Pub Date : 2025-12-06DOI: 10.1016/j.cpc.2025.109982
Mayanak K Gupta
Machine learning and computing advancements have made it possible to carry out simulations over longer lengths and timescales. This has opened up new opportunities for understanding materials in different thermodynamic conditions. These large-scale simulations help analyze experimental measurements such as inelastic scattering and study diffusion in solid electrolytes for potential use in future batteries. However, analyzing these large datasets presents challenges in extracting useful thermodynamic and transport properties. To address these challenges, the Fortran-based code MDLAB has been developed. This code processes large-scale molecular dynamics simulation trajectories from various software and computes important quantities like mean squared displacements, phonon spectra, pair-distribution functions, simulated neutron/X-ray spectra and more. This comprehensive approach allows for a deeper understanding of material behavior, ultimately enhancing our overall grasp of condensed matter physics.
{"title":"A molecular dynamics postprocessing tool for analyzing the structure and dynamics of materials","authors":"Mayanak K Gupta","doi":"10.1016/j.cpc.2025.109982","DOIUrl":"10.1016/j.cpc.2025.109982","url":null,"abstract":"<div><div>Machine learning and computing advancements have made it possible to carry out simulations over longer lengths and timescales. This has opened up new opportunities for understanding materials in different thermodynamic conditions. These large-scale simulations help analyze experimental measurements such as inelastic scattering and study diffusion in solid electrolytes for potential use in future batteries. However, analyzing these large datasets presents challenges in extracting useful thermodynamic and transport properties. To address these challenges, the Fortran-based code MDLAB has been developed. This code processes large-scale molecular dynamics simulation trajectories from various software and computes important quantities like mean squared displacements, phonon spectra, pair-distribution functions, simulated neutron/X-ray spectra and more. This comprehensive approach allows for a deeper understanding of material behavior, ultimately enhancing our overall grasp of condensed matter physics.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"320 ","pages":"Article 109982"},"PeriodicalIF":3.4,"publicationDate":"2025-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880069","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}
We present the C++ program for Standard Model (SM) extensions that feature a Dark Matter (DM) candidate. The tool allows to efficiently scan the parameter spaces of these models to find parameter combinations that lead to relic density values which are compatible with the measured value within the uncertainty specified by the user. The code computes the relic density for freeze-out (co-)annihilation processes. The user can choose between several pre-installed models or any arbitrary other model featuring a discrete symmetry, by solely providing the corresponding FeynRules model files. The code automatically generates the required (co-)annihilation amplitudes and thermally averaged cross sections, including the total widths in the s-channel mediators, and solves the Boltzmann equation to determine the relic density. It can easily be linked to other tools like e.g. ScannerS to check for the relevant theoretical and experimental constraints, or to BSMPT to investigate the phase history of the model and possibly related gravitational waves signals.
{"title":"RelExt: A new dark matter tool for the exploration of dark matter models","authors":"Rodrigo Capucha , Karim Elyaouti , Margarete Mühlleitner , Johann Plotnikov , Rui Santos","doi":"10.1016/j.cpc.2025.109968","DOIUrl":"10.1016/j.cpc.2025.109968","url":null,"abstract":"<div><div>We present the <span>C++</span> program <span><math><mi>RelExt</mi></math></span> for Standard Model (SM) extensions that feature a Dark Matter (DM) candidate. The tool allows to efficiently scan the parameter spaces of these models to find parameter combinations that lead to relic density values which are compatible with the measured value within the uncertainty specified by the user. The code computes the relic density for freeze-out (co-)annihilation processes. The user can choose between several pre-installed models or any arbitrary other model featuring a discrete <span><math><msub><mi>Z</mi><mn>2</mn></msub></math></span> symmetry, by solely providing the corresponding <span>FeynRules</span> model files. The code automatically generates the required (co-)annihilation amplitudes and thermally averaged cross sections, including the total widths in the <em>s</em>-channel mediators, and solves the Boltzmann equation to determine the relic density. It can easily be linked to other tools like e.g. <span>ScannerS</span> to check for the relevant theoretical and experimental constraints, or to <span>BSMPT</span> to investigate the phase history of the model and possibly related gravitational waves signals.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"320 ","pages":"Article 109968"},"PeriodicalIF":3.4,"publicationDate":"2025-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145836599","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 : 2025-12-06DOI: 10.1016/j.cpc.2025.109983
Janghoon Seo , Gahyung Jo , Jae-Min Kwon , Eisung Yoon
We present a computationally efficient implementation of the nonlinear Rosenbluth-Fokker-Planck (RFP) collision operator for multi-species kinetic simulations within the discontinuous Galerkin (DG) framework. Inter-species collisions with significant mass disparities require high-order Gaussian quadrature integration to accurately resolve the steep gradients in the Rosenbluth potentials of slower species. To mitigate the computational overhead associated with numerous quadrature points, we employ precomputed integration matrices. Since the conventional upwind scheme for the DG method is not compatible with precomputed matrices, we implement the Harten, Lax and van Leer (HLL) flux formulation for advective flow calculations at cell boundaries. Conservation of momentum and energy is ensured through an additional advective-diffusive operator, utilizing the slow-to-fast species collision as a reference state. We address the numerical challenge of artificial non-vanishing collisional effects at equilibrium through compensatory terms, thereby achieving stable collisional equilibrium states. Comprehensive numerical benchmarks validate both the efficiency and accuracy of our proposed scheme. In particular, our model achieves robust interspecies collisional equilibrium even under conditions of extreme mass disparity and relatively low velocity resolution.
{"title":"Multi-species Rosenbluth Fokker-Planck collision operator for discontinuous Galerkin method","authors":"Janghoon Seo , Gahyung Jo , Jae-Min Kwon , Eisung Yoon","doi":"10.1016/j.cpc.2025.109983","DOIUrl":"10.1016/j.cpc.2025.109983","url":null,"abstract":"<div><div>We present a computationally efficient implementation of the nonlinear Rosenbluth-Fokker-Planck (RFP) collision operator for multi-species kinetic simulations within the discontinuous Galerkin (DG) framework. Inter-species collisions with significant mass disparities require high-order Gaussian quadrature integration to accurately resolve the steep gradients in the Rosenbluth potentials of slower species. To mitigate the computational overhead associated with numerous quadrature points, we employ precomputed integration matrices. Since the conventional upwind scheme for the DG method is not compatible with precomputed matrices, we implement the Harten, Lax and van Leer (HLL) flux formulation for advective flow calculations at cell boundaries. Conservation of momentum and energy is ensured through an additional advective-diffusive operator, utilizing the slow-to-fast species collision as a reference state. We address the numerical challenge of artificial non-vanishing collisional effects at equilibrium through compensatory terms, thereby achieving stable collisional equilibrium states. Comprehensive numerical benchmarks validate both the efficiency and accuracy of our proposed scheme. In particular, our model achieves robust interspecies collisional equilibrium even under conditions of extreme mass disparity and relatively low velocity resolution.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"320 ","pages":"Article 109983"},"PeriodicalIF":3.4,"publicationDate":"2025-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145733049","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 : 2025-12-04DOI: 10.1016/j.cpc.2025.109984
R. Rossi , A. Murari , T. Craciunescu , N. Rutigliano , I. Wyss , J. Vega , P. Gaudio , M. Gelfusa , on behalf of JET Contributors* and EUROfusion Tokamak Exploitation Team
Autoencoders are neural networks capable of learning compact representations of data through unsupervised learning. By encoding input data into a lower-dimensional space and subsequently reconstructing it, they enable efficient feature extraction, denoising, anomaly detection, and other applications. This work develops autoencoder-based methodologies tailored to time-dependent problems, specifically for reconstructing hidden dynamics, modelling governing equations, and detecting causal relationships.
A physics-informed autoencoder (PIC-AE) is introduced to impose physical or mathematical constraints on the latent representation, allowing the discovery of fundamental dynamics and model parameters. The PIC-AE effectively reconstructs equivalent dynamical systems from indirect measurements, as exemplified by numerical tests based on the Lotka-Volterra system of equations. It has been applied to edge-localized modes (ELMs) in nuclear fusion plasmas to assess whether they follow a Lotka-Volterra model and the results indicate the need for alternative sets of equations.
For causality detection, a novel autoencoder-based method has been developed to overcome the limitations of traditional techniques. This new approach accurately identifies causal relationships while providing a probabilistic measure of their strength. Applied to nuclear fusion data, it has confirmed the causal influence of ion cyclotron resonance heating (ICRH) on sawtooth crashes, reproducing previous findings obtained with different methodologies and extending the analysis to the spatio-temporal domain.
Although initially designed for nuclear fusion applications, the proposed methodologies are broadly applicable to any scientific and technological domain, in which time series analysis is crucial. Indeed, the developed tools have the representational capabilities of deep learning networks but are much less prone to overfitting and can be accurate even whit sparse data. Future work will explore alternative representations for ELMs and further validate the causality detection method across different datasets.
{"title":"On the use of autoencoders to study the dynamics and the causality relations of complex systems with applications to nuclear fusion","authors":"R. Rossi , A. Murari , T. Craciunescu , N. Rutigliano , I. Wyss , J. Vega , P. Gaudio , M. Gelfusa , on behalf of JET Contributors* and EUROfusion Tokamak Exploitation Team","doi":"10.1016/j.cpc.2025.109984","DOIUrl":"10.1016/j.cpc.2025.109984","url":null,"abstract":"<div><div>Autoencoders are neural networks capable of learning compact representations of data through unsupervised learning. By encoding input data into a lower-dimensional space and subsequently reconstructing it, they enable efficient feature extraction, denoising, anomaly detection, and other applications. This work develops autoencoder-based methodologies tailored to time-dependent problems, specifically for reconstructing hidden dynamics, modelling governing equations, and detecting causal relationships.</div><div>A physics-informed autoencoder (PIC-AE) is introduced to impose physical or mathematical constraints on the latent representation, allowing the discovery of fundamental dynamics and model parameters. The PIC-AE effectively reconstructs equivalent dynamical systems from indirect measurements, as exemplified by numerical tests based on the Lotka-Volterra system of equations. It has been applied to edge-localized modes (ELMs) in nuclear fusion plasmas to assess whether they follow a Lotka-Volterra model and the results indicate the need for alternative sets of equations.</div><div>For causality detection, a novel autoencoder-based method has been developed to overcome the limitations of traditional techniques. This new approach accurately identifies causal relationships while providing a probabilistic measure of their strength. Applied to nuclear fusion data, it has confirmed the causal influence of ion cyclotron resonance heating (ICRH) on sawtooth crashes, reproducing previous findings obtained with different methodologies and extending the analysis to the spatio-temporal domain.</div><div>Although initially designed for nuclear fusion applications, the proposed methodologies are broadly applicable to any scientific and technological domain, in which time series analysis is crucial. Indeed, the developed tools have the representational capabilities of deep learning networks but are much less prone to overfitting and can be accurate even whit sparse data. Future work will explore alternative representations for ELMs and further validate the causality detection method across different datasets.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"320 ","pages":"Article 109984"},"PeriodicalIF":3.4,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145836432","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}
<div><div>This paper presents GeoDualSPHysics, an open-source, graphics processing unit (GPU)-accelerated smoothed particle hydrodynamics (SPH) solver designed for simulating large-deformation geomaterial and their interactions with multi-body systems. Built upon the popular open-source SPH solver DualSPHysics, the solver leverages its highly parallelised SPH scheme empowered by the CUDA parallelisation while extending its capabilities to large-deformation geomechanics problems with particles up to the order of 10⁸ on a single GPU. The SPH geomechanics model is enhanced by a noise-free stress treatment technique that stabilizes and accurately resolves stress fields, as well as an extended modified Dynamic Boundary Condition (mDBC) ensuring first-order consistency in solid boundary modelling. Additionally, the coupling interface between DualSPHysics and the multi-body dynamics solver Project Chrono is adapted for simulating interactions between geomaterials and multiple interacting rigid bodies. Benchmark validations confirm the solver’s accuracy in resolving geotechnical failures, impact forces on solid boundaries, and geomaterial-multibody system interactions. GPU profiling of the newly implemented CUDA kernels demonstrates their performance metrics are similar to those of the original DualSPHysics solver. Performance evaluations demonstrate its saving in memory usage of 30-50% and improvements in computational efficiency over existing SPH geomechanics solvers, achieving practical simulation speeds for systems with tens of millions of particles and showing a speedup of up to 180x compared to the optimised multi-core CPU implementation. These advances position GeoDualSPHysics as a versatile, efficient tool for high-fidelity simulations of complex geotechnical systems.</div></div><div><h3>Program summary</h3><div>Program title: GeoDualSPHysics</div><div>CPC Library link to program files: <span><span>https://doi.org/10.17632/z4sh62y97g.1</span><svg><path></path></svg></span></div><div>Licensing provisions: GNU Lesser General Public License</div><div>Programming language: C++ and CUDA</div><div>Nature of problem: Simulating large deformations in geomaterials and their interactions with movable or fixed solid bodies is critical for addressing engineering challenges such as landslides, soil-machine interactions, and off-road vehicle mobility. While the Smoothed Particle Hydrodynamics (SPH) method is well-suited for modelling continuum-based geomaterial behaviour in these scenarios, critical computational barriers persist, including: (1) numerical instabilities and unphysical noise in large-deformation regimes, (2) inefficiency in scaling simulations to millions of particles for real-world systems, and (3) inadequate frameworks for robust, two-way coupling between deformable geomaterials and multi-body systems. Overcoming these limitations demands stabilized SPH formulations, high-performance computing architectures, and two-way coupling with multibody
{"title":"GeoDualSPHysics: a high-performance SPH solver for large deformation modelling of geomaterials with two-way coupling to multi-body systems","authors":"Ruofeng Feng , Jidong Zhao , Georgios Fourtakas , Benedict D Rogers","doi":"10.1016/j.cpc.2025.109965","DOIUrl":"10.1016/j.cpc.2025.109965","url":null,"abstract":"<div><div>This paper presents GeoDualSPHysics, an open-source, graphics processing unit (GPU)-accelerated smoothed particle hydrodynamics (SPH) solver designed for simulating large-deformation geomaterial and their interactions with multi-body systems. Built upon the popular open-source SPH solver DualSPHysics, the solver leverages its highly parallelised SPH scheme empowered by the CUDA parallelisation while extending its capabilities to large-deformation geomechanics problems with particles up to the order of 10⁸ on a single GPU. The SPH geomechanics model is enhanced by a noise-free stress treatment technique that stabilizes and accurately resolves stress fields, as well as an extended modified Dynamic Boundary Condition (mDBC) ensuring first-order consistency in solid boundary modelling. Additionally, the coupling interface between DualSPHysics and the multi-body dynamics solver Project Chrono is adapted for simulating interactions between geomaterials and multiple interacting rigid bodies. Benchmark validations confirm the solver’s accuracy in resolving geotechnical failures, impact forces on solid boundaries, and geomaterial-multibody system interactions. GPU profiling of the newly implemented CUDA kernels demonstrates their performance metrics are similar to those of the original DualSPHysics solver. Performance evaluations demonstrate its saving in memory usage of 30-50% and improvements in computational efficiency over existing SPH geomechanics solvers, achieving practical simulation speeds for systems with tens of millions of particles and showing a speedup of up to 180x compared to the optimised multi-core CPU implementation. These advances position GeoDualSPHysics as a versatile, efficient tool for high-fidelity simulations of complex geotechnical systems.</div></div><div><h3>Program summary</h3><div>Program title: GeoDualSPHysics</div><div>CPC Library link to program files: <span><span>https://doi.org/10.17632/z4sh62y97g.1</span><svg><path></path></svg></span></div><div>Licensing provisions: GNU Lesser General Public License</div><div>Programming language: C++ and CUDA</div><div>Nature of problem: Simulating large deformations in geomaterials and their interactions with movable or fixed solid bodies is critical for addressing engineering challenges such as landslides, soil-machine interactions, and off-road vehicle mobility. While the Smoothed Particle Hydrodynamics (SPH) method is well-suited for modelling continuum-based geomaterial behaviour in these scenarios, critical computational barriers persist, including: (1) numerical instabilities and unphysical noise in large-deformation regimes, (2) inefficiency in scaling simulations to millions of particles for real-world systems, and (3) inadequate frameworks for robust, two-way coupling between deformable geomaterials and multi-body systems. Overcoming these limitations demands stabilized SPH formulations, high-performance computing architectures, and two-way coupling with multibody","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"320 ","pages":"Article 109965"},"PeriodicalIF":3.4,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145733045","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 : 2025-12-02DOI: 10.1016/j.cpc.2025.109963
Xiang Dong , Yu-Chang Sun , Chu-Cheng Pan , Ao-Yan Cheng , Ao-Bo Wang , Hao Cai , Kai Zhu
This paper introduces a novel Partial Wave Analysis Code Generator (PWACG) that automatically generates high-performance partial wave analysis codes. This is achieved by leveraging the JAX automatic differentiation library and the jinja2 template engine. The resulting code is constructed using the high-performance API of JAX, and includes support for the Newton’s Conjugate Gradient optimization method, as well as the full utilization of parallel computing capabilities offered by GPUs. By harnessing these advanced computing techniques, PWACG demonstrates a significant advantage in efficiently identifying global optimal points compared to conventional partial wave analysis software packages.
PROGRAM SUMMARY
• Program Title: PWACG: Partial Wave Analysis Code Generator
• CPC Library link to program files:https://doi.org/10.17632/47ppcnx77x.1
• Licensing provisions: This software is distributed under the MIT License.
• Nature of problem: The program addresses the need for high-performance computational tools in high-energy physics partial wave analysis (PWA). It introduces the Newton-conjugate gradient method for optimization, enhancing the accuracy and stability of fits.
• Solution method: PWACG employs code generation and automatic differentiation techniques to automate the creation of PWA code. It leverages the computational capabilities of JAX for efficient execution and supports advanced features such as multi-GPU computation.
{"title":"PWACG: Partial wave analysis code generator supporting Newton-conjugate gradient method","authors":"Xiang Dong , Yu-Chang Sun , Chu-Cheng Pan , Ao-Yan Cheng , Ao-Bo Wang , Hao Cai , Kai Zhu","doi":"10.1016/j.cpc.2025.109963","DOIUrl":"10.1016/j.cpc.2025.109963","url":null,"abstract":"<div><div>This paper introduces a novel Partial Wave Analysis Code Generator (PWACG) that automatically generates high-performance partial wave analysis codes. This is achieved by leveraging the JAX automatic differentiation library and the jinja2 template engine. The resulting code is constructed using the high-performance API of JAX, and includes support for the Newton’s Conjugate Gradient optimization method, as well as the full utilization of parallel computing capabilities offered by GPUs. By harnessing these advanced computing techniques, PWACG demonstrates a significant advantage in efficiently identifying global optimal points compared to conventional partial wave analysis software packages.</div><div><strong>PROGRAM SUMMARY</strong></div><div>• <strong>Program Title:</strong> PWACG: Partial Wave Analysis Code Generator</div><div>• <strong>CPC Library link to program files:</strong> <span><span>https://doi.org/10.17632/47ppcnx77x.1</span><svg><path></path></svg></span></div><div>• <strong>Licensing provisions:</strong> This software is distributed under the MIT License.</div><div>• <strong>Programming language:</strong> Python</div><div>• <strong>External routines/libraries:</strong> jaxlib, jax, jinja2, matplotlib, numpy, scipy</div><div>• <strong>Nature of problem:</strong> The program addresses the need for high-performance computational tools in high-energy physics partial wave analysis (PWA). It introduces the Newton-conjugate gradient method for optimization, enhancing the accuracy and stability of fits.</div><div>• <strong>Solution method:</strong> PWACG employs code generation and automatic differentiation techniques to automate the creation of PWA code. It leverages the computational capabilities of JAX for efficient execution and supports advanced features such as multi-GPU computation.</div><div>• <strong>GitHub repository:</strong> <span><span>https://github.com/caihao/PWACG</span><svg><path></path></svg></span></div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"320 ","pages":"Article 109963"},"PeriodicalIF":3.4,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786464","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 : 2025-12-01DOI: 10.1016/j.cpc.2025.109967
Lucas Amoudruz , Sergey Litvinov , Riccardo Murri , Volker Eyrich , Jens Zudrop , Costas Bekas , Petros Koumoutsakos
We investigate the capabilities of cloud computing for large-scale, tightly-coupled simulations of biological fluids in complex geometries, traditionally performed in supercomputing centers. We demonstrate scalable and efficient simulations in the public cloud. We perform meso-scale simulations of blood flow in image-reconstructed capillaries, and examine targeted drug delivery by artificial bacterial flagella (ABFs). The simulations deploy dissipative particle dynamics (DPD) with two software frameworks, Mirheo(developed by our team) and LAMMPS. Mirheoexhibits remarkable weak scalability for up to 512 GPUs. Similarly, LAMMPS demonstrated excellent weak scalability for pure solvent as well as for blood suspensions and ABFs in reconstructed retinal capillaries. In particular, LAMMPS maintained weak scaling above 90 % on the cloud for up to 2000 cores. Our findings demonstrate that cloud computing can support tightly coupled, large-scale scientific simulations with competitive performance.
{"title":"Scalable, cloud-based simulations of blood flow and targeted drug delivery in retinal capillaries","authors":"Lucas Amoudruz , Sergey Litvinov , Riccardo Murri , Volker Eyrich , Jens Zudrop , Costas Bekas , Petros Koumoutsakos","doi":"10.1016/j.cpc.2025.109967","DOIUrl":"10.1016/j.cpc.2025.109967","url":null,"abstract":"<div><div>We investigate the capabilities of cloud computing for large-scale, tightly-coupled simulations of biological fluids in complex geometries, traditionally performed in supercomputing centers. We demonstrate scalable and efficient simulations in the public cloud. We perform meso-scale simulations of blood flow in image-reconstructed capillaries, and examine targeted drug delivery by artificial bacterial flagella (ABFs). The simulations deploy dissipative particle dynamics (DPD) with two software frameworks, Mirheo(developed by our team) and LAMMPS. Mirheoexhibits remarkable weak scalability for up to 512 GPUs. Similarly, LAMMPS demonstrated excellent weak scalability for pure solvent as well as for blood suspensions and ABFs in reconstructed retinal capillaries. In particular, LAMMPS maintained weak scaling above 90 % on the cloud for up to 2000 cores. Our findings demonstrate that cloud computing can support tightly coupled, large-scale scientific simulations with competitive performance.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"320 ","pages":"Article 109967"},"PeriodicalIF":3.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681671","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 : 2025-11-28DOI: 10.1016/j.cpc.2025.109966
Seokhwan Min, Jonghwa Shin
Controlling the scattering of waves from multi-shell spherical systems and particles is a crucial aspect in many applications in photonics such as superdirective antennae and structural coloring. Nevertheless, the effective design of such systems is non-trivial due to the coexistence of topological (number of shells and their material composition) and shape (shell thicknesses) parameters. Thus far, general-purpose algorithms such as parameter sweeps, gradient descent, differential evolution, and deep neural networks have been used to optimize particle shape under one or a few fixed topologies, limiting the complexity and effectiveness of the resulting designs. To address this shortcoming, we present a topology nucleation algorithm that allows the concurrent design of particle topology and shape through the use of a topology derivative expression derived from the transfer matrix formulation of the analytical Mie scattering theory. The principle behind our algorithm can readily be applied to the design of multi-shell spherical systems in other fields such as acoustics and quantum transport.
{"title":"Eschallot: A topology nucleation algorithm for designing stratified, spherically symmetric systems that exhibit complex angular scattering of electromagnetic waves","authors":"Seokhwan Min, Jonghwa Shin","doi":"10.1016/j.cpc.2025.109966","DOIUrl":"10.1016/j.cpc.2025.109966","url":null,"abstract":"<div><div>Controlling the scattering of waves from multi-shell spherical systems and particles is a crucial aspect in many applications in photonics such as superdirective antennae and structural coloring. Nevertheless, the effective design of such systems is non-trivial due to the coexistence of topological (number of shells and their material composition) and shape (shell thicknesses) parameters. Thus far, general-purpose algorithms such as parameter sweeps, gradient descent, differential evolution, and deep neural networks have been used to optimize particle shape under one or a few fixed topologies, limiting the complexity and effectiveness of the resulting designs. To address this shortcoming, we present a topology nucleation algorithm that allows the concurrent design of particle topology and shape through the use of a topology derivative expression derived from the transfer matrix formulation of the analytical Mie scattering theory. The principle behind our algorithm can readily be applied to the design of multi-shell spherical systems in other fields such as acoustics and quantum transport.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"320 ","pages":"Article 109966"},"PeriodicalIF":3.4,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145733048","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 : 2025-11-27DOI: 10.1016/j.cpc.2025.109957
Nicolás F. Barrera , Javiera Cabezas-Escares , Mònica Calatayud , Francisco Munoz , Tatiana Gómez , Carlos Cárdenas
FukuiGrid is a Python-based code that calculates Fukui functions and Fukui potentials in systems with periodic boundary conditions, making it a valuable tool for solid-state chemistry. It focuses on chemical reactivity descriptors from Conceptual Density-Functional Theory (c-DFT) and enables the calculation of Fukui functions through methods such as finite differences and interpolation. FukuiGrid addresses the challenges associated with periodic boundary conditions when calculating the electrostatic potential of a Fukui function (known as the Fukui potential) by integrating various corrections to alleviate the compensating background of charge. These corrections include the electrode approach and self-consistent potential correction as post-processing techniques. This package is compatible with VASP outputs and specifically designed to study the reactivity of surfaces and adsorbates. It generates surface reactivity maps and provides insights into adsorption site preferences, as well as regions prone to electron donation or withdrawal. FukuiGrid has been designed to make c-DFT easier for the surface chemistry community.
{"title":"FukuiGrid: A Python code for c-DFT in solid-state chemistry","authors":"Nicolás F. Barrera , Javiera Cabezas-Escares , Mònica Calatayud , Francisco Munoz , Tatiana Gómez , Carlos Cárdenas","doi":"10.1016/j.cpc.2025.109957","DOIUrl":"10.1016/j.cpc.2025.109957","url":null,"abstract":"<div><div>FukuiGrid is a Python-based code that calculates Fukui functions and Fukui potentials in systems with periodic boundary conditions, making it a valuable tool for solid-state chemistry. It focuses on chemical reactivity descriptors from Conceptual Density-Functional Theory (c-DFT) and enables the calculation of Fukui functions through methods such as finite differences and interpolation. FukuiGrid addresses the challenges associated with periodic boundary conditions when calculating the electrostatic potential of a Fukui function (known as the Fukui potential) by integrating various corrections to alleviate the compensating background of charge. These corrections include the electrode approach and self-consistent potential correction as post-processing techniques. This package is compatible with VASP outputs and specifically designed to study the reactivity of surfaces and adsorbates. It generates surface reactivity maps and provides insights into adsorption site preferences, as well as regions prone to electron donation or withdrawal. FukuiGrid has been designed to make c-DFT easier for the surface chemistry community.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"320 ","pages":"Article 109957"},"PeriodicalIF":3.4,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681707","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 : 2025-11-27DOI: 10.1016/j.cpc.2025.109959
Zhixin Lu, Guo Meng, Roman Hatzky, Philipp Lauber, Matthias Hoelzl
The features of the TRIMEG-GKX code are described with emphasis on the exploration using novel/different schemes compared to other gyrokinetic codes, particularly the use of object-oriented programming, filter/buffer-free treatment, and a high-order piecewise field-aligned finite element method. The TRIMEG-GKX code solves the electromagnetic gyrokinetic equation using the particle-in-cell scheme, taking into account multi-species effects and shear Alfvén physics. The mixed-variable/pullback scheme has been implemented to enable electromagnetic studies. This code is parallelized using particle decomposition and domain cloning among computing nodes, replacing traditional domain decomposition techniques. The applications to study the micro- and macro-instabilities are demonstrated, including the energetic-particle-driven Alfvén eigenmode, ion temperature gradient mode, and kinetic ballooning mode. Good performance is achieved in both ad hoc and experimentally reconstructed equilibria, such as those of the ASDEX Upgrade (AUG), Tokamak á configuration variable (TCV), and the Joint European Torus (JET). Future studies of edge physics using the high-order C1 finite element method for triangular meshes in the TRIMEG-C1 code will be built upon the same numerical methods.
{"title":"TRIMEG-GKX: An electromagnetic gyrokinetic particle code with a piecewise field-aligned finite element method for micro- and macro-instability studies in tokamak core plasmas","authors":"Zhixin Lu, Guo Meng, Roman Hatzky, Philipp Lauber, Matthias Hoelzl","doi":"10.1016/j.cpc.2025.109959","DOIUrl":"10.1016/j.cpc.2025.109959","url":null,"abstract":"<div><div>The features of the TRIMEG-GKX code are described with emphasis on the exploration using novel/different schemes compared to other gyrokinetic codes, particularly the use of object-oriented programming, filter/buffer-free treatment, and a high-order piecewise field-aligned finite element method. The TRIMEG-GKX code solves the electromagnetic gyrokinetic equation using the particle-in-cell scheme, taking into account multi-species effects and shear Alfvén physics. The mixed-variable/pullback scheme has been implemented to enable electromagnetic studies. This code is parallelized using particle decomposition and domain cloning among computing nodes, replacing traditional domain decomposition techniques. The applications to study the micro- and macro-instabilities are demonstrated, including the energetic-particle-driven Alfvén eigenmode, ion temperature gradient mode, and kinetic ballooning mode. Good performance is achieved in both ad hoc and experimentally reconstructed equilibria, such as those of the ASDEX Upgrade (AUG), Tokamak á configuration variable (TCV), and the Joint European Torus (JET). Future studies of edge physics using the high-order <em>C</em><sup>1</sup> finite element method for triangular meshes in the TRIMEG-C1 code will be built upon the same numerical methods.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"320 ","pages":"Article 109959"},"PeriodicalIF":3.4,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681673","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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