Pub Date : 2025-10-10DOI: 10.1016/j.cpc.2025.109897
Tomasz Świsłocki , Krzysztof Gawryluk , Mirosław Brewczyk , Tomasz Karpiuk
In this work we define, analyze, and compare different numerical schemes that can be used to study the ground state properties of Bose-Fermi systems, such as mixtures of different atomic species under external forces or self-bound quantum droplets. The bosonic atoms are assumed to be condensed and are described by the generalized Gross-Pitaevskii equation. The fermionic atoms, on the other hand, are treated individually, and each atom is associated with a wave function whose evolution follows the Hartree-Fock equation. We solve such a formulated set of equations using a variety of methods, including those based on adiabatic switching of interactions and the imaginary time propagation technique combined with the Gram-Schmidt orthonormalization or the diagonalization of the Hamiltonian matrix. We show how different algorithms compete at the numerical level by studying the mixture in the range of parameters covering the formation of self-bound quantum Bose-Fermi droplets.
{"title":"Competitive algorithms for calculating the ground state properties of Bose-Fermi mixtures","authors":"Tomasz Świsłocki , Krzysztof Gawryluk , Mirosław Brewczyk , Tomasz Karpiuk","doi":"10.1016/j.cpc.2025.109897","DOIUrl":"10.1016/j.cpc.2025.109897","url":null,"abstract":"<div><div>In this work we define, analyze, and compare different numerical schemes that can be used to study the ground state properties of Bose-Fermi systems, such as mixtures of different atomic species under external forces or self-bound quantum droplets. The bosonic atoms are assumed to be condensed and are described by the generalized Gross-Pitaevskii equation. The fermionic atoms, on the other hand, are treated individually, and each atom is associated with a wave function whose evolution follows the Hartree-Fock equation. We solve such a formulated set of equations using a variety of methods, including those based on adiabatic switching of interactions and the imaginary time propagation technique combined with the Gram-Schmidt orthonormalization or the diagonalization of the Hamiltonian matrix. We show how different algorithms compete at the numerical level by studying the mixture in the range of parameters covering the formation of self-bound quantum Bose-Fermi droplets.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"318 ","pages":"Article 109897"},"PeriodicalIF":3.4,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145333699","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 : 2025-10-09DOI: 10.1016/j.cpc.2025.109891
S. Fioccola , L. Giacomazzi , D. Ceresoli , N. Richard , A. Hemeryck , L. Martin-Samos
Orbital magnetization, a key property arising from the orbital motion of electrons, plays a crucial role in determining the magnetic behavior of molecules and solids. Despite its straightforward calculation in finite systems, the computation in periodic systems poses challenges due to the ill-defined position operator and surface current contributions. The modern theory of orbital magnetization, formulated in the Wannier representation and implemented within the Density Functional Theory (DFT) framework, offers an accurate solution through the “converse approach.” In this paper, we introduce , a refactored and modular implementation of the converse method, designed to replace the outdated routines from Quantum ESPRESSO (version 3.2). integrates recent advancements in computational libraries, including scaLAPACK and ELPA, to enhance scalability and computational efficiency, particularly for large supercell calculations. While incorporates these improvements for scalability, the main focus of this work is provide the community with a performing and accurate first principles orbital magnetization package to compute properties such as Electron Paramagnetic Resonance (EPR) g-tensors and Nuclear Magnetic Resonance (NMR) chemical shifts, specially in systems where perturbative methods fail. We demonstrate the effectiveness of through several benchmark cases, including the NMR chemical shift of 27Al in alumina and 17O and 29Si in α-quartz, as well as the EPR g-tensor of radicals and substitutional nitrogen defects in silicon. In all cases, the results show excellent agreement with theoretical and experimental data, with significant improvements in accuracy for EPR calculations over the linear response approach. The package, fully compatible with the latest Quantum ESPRESSO versions, opens new possibilities for studying complex materials with enhanced precision.
Program summary
Program Title: qe-converse
CPC Library link to program files:https://doi.org/10.17632/3tyhmxknfc.1
{"title":"QE-CONVERSE: An open-source package for the quantum ESPRESSO distribution to compute non-perturbatively orbital magnetization from first principles, including NMR chemical shifts and EPR parameters","authors":"S. Fioccola , L. Giacomazzi , D. Ceresoli , N. Richard , A. Hemeryck , L. Martin-Samos","doi":"10.1016/j.cpc.2025.109891","DOIUrl":"10.1016/j.cpc.2025.109891","url":null,"abstract":"<div><div>Orbital magnetization, a key property arising from the orbital motion of electrons, plays a crucial role in determining the magnetic behavior of molecules and solids. Despite its straightforward calculation in finite systems, the computation in periodic systems poses challenges due to the ill-defined position operator and surface current contributions. The modern theory of orbital magnetization, formulated in the Wannier representation and implemented within the Density Functional Theory (DFT) framework, offers an accurate solution through the “converse approach.” In this paper, we introduce <figure><img></figure>, a refactored and modular implementation of the converse method, designed to replace the outdated routines from Quantum ESPRESSO (version 3.2). <figure><img></figure> integrates recent advancements in computational libraries, including scaLAPACK and ELPA, to enhance scalability and computational efficiency, particularly for large supercell calculations. While <figure><img></figure> incorporates these improvements for scalability, the main focus of this work is provide the community with a performing and accurate first principles orbital magnetization package to compute properties such as Electron Paramagnetic Resonance (EPR) g-tensors and Nuclear Magnetic Resonance (NMR) chemical shifts, specially in systems where perturbative methods fail. We demonstrate the effectiveness of <figure><img></figure> through several benchmark cases, including the NMR chemical shift of <sup>27</sup>Al in alumina and <sup>17</sup>O and <sup>29</sup>Si in <em>α</em>-quartz, as well as the EPR g-tensor of <span><math><mmultiscripts><mrow><mi>Σ</mi></mrow><mprescripts></mprescripts><none></none><mrow><mi>n</mi></mrow></mmultiscripts><mo>(</mo><mi>n</mi><mo>≥</mo><mn>2</mn><mo>)</mo></math></span> radicals and substitutional nitrogen defects in silicon. In all cases, the results show excellent agreement with theoretical and experimental data, with significant improvements in accuracy for EPR calculations over the linear response approach. The <figure><img></figure> package, fully compatible with the latest Quantum ESPRESSO versions, opens new possibilities for studying complex materials with enhanced precision.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> qe-converse</div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/3tyhmxknfc.1</span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span>https://github.com/mammasmias/QE-CONVERSE.git</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> GNU General Public Licence 3.0</div><div><em>Programming language:</em> Fortran 90</div><div><em>Nature of problem:</em> Ab-initio calculation of the EPR <em>g</em>-tensor and the NMR chemical shift in solid state.</div><div><em>Solution method:</em> Compute the orbital magnetization through a non-pertubative method.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"318 ","pages":"Article 109891"},"PeriodicalIF":3.4,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145263525","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 : 2025-10-09DOI: 10.1016/j.cpc.2025.109893
J.T. Omotani , D. Dickinson , B.D. Dudson , L. Easy , D. Hoare , P. Hill , T. Nicholas , J. Parker , F. Riva , N.R. Walkden , Q. Xia , F. Militello
The scrape-off layer of a tokamak fusion reactor carries the plasma exhaust from the hot core plasma to the material surfaces of the reactor vessel. The heat loads imposed by the exhaust are a critical limit on the performance of fusion power plants. Turbulent transport of the plasma regulates the width of the scrape-off layer plasma and must be modelled to understand the intensity of these heat loads.
STORM is a plasma turbulence code capable of simulating three dimensional turbulence across the full scrape-off layer of a tokamak fusion reactor, using a drift reduced, collisional fluid model. STORM uses mostly finite difference schemes, with a staggered grid in the direction parallel to the magnetic field. We describe the model, geometry and initialisation options used by STORM, as well as the numerical methods, which are implemented using the BOUT++ plasma simulation framework.
BOUT++ has been enhanced alongside the development of STORM, providing better support for staggered grid methods. We summarise these enhancements, including a detailed explanation of the parallel derivative methods, which underwent a major update for version 4 of BOUT++.
Program summary
Program Title: STORM
CPC Library link to program files:https://doi.org/10.17632/zm3tdfhp9r.1
Supplementary material: Configuration and input files and post-processing scripts to run the example code given in Listings 1, 2, and 3.
Nature of problem: The scrape-off layer region of tokamak fusion reactors carries the plasma exhaust which escapes from the core, confined plasma and reaches material surfaces along open magnetic field lines. The power and particle loads on the material surfaces are a critical limiting factor for the performance of fusion reactors, but are challenging to simulate due to the large fluctuation amplitudes, complex magnetic geometry, and widely separated time- and length-scales. Three dimensional simulations of plasma turbulence are needed to understand the particle and energy transport in the scrape-off layer and provide predictive capability for the design of future reactors.
Solution method: STORM solves a drift reduced, collisional, fluid model for the scrape-off layer plasma. The model is discretised in space using mostly finite difference methods, combined in some places with Fourier methods that take advantage of the toroidal symmetry of the tokamak geometry. The fastest dynamics occur in the direction parallel to the magnetic field, for which a staggered grid is used to avoid the chequerboard instability associated with advective equations [1, s
托卡马克聚变反应堆的刮擦层将等离子体废气从热核心等离子体输送到反应堆容器的材料表面。废气产生的热负荷是影响核聚变电厂性能的关键因素。等离子体的湍流传输调节了刮擦层等离子体的宽度，必须对其进行建模以了解这些热负荷的强度。STORM是一个等离子体湍流代码，能够使用减少漂移的碰撞流体模型模拟托卡马克聚变反应堆整个刮擦层的三维湍流。STORM主要使用有限差分格式，在与磁场平行的方向上错开网格。我们描述了STORM使用的模型、几何和初始化选项，以及使用but++等离子体模拟框架实现的数值方法。随着STORM的开发，but++得到了增强，为交错网格方法提供了更好的支持。我们总结了这些增强，包括对并行派生方法的详细解释，并行派生方法在第4版中进行了重大更新。程序摘要程序标题：STORMCPC库链接到程序文件：https://doi.org/10.17632/zm3tdfhp9r.1Developer's存储库链接：https://github.com/boutproject/STORMLicensing条款：gplv3编程语言：c++补充材料：配置和输入文件以及运行清单1、2和3中给出的示例代码的后处理脚本。问题的性质：托卡马克聚变反应堆的刮擦层区域携带着从堆芯中逸出的等离子体废气，受限制的等离子体沿着开放的磁力线到达材料表面。材料表面的功率和粒子载荷是影响核聚变反应堆性能的一个关键限制因素，但由于其波动幅度大、磁几何结构复杂、时间和长度尺度分散等原因，模拟具有挑战性。为了了解刮擦层中粒子和能量的输运，并为未来反应堆的设计提供预测能力，需要对等离子体湍流进行三维模拟。解决方法：STORM解决了刮擦层等离子体的减少漂移、碰撞、流体模型。该模型主要使用有限差分方法在空间上离散，在某些地方结合利用托卡马克几何结构的环面对称性的傅立叶方法。最快的动力学发生在与磁场平行的方向上，交错网格用于避免与平流方程相关的棋盘不稳定性[1，第6.2节，6.3节]。时间求解器是由SUNDIALS库[2]提供的完全隐式、无矩阵、变步长、变阶方法。STORM是使用用于等离子体模拟的but++框架实现的。帕坦卡，数值传热与流体流动，西半球出版公司，1980。C. Hindmarsh， P. N. Brown， K. E. Grant，等。数学。软件31(3)(2005)363-396。
{"title":"STORM: Scrape-off layer turbulence in tokamak fusion reactors","authors":"J.T. Omotani , D. Dickinson , B.D. Dudson , L. Easy , D. Hoare , P. Hill , T. Nicholas , J. Parker , F. Riva , N.R. Walkden , Q. Xia , F. Militello","doi":"10.1016/j.cpc.2025.109893","DOIUrl":"10.1016/j.cpc.2025.109893","url":null,"abstract":"<div><div>The scrape-off layer of a tokamak fusion reactor carries the plasma exhaust from the hot core plasma to the material surfaces of the reactor vessel. The heat loads imposed by the exhaust are a critical limit on the performance of fusion power plants. Turbulent transport of the plasma regulates the width of the scrape-off layer plasma and must be modelled to understand the intensity of these heat loads.</div><div>STORM is a plasma turbulence code capable of simulating three dimensional turbulence across the full scrape-off layer of a tokamak fusion reactor, using a drift reduced, collisional fluid model. STORM uses mostly finite difference schemes, with a staggered grid in the direction parallel to the magnetic field. We describe the model, geometry and initialisation options used by STORM, as well as the numerical methods, which are implemented using the BOUT++ plasma simulation framework.</div><div>BOUT++ has been enhanced alongside the development of STORM, providing better support for staggered grid methods. We summarise these enhancements, including a detailed explanation of the parallel derivative methods, which underwent a major update for version 4 of BOUT++.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> STORM</div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/zm3tdfhp9r.1</span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span>https://github.com/boutproject/STORM</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> GPLv3</div><div><em>Programming language:</em> C++</div><div><em>Supplementary material:</em> Configuration and input files and post-processing scripts to run the example code given in Listings 1, 2, and 3.</div><div><em>Nature of problem:</em> The scrape-off layer region of tokamak fusion reactors carries the plasma exhaust which escapes from the core, confined plasma and reaches material surfaces along open magnetic field lines. The power and particle loads on the material surfaces are a critical limiting factor for the performance of fusion reactors, but are challenging to simulate due to the large fluctuation amplitudes, complex magnetic geometry, and widely separated time- and length-scales. Three dimensional simulations of plasma turbulence are needed to understand the particle and energy transport in the scrape-off layer and provide predictive capability for the design of future reactors.</div><div><em>Solution method:</em> STORM solves a drift reduced, collisional, fluid model for the scrape-off layer plasma. The model is discretised in space using mostly finite difference methods, combined in some places with Fourier methods that take advantage of the toroidal symmetry of the tokamak geometry. The fastest dynamics occur in the direction parallel to the magnetic field, for which a staggered grid is used to avoid the chequerboard instability associated with advective equations [1, s","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"318 ","pages":"Article 109893"},"PeriodicalIF":3.4,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145263437","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 : 2025-10-08DOI: 10.1016/j.cpc.2025.109894
Nikolay D. Gagunashvili
Two maximum likelihood-based algorithms for unfolding or deconvolution are considered: the Richardson-Lucy method and the Data Unfolding method with Mean Integrated Square Error (MISE) optimization. Unfolding is viewed as a procedure for estimating an unknown probability density function. Both external and internal quality assessment methods can be applied for this purpose. In some cases, external criteria exist to evaluate deconvolution quality. A typical example is the deconvolution of a blurred image, where the sharpness of the restored image serves as an indicator of quality. However, defining such external criteria can be challenging, particularly when a measurement has not been performed previously. In such instances, internal criteria are necessary to assess the quality of the result independently of external information. The article discusses two internal criteria: MISE for the unfolded distribution and the condition number of the correlation matrix of the unfolded distribution. These internal quality criteria are applied to a comparative analysis of the two methods using identical numerical data. The results of the analysis demonstrate the superiority of the Data Unfolding method with MISE optimization over the Richardson-Lucy method.
{"title":"Comparative analysis of Richardson-Lucy deconvolution and data unfolding with mean integrated square error optimization","authors":"Nikolay D. Gagunashvili","doi":"10.1016/j.cpc.2025.109894","DOIUrl":"10.1016/j.cpc.2025.109894","url":null,"abstract":"<div><div>Two maximum likelihood-based algorithms for unfolding or deconvolution are considered: the Richardson-Lucy method and the Data Unfolding method with Mean Integrated Square Error (MISE) optimization. Unfolding is viewed as a procedure for estimating an unknown probability density function. Both external and internal quality assessment methods can be applied for this purpose. In some cases, external criteria exist to evaluate deconvolution quality. A typical example is the deconvolution of a blurred image, where the sharpness of the restored image serves as an indicator of quality. However, defining such external criteria can be challenging, particularly when a measurement has not been performed previously. In such instances, internal criteria are necessary to assess the quality of the result independently of external information. The article discusses two internal criteria: MISE for the unfolded distribution and the condition number of the correlation matrix of the unfolded distribution. These internal quality criteria are applied to a comparative analysis of the two methods using identical numerical data. The results of the analysis demonstrate the superiority of the Data Unfolding method with MISE optimization over the Richardson-Lucy method.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"318 ","pages":"Article 109894"},"PeriodicalIF":3.4,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145263441","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 : 2025-10-08DOI: 10.1016/j.cpc.2025.109892
Piotr Białas , Vaibhav Chahar , Piotr Korcyl , Tomasz Stebel , Mateusz Winiarski , Dawid Zapolski
Autoregressive Neural Networks (ANN) have been recently proposed as a mechanism to improve the efficiency of Monte Carlo algorithms for several spin systems. The idea relies on the fact that the total probability of a configuration can be factorized into conditional probabilities of each spin, which in turn can be approximated by a neural network. Once trained, the ANNs can be used to sample configurations from the approximated probability distribution and to explicitly evaluate this probability for a given configuration. It has also been observed that such conditional probabilities give access to information-theoretic observables such as mutual information or entanglement entropy. In this paper, we describe the hierarchical autoregressive network (HAN) algorithm in three spatial dimensions and study its performance using the example of the Ising model. We compare HAN with three other autoregressive architectures and the classical Wolff cluster algorithm. Finally, we provide estimates of thermodynamic observables for the three-dimensional Ising model, such as entropy and free energy, in a range of temperatures across the phase transition.
{"title":"Hierarchical autoregressive neural networks in three-dimensional statistical system","authors":"Piotr Białas , Vaibhav Chahar , Piotr Korcyl , Tomasz Stebel , Mateusz Winiarski , Dawid Zapolski","doi":"10.1016/j.cpc.2025.109892","DOIUrl":"10.1016/j.cpc.2025.109892","url":null,"abstract":"<div><div>Autoregressive Neural Networks (ANN) have been recently proposed as a mechanism to improve the efficiency of Monte Carlo algorithms for several spin systems. The idea relies on the fact that the total probability of a configuration can be factorized into conditional probabilities of each spin, which in turn can be approximated by a neural network. Once trained, the ANNs can be used to sample configurations from the approximated probability distribution and to explicitly evaluate this probability for a given configuration. It has also been observed that such conditional probabilities give access to information-theoretic observables such as mutual information or entanglement entropy. In this paper, we describe the hierarchical autoregressive network (HAN) algorithm in three spatial dimensions and study its performance using the example of the Ising model. We compare HAN with three other autoregressive architectures and the classical Wolff cluster algorithm. Finally, we provide estimates of thermodynamic observables for the three-dimensional Ising model, such as entropy and free energy, in a range of temperatures across the phase transition.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"318 ","pages":"Article 109892"},"PeriodicalIF":3.4,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145263442","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 : 2025-10-06DOI: 10.1016/j.cpc.2025.109890
D.I. Glazier , R. Tyson
The use of machine learning approaches continues to have many benefits in experimental nuclear and particle physics. One common issue is generating training data which is sufficiently realistic to give reliable results. Here we advocate using real experimental data as the source of training data and demonstrate how one might subtract background contributions through the use of probabilistic weights which can be readily applied to training data. The sPlot formalism is a common tool used to isolate distributions from different sources. However, the negative sWeights produced by the sPlot technique can cause training problems and poor predictive power. This article demonstrates how density ratio estimation can be applied to convert sWeights to event probabilities, which we call drWeights. The drWeights can then be applied to produce the distributions of interest and are consistent with direct use of the sWeights. This article will also show how decision trees are particularly well suited to convert sWeights, with the benefit of fast prediction rates and adaptability to aspects of experimental data such as the data sample size and proportions of different event sources. We also show that a density ratio product approach in which the initial drWeights are reweighted by an additional converter gives substantially better results.
{"title":"Converting sWeights to probabilities with density ratios","authors":"D.I. Glazier , R. Tyson","doi":"10.1016/j.cpc.2025.109890","DOIUrl":"10.1016/j.cpc.2025.109890","url":null,"abstract":"<div><div>The use of machine learning approaches continues to have many benefits in experimental nuclear and particle physics. One common issue is generating training data which is sufficiently realistic to give reliable results. Here we advocate using real experimental data as the source of training data and demonstrate how one might subtract background contributions through the use of probabilistic weights which can be readily applied to training data. The <em>sPlot</em> formalism is a common tool used to isolate distributions from different sources. However, the negative <em>sWeights</em> produced by the <em>sPlot</em> technique can cause training problems and poor predictive power. This article demonstrates how density ratio estimation can be applied to convert <em>sWeights</em> to event probabilities, which we call <em>drWeights</em>. The <em>drWeights</em> can then be applied to produce the distributions of interest and are consistent with direct use of the <em>sWeights</em>. This article will also show how decision trees are particularly well suited to convert <em>sWeights</em>, with the benefit of fast prediction rates and adaptability to aspects of experimental data such as the data sample size and proportions of different event sources. We also show that a density ratio product approach in which the initial <em>drWeights</em> are reweighted by an additional converter gives substantially better results.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"318 ","pages":"Article 109890"},"PeriodicalIF":3.4,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145263440","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 : 2025-10-01DOI: 10.1016/j.cpc.2025.109881
E. Fan , Tianhan Zhang , Jiaao Hao , Chih-Yung Wen , Lisong Shi
In this study, we introduce Fire, an open-source adaptive mesh refinement (AMR) solver for supersonic reacting flows, and conduct theoretical analyses on the efficiency of AMR methods. Fire is developed within the AMR framework of ECOGEN (Schmidmayer et al., 2020). To accurately model compressible multi-component reacting flows, the Fire solver employs the thermally perfect gas model for multi-species gaseous mixtures, mixture-averaged transport models for viscous fluxes, and detailed finite-rate chemistry for combustion processes. The solver utilizes the Harten-Lax-van Leer Contact approximate Riemann solver with low-Mach number correction to evaluate inviscid fluxes, demonstrating its superiority over the traditional Harten-Lax-van Leer Contact solver on detonation simulations. Moreover, we deduce the theoretical speedup ratio (denoted as ) of AMR methods over uniform-grid methods by analyzing the advancing procedures. This theoretical analysis is well-supported by the numerical speedup ratio (denoted as ) given by numerical tests. To further enhance computational efficiency, we propose a three-stage AMR strategy specifically tailored to the characteristics of inert flows, flame fronts, and shock-flame interactions. Comprehensive validation tests, encompassing unsteady convection and diffusion, planar deflagration, inert and reacting shock-bubble interactions, planar detonations, and detonation cellular structures, confirm the accuracy and efficiency of Fire in simulating supersonic combustions. We anticipate that this work will not only serve as a valuable numerical tool for supersonic reacting flows research but also contribute to a deeper understanding and improvement of AMR methodologies.
本文引入了开源的超声速反应流自适应网格细化（AMR）求解器Fire，并对AMR方法的效率进行了理论分析。Fire是在ECOGEN的AMR框架内开发的（Schmidmayer et al., 2020）。为了精确地模拟可压缩的多组分反应流，Fire求解器采用了多组分气体混合物的热完美气体模型，粘性通量的混合平均输运模型，以及燃烧过程的详细有限速率化学。该求解器利用低马赫数修正的Harten-Lax-van Leer接触近似黎曼求解器来计算无粘通量，在爆震源模拟中具有优于传统Harten-Lax-van Leer接触求解器的优势。此外，通过对改进过程的分析，推导出AMR方法相对于均匀网格方法的理论加速比（η）。这一理论分析得到数值加速比（以η值表示）数值试验的很好支持。为了进一步提高计算效率，我们提出了一种专门针对惰性流动、火焰锋面和激波-火焰相互作用特征的三阶段AMR策略。包括非定常对流和扩散、平面爆燃、惰性和反应的激波-气泡相互作用、平面爆轰和爆轰细胞结构在内的综合验证试验，证实了Fire模拟超音速燃烧的准确性和效率。我们期望这项工作不仅可以作为超声速反应流动研究的有价值的数值工具，而且有助于更深入地理解和改进AMR方法。
{"title":"Fire: An open-source adaptive mesh refinement solver for supersonic reacting flows","authors":"E. Fan , Tianhan Zhang , Jiaao Hao , Chih-Yung Wen , Lisong Shi","doi":"10.1016/j.cpc.2025.109881","DOIUrl":"10.1016/j.cpc.2025.109881","url":null,"abstract":"<div><div>In this study, we introduce Fire, an open-source adaptive mesh refinement (AMR) solver for supersonic reacting flows, and conduct theoretical analyses on the efficiency of AMR methods. Fire is developed within the AMR framework of ECOGEN (Schmidmayer et al., 2020). To accurately model compressible multi-component reacting flows, the Fire solver employs the thermally perfect gas model for multi-species gaseous mixtures, mixture-averaged transport models for viscous fluxes, and detailed finite-rate chemistry for combustion processes. The solver utilizes the Harten-Lax-van Leer Contact approximate Riemann solver with low-Mach number correction to evaluate inviscid fluxes, demonstrating its superiority over the traditional Harten-Lax-van Leer Contact solver on detonation simulations. Moreover, we deduce the theoretical speedup ratio (denoted as <span><math><msub><mrow><mi>η</mi></mrow><mrow><mtext>the</mtext></mrow></msub></math></span>) of AMR methods over uniform-grid methods by analyzing the advancing procedures. This theoretical analysis is well-supported by the numerical speedup ratio (denoted as <span><math><msub><mrow><mi>η</mi></mrow><mrow><mtext>num</mtext></mrow></msub></math></span>) given by numerical tests. To further enhance computational efficiency, we propose a three-stage AMR strategy specifically tailored to the characteristics of inert flows, flame fronts, and shock-flame interactions. Comprehensive validation tests, encompassing unsteady convection and diffusion, planar deflagration, inert and reacting shock-bubble interactions, planar detonations, and detonation cellular structures, confirm the accuracy and efficiency of Fire in simulating supersonic combustions. We anticipate that this work will not only serve as a valuable numerical tool for supersonic reacting flows research but also contribute to a deeper understanding and improvement of AMR methodologies.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"318 ","pages":"Article 109881"},"PeriodicalIF":3.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145263438","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 : 2025-10-01DOI: 10.1016/j.cpc.2025.109882
Tapas Tripura , Souvik Chakraborty
Machine learning has witnessed substantial growth in recent years, leading to the development of advanced deep learning models crafted to address a wide range of real-world challenges spanning various domains, including the acceleration of scientific computing. Contemporary deep learning approaches to solving partial differential equations (PDEs) involve approximating either the function mapping of a specific problem or the solution operators of a pre-defined physical system. Consequently, solving multiple PDEs representing a variety of physical systems requires training of multiple deep learning models. The creation of physics-specific models from scratch for each new physical system remains a resource-intensive undertaking, demanding considerable (i) computational time, (ii) memory resources, (iii) energy, (iv) intensive physics-specific manual tuning, and (v) large problem-specific training datasets. A more generalized machine learning-enhanced computational approach would be to learn a single unified deep learning model (commonly defined as the foundation model) instead of training multiple solvers from scratch. Besides accelerating computational simulations, such unified models will address all the above challenges. In this study, we introduce the Neural Combinatorial Wavelet Neural Operator (NCWNO) as a foundational model for scientific computing. The NCWNO leverages a gated structure that employs local wavelet integral blocks to acquire shared features across multiple physical systems, complemented by a memory-based ensembling approach among these local wavelet experts. The proposed NCWNO offers two key advantages: (i) it can simultaneously learn solution operators for multiple parametric PDEs, and (ii) with pre-training, it can be fine-tuned to new parametric PDEs with reduced training datasets and time. The proposed NCWNO is the first kernel-based foundational operator learning algorithm distinguished by its (i) integral-kernel-based learning structure, (ii) robustness against catastrophic forgetting of old PDEs, and (iii) the facilitation of knowledge transfer across dissimilar physical systems. Through an extensive set of benchmark examples, we demonstrate that the NCWNO can outperform existing multiphysics and task-specific baseline operator learning frameworks.
{"title":"Neural combinatorial wavelet neural operator for catastrophic forgetting free in-context operator learning of multiple partial differential equations","authors":"Tapas Tripura , Souvik Chakraborty","doi":"10.1016/j.cpc.2025.109882","DOIUrl":"10.1016/j.cpc.2025.109882","url":null,"abstract":"<div><div>Machine learning has witnessed substantial growth in recent years, leading to the development of advanced deep learning models crafted to address a wide range of real-world challenges spanning various domains, including the acceleration of scientific computing. Contemporary deep learning approaches to solving partial differential equations (PDEs) involve approximating either the function mapping of a specific problem or the solution operators of a pre-defined physical system. Consequently, solving multiple PDEs representing a variety of physical systems requires training of multiple deep learning models. The creation of physics-specific models from scratch for each new physical system remains a resource-intensive undertaking, demanding considerable (i) computational time, (ii) memory resources, (iii) energy, (iv) intensive physics-specific manual tuning, and (v) large problem-specific training datasets. A more generalized machine learning-enhanced computational approach would be to learn a single unified deep learning model (commonly defined as the foundation model) instead of training multiple solvers from scratch. Besides accelerating computational simulations, such unified models will address all the above challenges. In this study, we introduce the Neural Combinatorial Wavelet Neural Operator (NCWNO) as a foundational model for scientific computing. The NCWNO leverages a gated structure that employs local wavelet integral blocks to acquire shared features across multiple physical systems, complemented by a memory-based ensembling approach among these local wavelet experts. The proposed NCWNO offers two key advantages: (i) it can simultaneously learn solution operators for multiple parametric PDEs, and (ii) with pre-training, it can be fine-tuned to new parametric PDEs with reduced training datasets and time. The proposed NCWNO is the first kernel-based foundational operator learning algorithm distinguished by its (i) integral-kernel-based learning structure, (ii) robustness against catastrophic forgetting of old PDEs, and (iii) the facilitation of knowledge transfer across dissimilar physical systems. Through an extensive set of benchmark examples, we demonstrate that the NCWNO can outperform existing multiphysics and task-specific baseline operator learning frameworks.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"318 ","pages":"Article 109882"},"PeriodicalIF":3.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145227754","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 : 2025-10-01DOI: 10.1016/j.cpc.2025.109879
Yuhang Wu , Jianhua Huang , Xu Qian , Wenjun Sun
In this paper, a Physics Informed Neural Networks (PINNs) method based on Variable Eddington Factor (VEF) acceleration iteration is proposed to address the time-dependent linear radiative transfer equations (LRTEs), which exhibit the characteristics of multi-scale and high dimensionality. Firstly, the factors relating to the failure of the vanilla PINNs in solving LRTEs within the diffusion regime are analyzed by the Neural Tangent Kernel (NTK) theory. Subsequently, the VEF-PINNs method is established, where PINNs are employed to handle the radiative transfer equations and the analytic VEF equations that are used to accelerate the iteration process. It is demonstrated that as the Knudsen number ε approaches 0, the VEF-PINNs method converges to the iteration of diffusion limit equations, thereby ensuring the proposed method maintains the asymptotic preserving property. A theoretical analysis about the approximation errors of the iterative solution of the VEF-PINNs method is given. To evaluate the performance of the method, comparisons are made with the vanilla PINNs and Asymptotic Preserving Neural Networks (APNNs) based on micro-macro decomposition. The results reveal that the proposed VEF-PINNs can effectively solve LRTEs in various opacity regimes and can enhance the solving efficiency to a certain extent.
{"title":"Physics informed neural networks with variable Eddington factor iteration for linear radiative transfer equations","authors":"Yuhang Wu , Jianhua Huang , Xu Qian , Wenjun Sun","doi":"10.1016/j.cpc.2025.109879","DOIUrl":"10.1016/j.cpc.2025.109879","url":null,"abstract":"<div><div>In this paper, a Physics Informed Neural Networks (PINNs) method based on Variable Eddington Factor (VEF) acceleration iteration is proposed to address the time-dependent linear radiative transfer equations (LRTEs), which exhibit the characteristics of multi-scale and high dimensionality. Firstly, the factors relating to the failure of the vanilla PINNs in solving LRTEs within the diffusion regime are analyzed by the Neural Tangent Kernel (NTK) theory. Subsequently, the VEF-PINNs method is established, where PINNs are employed to handle the radiative transfer equations and the analytic VEF equations that are used to accelerate the iteration process. It is demonstrated that as the Knudsen number <em>ε</em> approaches 0, the VEF-PINNs method converges to the iteration of diffusion limit equations, thereby ensuring the proposed method maintains the asymptotic preserving property. A theoretical analysis about the approximation errors of the iterative solution of the VEF-PINNs method is given. To evaluate the performance of the method, comparisons are made with the vanilla PINNs and Asymptotic Preserving Neural Networks (APNNs) based on micro-macro decomposition. The results reveal that the proposed VEF-PINNs can effectively solve LRTEs in various opacity regimes and can enhance the solving efficiency to a certain extent.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"318 ","pages":"Article 109879"},"PeriodicalIF":3.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145263524","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 : 2025-10-01DOI: 10.1016/j.cpc.2025.109880
Amishga Alphonius , Lucka Barbeau , Bruno Blais , Olivier Gaboriault , Olivier Guévremont , Justin Lamouche , Pierre Laurentin , Oreste Marquis , Peter Munch , Victor Oliveira Ferreira , Hélène Papillon-Laroche , Paul Alexander Patience , Laura Prieto Saavedra , Mikael Vaillant
<div><div><span>Lethe</span> is an open-source Computational Fluid Dynamics (CFD) software framework with extensive multiphase and multiphysics capabilities. By leveraging the <span>deal.II</span> open-source framework, <span>Lethe</span> finite element solvers scale well on modern high-performance computers while possessing advanced features such as dynamic mesh adaptation, load-balancing, isoparametric high-order capabilities, and a fully-fledged Discrete Element Method (DEM) module. To facilitate contributions from the community, <span>Lethe</span> is extensively tested with continuous integration using over 450 unit and functional tests. Furthermore, <span>Lethe</span> contains 74 fully documented examples with pre-processing and post-processing steps to allow users to learn how to rapidly use and modify the framework. In this article, we give an overview of the simulation models available within <span>Lethe</span> and illustrate these capabilities with a selected list of examples including turbulent and multiphase flows.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> <span>Lethe</span></div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/mc5trb4kd3.1</span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span>https://github.com/chaos-polymtl/lethe</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> Apache-2.0</div><div><em>Programming language:</em> C++</div><div><em>Nature of problem:</em> Single-phase incompressible flows of Newtonian and generalized Newtonian fluids. Granular flows of cohesive or non-cohesive spherical particles. Multiphase flows, including particle-laden (solid-liquid and solid-gas) flows and fluid-fluid (gas-liquid and liquid-liquid) flows. Multiphysics coupling with heat transfer.</div><div><em>Solution method:</em> <span>Lethe</span> uses stabilized continuous Galerkin finite element formulations to solve the incompressible Navier-Stokes equations and other partial differential equations. <span>Lethe</span> utilizes the DEM to simulate granular flows. For particle-laden flow simulations, <span>Lethe</span> uses an unresolved CFD-DEM approach for flows containing numerous spherical particles (<span><math><mo>></mo><msup><mrow><mn>10</mn></mrow><mrow><mn>3</mn></mrow></msup></math></span>), while a resolved CFD-DEM approach is used for flows with few spherical or non-spherical particles (<100). For gas-liquid and liquid-liquid flows, Volume of Fluid (VOF) or Cahn–Hilliard (CH) models are used.</div><div><em>Additional comments including restrictions and unusual features:</em> <span>Lethe</span> possesses both matrix-based and matrix-free CFD solvers for incompressible flows. The matrix-free solver efficiently simulates larger problem sizes with more than 1B unknowns, but only supports hexahedral (structured or unstructured) meshes, whereas the matrix-based solver supports both tetrahedral an
{"title":"Lethe 1.0: An open-source parallel high-order computational fluid dynamics software framework for single and multiphase flows","authors":"Amishga Alphonius , Lucka Barbeau , Bruno Blais , Olivier Gaboriault , Olivier Guévremont , Justin Lamouche , Pierre Laurentin , Oreste Marquis , Peter Munch , Victor Oliveira Ferreira , Hélène Papillon-Laroche , Paul Alexander Patience , Laura Prieto Saavedra , Mikael Vaillant","doi":"10.1016/j.cpc.2025.109880","DOIUrl":"10.1016/j.cpc.2025.109880","url":null,"abstract":"<div><div><span>Lethe</span> is an open-source Computational Fluid Dynamics (CFD) software framework with extensive multiphase and multiphysics capabilities. By leveraging the <span>deal.II</span> open-source framework, <span>Lethe</span> finite element solvers scale well on modern high-performance computers while possessing advanced features such as dynamic mesh adaptation, load-balancing, isoparametric high-order capabilities, and a fully-fledged Discrete Element Method (DEM) module. To facilitate contributions from the community, <span>Lethe</span> is extensively tested with continuous integration using over 450 unit and functional tests. Furthermore, <span>Lethe</span> contains 74 fully documented examples with pre-processing and post-processing steps to allow users to learn how to rapidly use and modify the framework. In this article, we give an overview of the simulation models available within <span>Lethe</span> and illustrate these capabilities with a selected list of examples including turbulent and multiphase flows.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> <span>Lethe</span></div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/mc5trb4kd3.1</span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span>https://github.com/chaos-polymtl/lethe</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> Apache-2.0</div><div><em>Programming language:</em> C++</div><div><em>Nature of problem:</em> Single-phase incompressible flows of Newtonian and generalized Newtonian fluids. Granular flows of cohesive or non-cohesive spherical particles. Multiphase flows, including particle-laden (solid-liquid and solid-gas) flows and fluid-fluid (gas-liquid and liquid-liquid) flows. Multiphysics coupling with heat transfer.</div><div><em>Solution method:</em> <span>Lethe</span> uses stabilized continuous Galerkin finite element formulations to solve the incompressible Navier-Stokes equations and other partial differential equations. <span>Lethe</span> utilizes the DEM to simulate granular flows. For particle-laden flow simulations, <span>Lethe</span> uses an unresolved CFD-DEM approach for flows containing numerous spherical particles (<span><math><mo>></mo><msup><mrow><mn>10</mn></mrow><mrow><mn>3</mn></mrow></msup></math></span>), while a resolved CFD-DEM approach is used for flows with few spherical or non-spherical particles (<100). For gas-liquid and liquid-liquid flows, Volume of Fluid (VOF) or Cahn–Hilliard (CH) models are used.</div><div><em>Additional comments including restrictions and unusual features:</em> <span>Lethe</span> possesses both matrix-based and matrix-free CFD solvers for incompressible flows. The matrix-free solver efficiently simulates larger problem sizes with more than 1B unknowns, but only supports hexahedral (structured or unstructured) meshes, whereas the matrix-based solver supports both tetrahedral an","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"318 ","pages":"Article 109880"},"PeriodicalIF":3.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145263439","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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