title:</em> GridapROMs.jl (version 1.0) <em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/h27nszy8bt.1</span><svg><path></path></svg></span> <em>Developer’s repository link:</em> <span><span>https://github.com/Gridap/GridapROMs.jl</span><svg><path></path></svg></span> <em>Licensing provisions:</em> MIT license <em>Programming language:</em> Julia <em>Nature of problem:</em> Numerical simulation of parameterized PDEs, including linear, nonlinear, single-field, multi-field, steady, and unsteady problems. Classical full-order models are computationally expensive, requiring intensive computations for each parameter configuration. <em>Solution method:</em> GridapROMs approximates the parameter-to-solution map using linear reduced order models. It constructs a reduced basis from the tangent hyperplane to the solution manifold and applies a (Petrov-)Galerkin projection to the full-order equations. Nonaffine parameter dependencies in the residual and/or Jacobian are efficiently handled using hyper-reduction techniques.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"320 ","pages":"Article 109985"},"PeriodicalIF":3.4,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786465","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}
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