Pub Date : 2026-01-25DOI: 10.1016/j.cpc.2026.110052
Danh Nam Nguyen , Jae Hun Lee , Chun Sang Yoo
<div><div>The official OpenFOAM distributions are currently not well-suited for accurate simulations of laminar reacting flows, primarily due to the restrictive Sutherland transport model and the oversimplified unity Lewis number assumption. These limitations can be addressed by employing a detailed transport model (DTM) grounded in kinetic gas theory. However, this approach significantly increases computational cost. To resolve this trade-off, we present a newly developed framework, <em>DTLreactingFoam</em>, designed for simulating laminar flames with integrated detailed transport and chemical kinetics while maintaining computational efficiency. The first level of cost reduction is achieved by incorporating a polynomial-fit transport model (FTM). Further acceleration is provided by a time-correlated thermophysical property evaluation (coTHERM) method, which dynamically updates properties at each time step or iteration by exploiting their temporal correlations. The framework is validated through a series of canonical laminar flame simulations. The results show excellent agreement with experimental measurements and benchmark software, confirming the accurate implementation of both the DTM and FTM. Moreover, validation results demonstrate that coupling the coTHERM method with either the DTM or FTM enables high-fidelity laminar flame simulations with substantially reduced computational cost. Notably, using the coTHERM method in conjunction with the FTM achieves up to a 77% reduction in computational time compared to the direct use of the DTM, without compromising accuracy.</div><div><strong>PROGRAM SUMMARY</strong> <em>Program Title:</em> DTLreactingFoam <em>CPC Library link to program files:</em> (to be added by Technical Editor) <em>Developer’s repository link (OF-12):</em> <span><span>https://github.com/danhnam11/DTLreactingFoam-12</span><svg><path></path></svg></span> <em>Developer’s repository link (OF-10):</em> <span><span>https://github.com/danhnam11/DTLreactingFoam-10</span><svg><path></path></svg></span> <em>Developer’s repository link (OF-8):</em> <span><span>https://github.com/danhnam11/DTLreactingFoam-8</span><svg><path></path></svg></span> <em>Code Ocean capsule:</em> (to be added by Technical Editor) <em>Licensing provisions:</em> GPLv3 <em>Programming language:</em> C++ <em>Supplementary material: Nature of problem:</em> Using the detailed transport model (DTM) based on the principles of kinetic gas theory can accurately simulate laminar reacting flows in OpenFOAM (OF). However, the accuracy comes at the cost of significantly greater computational effort since all thermophyscal properties are recomputed in every single cell and at every time step throughout the simulation when using DTM in OF. <em>Solution method:</em> In reacting flow simulations, the evolution of thermodynamic state variables and species concentrations between successive steps are correlated. The change in these quantities from one step to the next are often minimal
{"title":"DTLreactingFoam: An efficient CFD tool for laminar reacting flow simulations using detailed chemistry and transport with time-correlated thermophysical properties","authors":"Danh Nam Nguyen , Jae Hun Lee , Chun Sang Yoo","doi":"10.1016/j.cpc.2026.110052","DOIUrl":"10.1016/j.cpc.2026.110052","url":null,"abstract":"<div><div>The official OpenFOAM distributions are currently not well-suited for accurate simulations of laminar reacting flows, primarily due to the restrictive Sutherland transport model and the oversimplified unity Lewis number assumption. These limitations can be addressed by employing a detailed transport model (DTM) grounded in kinetic gas theory. However, this approach significantly increases computational cost. To resolve this trade-off, we present a newly developed framework, <em>DTLreactingFoam</em>, designed for simulating laminar flames with integrated detailed transport and chemical kinetics while maintaining computational efficiency. The first level of cost reduction is achieved by incorporating a polynomial-fit transport model (FTM). Further acceleration is provided by a time-correlated thermophysical property evaluation (coTHERM) method, which dynamically updates properties at each time step or iteration by exploiting their temporal correlations. The framework is validated through a series of canonical laminar flame simulations. The results show excellent agreement with experimental measurements and benchmark software, confirming the accurate implementation of both the DTM and FTM. Moreover, validation results demonstrate that coupling the coTHERM method with either the DTM or FTM enables high-fidelity laminar flame simulations with substantially reduced computational cost. Notably, using the coTHERM method in conjunction with the FTM achieves up to a 77% reduction in computational time compared to the direct use of the DTM, without compromising accuracy.</div><div><strong>PROGRAM SUMMARY</strong> <em>Program Title:</em> DTLreactingFoam <em>CPC Library link to program files:</em> (to be added by Technical Editor) <em>Developer’s repository link (OF-12):</em> <span><span>https://github.com/danhnam11/DTLreactingFoam-12</span><svg><path></path></svg></span> <em>Developer’s repository link (OF-10):</em> <span><span>https://github.com/danhnam11/DTLreactingFoam-10</span><svg><path></path></svg></span> <em>Developer’s repository link (OF-8):</em> <span><span>https://github.com/danhnam11/DTLreactingFoam-8</span><svg><path></path></svg></span> <em>Code Ocean capsule:</em> (to be added by Technical Editor) <em>Licensing provisions:</em> GPLv3 <em>Programming language:</em> C++ <em>Supplementary material: Nature of problem:</em> Using the detailed transport model (DTM) based on the principles of kinetic gas theory can accurately simulate laminar reacting flows in OpenFOAM (OF). However, the accuracy comes at the cost of significantly greater computational effort since all thermophyscal properties are recomputed in every single cell and at every time step throughout the simulation when using DTM in OF. <em>Solution method:</em> In reacting flow simulations, the evolution of thermodynamic state variables and species concentrations between successive steps are correlated. The change in these quantities from one step to the next are often minimal","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"322 ","pages":"Article 110052"},"PeriodicalIF":3.4,"publicationDate":"2026-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146057321","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 : 2026-01-24DOI: 10.1016/j.cpc.2026.110053
Yutong Wu, Zecheng Qiu, Junxiang Yang
This paper presents a numerical model for simulating the dynamics of multiple interacting vesicles using a multi-phase-field framework. We use N phase-field variables, each possibly containing multiple disconnected vesicles, and enforce volume and surface-area constraint per variable. Their evolution is governed by the variational derivatives of a total energy functional encompassing bending elasticity, surface area and volume conservation, and inter-vesicle repulsion. A semi-implicit finite difference scheme is developed to discretize the system, achieving numerical stability and efficiency. Extensive three-dimensional simulations demonstrate the method’s capability to maintain physical constraints and accurately capture complex vesicle deformations and interactions across various configurations. The simulation code corresponding to Sections 4.3.4 and 4.3.5 (Figs. 10 & 11) in this paper can be accessed at https://github.com/aaron-z-chiu/multiple-vesicles.
{"title":"A three-dimensional multi-phase-field vesicles model and its practical finite difference solver","authors":"Yutong Wu, Zecheng Qiu, Junxiang Yang","doi":"10.1016/j.cpc.2026.110053","DOIUrl":"10.1016/j.cpc.2026.110053","url":null,"abstract":"<div><div>This paper presents a numerical model for simulating the dynamics of multiple interacting vesicles using a multi-phase-field framework. We use <em>N</em> phase-field variables, each possibly containing multiple disconnected vesicles, and enforce volume and surface-area constraint per variable. Their evolution is governed by the variational derivatives of a total energy functional encompassing bending elasticity, surface area and volume conservation, and inter-vesicle repulsion. A semi-implicit finite difference scheme is developed to discretize the system, achieving numerical stability and efficiency. Extensive three-dimensional simulations demonstrate the method’s capability to maintain physical constraints and accurately capture complex vesicle deformations and interactions across various configurations. The simulation code corresponding to Sections 4.3.4 and 4.3.5 (Figs. 10 & 11) in this paper can be accessed at <span><span>https://github.com/aaron-z-chiu/multiple-vesicles</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"321 ","pages":"Article 110053"},"PeriodicalIF":3.4,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146073661","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 : 2026-01-21DOI: 10.1016/j.cpc.2026.110036
Sojeong Park , Wei Liu , Simon Julian Lauw , Wooseop Kwak , Chandra S. Verma , Hwee Kuan Lee
Molecular dynamics (MD) simulation is an essential tool for condensed matter physics, materials science, structural/mechanistic biology, and multi-agent systems. Despite their successes, traditional numerical integration methods for solving Hamilton’s equations of motion are computationally intensive, limiting simulations to short time scales. Recent advancements in machine learning have opened new avenues for accelerating MD simulations. This work introduces the Local Update Function network (LUFnet), a transformer-based neural network designed to increase time integration step sizes significantly while maintaining simulation stability and accuracy. LUFnet integrates local spatial and temporal information, enabling efficient rollout for long-time-scale simulations. By preserving key symmetries such as translational invariance, Galilean coordinate transformation invariance, and particle exchange symmetry, LUFnet achieves robust performance across different thermodynamic states. LUFnet is designed to accommodate general physical models (e.g., Lennard-Jones, Coulomb potential, on lattice systems). Its framework allows the model to be trained on small systems and directly applied to larger systems, maintaining computation efficiency and computation memory usage that scales linearly with the number of particles. Benchmarked on Lennard-Jones systems, LUFnet demonstrated minimal accuracy degradation even after rollout over many large time integration steps, offering an effective approach to molecular dynamics simulations.
{"title":"Scalable neural network driven molecular dynamics simulation","authors":"Sojeong Park , Wei Liu , Simon Julian Lauw , Wooseop Kwak , Chandra S. Verma , Hwee Kuan Lee","doi":"10.1016/j.cpc.2026.110036","DOIUrl":"10.1016/j.cpc.2026.110036","url":null,"abstract":"<div><div>Molecular dynamics (MD) simulation is an essential tool for condensed matter physics, materials science, structural/mechanistic biology, and multi-agent systems. Despite their successes, traditional numerical integration methods for solving Hamilton’s equations of motion are computationally intensive, limiting simulations to short time scales. Recent advancements in machine learning have opened new avenues for accelerating MD simulations. This work introduces the Local Update Function network (LUFnet), a transformer-based neural network designed to increase time integration step sizes significantly while maintaining simulation stability and accuracy. LUFnet integrates local spatial and temporal information, enabling efficient rollout for long-time-scale simulations. By preserving key symmetries such as translational invariance, Galilean coordinate transformation invariance, and particle exchange symmetry, LUFnet achieves robust performance across different thermodynamic states. LUFnet is designed to accommodate general physical models (e.g., Lennard-Jones, Coulomb potential, on lattice systems). Its framework allows the model to be trained on small systems and directly applied to larger systems, maintaining computation efficiency and computation memory usage that scales linearly with the number of particles. Benchmarked on Lennard-Jones systems, LUFnet demonstrated minimal accuracy degradation even after rollout over many large time integration steps, offering an effective approach to molecular dynamics simulations.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"322 ","pages":"Article 110036"},"PeriodicalIF":3.4,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076756","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 : 2026-01-21DOI: 10.1016/j.cpc.2026.110039
Ping-Hsuan Tsai , Seung Whan Chung , Debojyoti Ghosh , John Loffeld , Youngsoo Choi , Jonathan L. Belof
Despite advancements in high-performance computing and modern numerical algorithms, computational cost remains prohibitive for multi-query kinetic plasma simulations. In this work, we develop data-driven reduced-order models (ROMs) for collisionless electrostatic plasma dynamics, based on the kinetic Vlasov-Poisson equation. Our ROM approach projects the equation onto a linear subspace defined by the proper orthogonal decomposition (POD) modes. We introduce an efficient tensorial method to update the nonlinear term using a precomputed third-order tensor. We capture multiscale behavior with a minimal number of POD modes by decomposing the solution manifold into multiple time windows and creating temporally local ROMs. We consider two strategies for decomposition: one based on the physical time and the other based on the electric field energy. Applied to the 1D1V Vlasov–Poisson simulations, that is, prescribed E-field, Landau damping, and two-stream instability, we demonstrate that our ROMs accurately capture the total energy of the system both for parametric and time extrapolation cases. The temporally local ROMs are more efficient and accurate than the single ROM. In addition, in the two-stream instability case, we show that the energy-windowing reduced-order model (EW-ROM) is more efficient and accurate than the time-windowing reduced-order model (TW-ROM). With the tensorial approach, EW-ROM solves the equation approximately 90 times faster than Eulerian simulations while maintaining a maximum relative error of 7.5% for the training data and 11% for the testing data.
{"title":"Local reduced-order modeling for electrostatic plasmas by physics-informed solution manifold decomposition","authors":"Ping-Hsuan Tsai , Seung Whan Chung , Debojyoti Ghosh , John Loffeld , Youngsoo Choi , Jonathan L. Belof","doi":"10.1016/j.cpc.2026.110039","DOIUrl":"10.1016/j.cpc.2026.110039","url":null,"abstract":"<div><div>Despite advancements in high-performance computing and modern numerical algorithms, computational cost remains prohibitive for multi-query kinetic plasma simulations. In this work, we develop data-driven reduced-order models (ROMs) for collisionless electrostatic plasma dynamics, based on the kinetic Vlasov-Poisson equation. Our ROM approach projects the equation onto a linear subspace defined by the proper orthogonal decomposition (POD) modes. We introduce an efficient tensorial method to update the nonlinear term using a precomputed third-order tensor. We capture multiscale behavior with a minimal number of POD modes by decomposing the solution manifold into multiple time windows and creating temporally local ROMs. We consider two strategies for decomposition: one based on the physical time and the other based on the electric field energy. Applied to the 1D1V Vlasov–Poisson simulations, that is, prescribed E-field, Landau damping, and two-stream instability, we demonstrate that our ROMs accurately capture the total energy of the system both for parametric and time extrapolation cases. The temporally local ROMs are more efficient and accurate than the single ROM. In addition, in the two-stream instability case, we show that the energy-windowing reduced-order model (EW-ROM) is more efficient and accurate than the time-windowing reduced-order model (TW-ROM). With the tensorial approach, EW-ROM solves the equation approximately 90 times faster than Eulerian simulations while maintaining a maximum relative error of 7.5% for the training data and 11% for the testing data.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"322 ","pages":"Article 110039"},"PeriodicalIF":3.4,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076755","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 : 2026-01-18DOI: 10.1016/j.cpc.2026.110040
Aaron Nagel , Johannes Löwe
To simulate highly-resolved flow fields, we extend the Quantum Lattice Boltzmann Method (QLBM) to be able to simulate multiple time steps without state extraction or reinitialization. We adjust and extend given QLBM approaches from the literature to completely remove the need to measure or reinitialize the flow field in between the simulation time steps. Therefore, our algorithm does not require to sample the entire flow field at any time. We solve the linear advection-diffusion problem with periodic boundary conditions and derive all necessary equations and build the corresponding quantum circuit diagrams, including details on the QLBM blocks and explicitly drawing the circuit gates. We discuss the general decay of a QLBM step and how that effects our algorithm. The new algorithm is verified on 1D and 2D test cases using the shot method of IBMs Qiskit package. We show excellent agreement and convergence between our QLBM and the classical Lattice Boltzmann method. The conclusion section includes a discussion on the advantages of our algorithm as well as limitations and to what extent it is more efficient.
{"title":"Quantum lattice boltzmann method for multiple time steps without reinitialization for linear advection-Diffusion problems","authors":"Aaron Nagel , Johannes Löwe","doi":"10.1016/j.cpc.2026.110040","DOIUrl":"10.1016/j.cpc.2026.110040","url":null,"abstract":"<div><div>To simulate highly-resolved flow fields, we extend the Quantum Lattice Boltzmann Method (QLBM) to be able to simulate multiple time steps without state extraction or reinitialization. We adjust and extend given QLBM approaches from the literature to completely remove the need to measure or reinitialize the flow field in between the simulation time steps. Therefore, our algorithm does not require to sample the entire flow field at any time. We solve the linear advection-diffusion problem with periodic boundary conditions and derive all necessary equations and build the corresponding quantum circuit diagrams, including details on the QLBM blocks and explicitly drawing the circuit gates. We discuss the general decay of a QLBM step and how that effects our algorithm. The new algorithm is verified on 1D and 2D test cases using the <em>shot</em> method of IBMs <em>Qiskit</em> package. We show excellent agreement and convergence between our QLBM and the classical Lattice Boltzmann method. The conclusion section includes a discussion on the advantages of our algorithm as well as limitations and to what extent it is more efficient.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"321 ","pages":"Article 110040"},"PeriodicalIF":3.4,"publicationDate":"2026-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034921","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 : 2026-01-18DOI: 10.1016/j.cpc.2026.110041
Saajid Chowdhury, Jesús Pérez-Ríos
We present a MATLAB script, atomiongpu.m, which can use GPU parallelization to run several million independent simulations per day of a trapped ion interacting with a low-density cloud of atoms, calculating classical trajectories of a trapped ion and an atom starting far away. The script uses ode45gpu, which is our optimized and specialized implementation of the Runge-Kutta algorithm used in MATLAB’s ODE solver ode45. We first discuss the physical system and show how ode45gpu can, on a CPU, solve it about 7x faster than MATLAB’s ode45, leading to a 600x-3500x speedup when running a million trajectories using ode45gpu in parallel on a GPU compared to ode45 on a CPU. Then, we show how to easily modify the inputs to atomiongpu.m to account for different kinds of atoms, ions, atom-ion interactions, trap potentials, simulation parameters, initial conditions, and computational hardware, so that atomiongpu.m automatically finds the probability of complex formation, the distribution of observables such as the scattering angle and complex lifetime, and plots of specific trajectories.
PROGRAM SUMMARY
Program Title:atomiongpu.m
CPC Library link to program files:https://doi.org/10.17632/sjw4hzw9jx.1
Nature of problem: Simulate classical dynamics (Newton’s laws) of an ion and atom, with up to several million different sets of initial conditions, store the final conditions and a few other scalar observables and their distributions, and plot specific trajectories.
Solution method: Implementing the algorithm behind ode45, MATLAB’s fourth/fifth-order adaptive-timestep Runge-Kutta method for propagating ordinary differential equations, we write a single, self-contained function, ode45gpu. Then, we use MATLAB’s arrayfun to parallelize it on multiple CPUs or GPUs. Finally, we wrote the wrapper script atomiongpu.m for quickly and conveniently using ode45gpu.
Additional comments: The source code for atomiongpu.m, ode45gpu, and figures can be found on the repository.
{"title":"GPU-parallelized MATLAB software for atom-ion dynamics","authors":"Saajid Chowdhury, Jesús Pérez-Ríos","doi":"10.1016/j.cpc.2026.110041","DOIUrl":"10.1016/j.cpc.2026.110041","url":null,"abstract":"<div><div>We present a MATLAB script, <span>atomiongpu.m</span>, which can use GPU parallelization to run several million independent simulations per day of a trapped ion interacting with a low-density cloud of atoms, calculating classical trajectories of a trapped ion and an atom starting far away. The script uses <span>ode45gpu</span>, which is our optimized and specialized implementation of the Runge-Kutta algorithm used in MATLAB’s ODE solver <span>ode45</span>. We first discuss the physical system and show how <span>ode45gpu</span> can, on a CPU, solve it about 7x faster than MATLAB’s <span>ode45</span>, leading to a 600x-3500x speedup when running a million trajectories using <span>ode45gpu</span> in parallel on a GPU compared to <span>ode45</span> on a CPU. Then, we show how to easily modify the inputs to <span>atomiongpu.m</span> to account for different kinds of atoms, ions, atom-ion interactions, trap potentials, simulation parameters, initial conditions, and computational hardware, so that <span>atomiongpu.m</span> automatically finds the probability of complex formation, the distribution of observables such as the scattering angle and complex lifetime, and plots of specific trajectories.</div></div><div><h3>PROGRAM SUMMARY</h3><div><em>Program Title:</em> <span>atomiongpu.m</span></div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/sjw4hzw9jx.1</span><svg><path></path></svg></span></div><div><em>Developer’s repository link:</em> <span><span>https://github.com/saajidchowdhury/supplementGPU</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> CC0 1.0</div><div><em>Programming language:</em> MATLAB R2023a, with Parallel Computing Toolbox installed</div><div><em>Supplementary material:</em> <span><span>https://github.com/saajidchowdhury/supplementGPU</span><svg><path></path></svg></span></div><div><em>Nature of problem:</em> Simulate classical dynamics (Newton’s laws) of an ion and atom, with up to several million different sets of initial conditions, store the final conditions and a few other scalar observables and their distributions, and plot specific trajectories.</div><div><em>Solution method:</em> Implementing the algorithm behind <span>ode45</span>, MATLAB’s fourth/fifth-order adaptive-timestep Runge-Kutta method for propagating ordinary differential equations, we write a single, self-contained function, <span>ode45gpu</span>. Then, we use MATLAB’s <span>arrayfun</span> to parallelize it on multiple CPUs or GPUs. Finally, we wrote the wrapper script <span>atomiongpu.m</span> for quickly and conveniently using <span>ode45gpu</span>.</div><div><em>Additional comments:</em> The source code for <span>atomiongpu.m</span>, <span>ode45gpu</span>, and figures can be found on the repository.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"321 ","pages":"Article 110041"},"PeriodicalIF":3.4,"publicationDate":"2026-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034924","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 : 2026-01-17DOI: 10.1016/j.cpc.2025.110016
Timothée Goubault De Brugière , Nicolas Heurtel
Exactly computing the full output distribution of linear optical circuits remains a challenge, as existing methods are either time-efficient but memory-intensive or memory-efficient but slow. Moreover, any realistic simulation must account for noise, such as photon loss, and any viable quantum computing scheme based on linear optics requires feedforward. This adds additional layers of complexity in the classical simulation as one needs to deal with extra combinatorics due to, e.g, the measurement or loss scenarios. In this paper, we propose an algorithm that models the output amplitudes as partial derivatives of a multivariate polynomial. The algorithm explores the lattice of all intermediate partial derivatives, where each derivative is used to compute more efficiently ones with higher degree. In terms of memory, storing one path from the root to the leaves is sufficient to iterate over all amplitudes and requires only 2n elements, as opposed to for the fastest state of the art method. This approach effectively balances the time-memory trade-off while extending to both noisy and feedforward scenarios with negligible cost. To the best of our knowledge, this is the first approach in the literature to meet all these requirements. We demonstrate how this method enables the simulation of systems that were previously out of reach, while providing a concrete implementation and complexity analysis.
{"title":"Fast and memory-efficient strong simulation of noisy adaptive linear optical circuits","authors":"Timothée Goubault De Brugière , Nicolas Heurtel","doi":"10.1016/j.cpc.2025.110016","DOIUrl":"10.1016/j.cpc.2025.110016","url":null,"abstract":"<div><div>Exactly computing the full output distribution of linear optical circuits remains a challenge, as existing methods are either time-efficient but memory-intensive or memory-efficient but slow. Moreover, any realistic simulation must account for noise, such as photon loss, and any viable quantum computing scheme based on linear optics requires feedforward. This adds additional layers of complexity in the classical simulation as one needs to deal with extra combinatorics due to, e.g, the measurement or loss scenarios. In this paper, we propose an algorithm that models the output amplitudes as partial derivatives of a multivariate polynomial. The algorithm explores the lattice of all intermediate partial derivatives, where each derivative is used to compute more efficiently ones with higher degree. In terms of memory, storing one path from the root to the leaves is sufficient to iterate over all amplitudes and requires only 2<sup><em>n</em></sup> elements, as opposed to <span><math><mrow><mo>(</mo><mfrac><mrow><mi>n</mi><mo>+</mo><mi>m</mi><mo>−</mo><mn>1</mn></mrow><mi>n</mi></mfrac><mo>)</mo></mrow></math></span> for the fastest state of the art method. This approach effectively balances the time-memory trade-off while extending to both noisy and feedforward scenarios with negligible cost. To the best of our knowledge, this is the first approach in the literature to meet all these requirements. We demonstrate how this method enables the simulation of systems that were previously out of reach, while providing a concrete implementation and complexity analysis.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"321 ","pages":"Article 110016"},"PeriodicalIF":3.4,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034919","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 : 2026-01-16DOI: 10.1016/j.cpc.2026.110038
Li Huang
<div><div>Analytic continuation is an essential step in quantum Monte Carlo calculations. We present version 2.0 of the ACFlow package, a full-fledged open source toolkit for analytic continuation of quantum Monte Carlo simulation data. The new version adds support for three recently developed analytic continuation methods, namely the barycentric rational function approximation method, the stochastic pole expansion method, and the Nevanlinna analytical continuation method. The well-established maximum entropy method is also enhanced with the Bayesian reconstruction entropy algorithm. Furthermore, a web-based graphical user interface and a testing toolkit for analytic continuation methods are introduced. In this paper, we at first summarize the basic principles of the newly implemented analytic continuation solvers, and the most important improvements of ACFlow 2.0. Then a representative example is provided to demonstrate the new usages and features.</div><div>PROGRAM SUMMARY <em>Program Title:</em> ACFlow <em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/th6w74gwjm.2</span><svg><path></path></svg></span> <em>Developer’s repository link:</em> <span><span>https://github.com/huangli712/ACFlow</span><svg><path></path></svg></span> <em>Licensing provisions:</em> GPLv3 <em>Programming language:</em> Julia <em>Journal reference of previous version:</em> Computer Physics Communications 292, 108,863 (2023) <em>Does the new version supersede the previous version?:</em> Yes <em>Reasons for the new version:</em> Many features, including new analytic continuation solvers, a web-based graphical user interface, and a benchmark toolkit, are implemented. The user’s manual and internal tests are greatly enhanced as well. <em>Summary of revisions:</em> (1) The barycentric rational function approximation method is implemented, which is extremely fast and accurate. (2) The stochastic pole expansion method is implemented. It is a new variation of the stochastic analytic continuation method. (3) The Nevanlinna analytical continuation method is implemented. If the input Matsubara data is noise-free, this method can reach unprecedented accuracy. (4) The traditional maximum entropy method is enhanced by the Bayesian reconstruction entropy algorithm. Then it is extended to implement the positive-negative entropy formalism to support analytic continuation for off-diagonal Green’s function. (5) A web-based graphical user interface, namely ACGui, is developed. (6) A benchmark toolkit for testing various analytic continuation methods and codes, namely ACTest, is developed. (7) The documentation is polished. More examples and tests are included. <em>Nature of problem:</em> Most of the quantum Monte Carlo algorithms work on the imaginary axis. In order to extract physical observables and compare them with the experimental results, analytic continuation must be done in the post-processing stage to convert the quantum Monte Carlo simulated data from
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Pub Date : 2026-01-16DOI: 10.1016/j.cpc.2026.110031
Anatoli Fedynitch , Hans Dembinski , Anton Prosekin
<div><div>Simulations of hadronic and nuclear interactions are essential in both collider and astroparticle physics. The Chromo package provides a unified Python interface to multiple widely used hadronic event generators, including EPOS, DPMJet, Sibyll, QGSJet, and Pythia. Built on top of their original Fortran and C<span>++</span> implementations, Chromo offers a zero-overhead abstraction layer suitable for use in Python scripts, Jupyter notebooks, or from the command line, while preserving the performance of direct calls to the generators. It is easy to install via precompiled binary wheels distributed through PyPI, and it integrates well with the Scientific Python ecosystem. Chromo supports event export in HepMC, ROOT, and SVG formats and provides a consistent interface for inspecting, filtering, and modifying particle collision events. This paper describes the architecture, typical use cases, and performance characteristics of Chromo and its role in contemporary astroparticle simulations, such as in the MCEq cascade solver.</div></div><div><h3>PROGRAM SUMMARY</h3><div><em>Program Title:</em> Chromo <em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/wdf9bvwhns.1</span><svg><path></path></svg></span> <em>Developer’s repository link:</em> <span><span>https://github.com/impy-project/chromo</span><svg><path></path></svg></span> <em>Licensing provisions:</em> BSD 3-clause <em>Programming language:</em> Python, Fortran, C<span>++</span> <em>Nature of problem:</em>Simulating hadronic and nuclear interactions currently requires users to learn multiple generator APIs (Fortran or C<span>++</span>), handle different build systems, and write glue code to translate between event formats. This complexity hinders rapid prototyping in Python, makes batch scripting cumbersome, and prevents seamless integration with the broader Scientific Python ecosystem (NumPy, SciPy, Matplotlib, etc.). A unified, zero-overhead interface is needed to streamline generator access, enforce consistent event I/O, and reduce boilerplate for both collider and astroparticle physics applications. <em>Solution method:</em>Chromo provides lightweight Python bindings for supported generators. Fortran-based generators are wrapped using NumPy’s f2py, and the C<span>++</span>-based Pythia8 is exposed via pybind11. Prebuilt wheels on PyPI simplify installation across platforms. After installation, Chromo offers a consistent Python API for generating, filtering, and editing events, and for exporting results to HepMC, ROOT, or SVG formats. It can be used interactively in Python scripts or Jupyter notebooks, or as a command-line tool for drop-in substitution of CRMC in shell workflows. Chromo is also suitable for integration into complex pipelines and batch systems. <em>Additional comments including restrictions and unusual features:</em>Chromo officially supports Linux and macOS by providing prebuilt wheels for Python 3.9-3.13. While most functionality may work o
{"title":"Chromo: A high-performance python interface to hadronic event generators for collider and cosmic-ray simulations","authors":"Anatoli Fedynitch , Hans Dembinski , Anton Prosekin","doi":"10.1016/j.cpc.2026.110031","DOIUrl":"10.1016/j.cpc.2026.110031","url":null,"abstract":"<div><div>Simulations of hadronic and nuclear interactions are essential in both collider and astroparticle physics. The Chromo package provides a unified Python interface to multiple widely used hadronic event generators, including EPOS, DPMJet, Sibyll, QGSJet, and Pythia. Built on top of their original Fortran and C<span>++</span> implementations, Chromo offers a zero-overhead abstraction layer suitable for use in Python scripts, Jupyter notebooks, or from the command line, while preserving the performance of direct calls to the generators. It is easy to install via precompiled binary wheels distributed through PyPI, and it integrates well with the Scientific Python ecosystem. Chromo supports event export in HepMC, ROOT, and SVG formats and provides a consistent interface for inspecting, filtering, and modifying particle collision events. This paper describes the architecture, typical use cases, and performance characteristics of Chromo and its role in contemporary astroparticle simulations, such as in the MCEq cascade solver.</div></div><div><h3>PROGRAM SUMMARY</h3><div><em>Program Title:</em> Chromo <em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/wdf9bvwhns.1</span><svg><path></path></svg></span> <em>Developer’s repository link:</em> <span><span>https://github.com/impy-project/chromo</span><svg><path></path></svg></span> <em>Licensing provisions:</em> BSD 3-clause <em>Programming language:</em> Python, Fortran, C<span>++</span> <em>Nature of problem:</em>Simulating hadronic and nuclear interactions currently requires users to learn multiple generator APIs (Fortran or C<span>++</span>), handle different build systems, and write glue code to translate between event formats. This complexity hinders rapid prototyping in Python, makes batch scripting cumbersome, and prevents seamless integration with the broader Scientific Python ecosystem (NumPy, SciPy, Matplotlib, etc.). A unified, zero-overhead interface is needed to streamline generator access, enforce consistent event I/O, and reduce boilerplate for both collider and astroparticle physics applications. <em>Solution method:</em>Chromo provides lightweight Python bindings for supported generators. Fortran-based generators are wrapped using NumPy’s f2py, and the C<span>++</span>-based Pythia8 is exposed via pybind11. Prebuilt wheels on PyPI simplify installation across platforms. After installation, Chromo offers a consistent Python API for generating, filtering, and editing events, and for exporting results to HepMC, ROOT, or SVG formats. It can be used interactively in Python scripts or Jupyter notebooks, or as a command-line tool for drop-in substitution of CRMC in shell workflows. Chromo is also suitable for integration into complex pipelines and batch systems. <em>Additional comments including restrictions and unusual features:</em>Chromo officially supports Linux and macOS by providing prebuilt wheels for Python 3.9-3.13. While most functionality may work o","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"321 ","pages":"Article 110031"},"PeriodicalIF":3.4,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146073660","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}
In this paper, we present a 2D numerical model developed to simulate the dynamics of soft core-shell particles. To accommodate large particle deformations, the particle surface is represented as a thin shell composed of mass points that interact through elasto-plastic force laws governing their linear and angular relative displacements. Particle shape changes are controlled by these interactions, in conjunction with a uniform particle core stiffness. This model can be applied to simulate flexible beams and core-shell particles of arbitrary shape. We calibrate and verify this model by comparing the deformation of constrained beams under load with theoretical predictions. Subsequently, we explore the diametral compression of a single particle between two walls, focusing on the influence of the particle core stiffness and shell plasticity. Our findings indicate that increased core stiffness reduces particle volume change and promotes the development of flat contact areas with the walls. To further illustrate the model capabilities, we apply it to the uniaxial compaction of a granular material composed of core-shell particles. We show that, depending on the core stiffness and shell plastic threshold, the compaction leads to either a significant reduction of particle volumes or an improved pore filling due to particle shape changes. At high compaction, particle shapes vary such that elastic particles without core stiffness become mostly elongated, elastic particles with core stiffness form polygonal shapes, while plastic particles develop elliptical or highly irregular forms. Finally, we simulate the tensile fracture of a tissue composed of elastic or plastic cells, illustrating the model’s potential applicability to soft tissues that undergo both large cell deformations and fracture.
{"title":"A soft particle dynamics method based on shape degrees of freedom for core-shell particles","authors":"Yohann Trivino , Vincent Richefeu , Farhang Radjai , Komlanvi Lampoh , Jean-Yves Delenne","doi":"10.1016/j.cpc.2026.110030","DOIUrl":"10.1016/j.cpc.2026.110030","url":null,"abstract":"<div><div>In this paper, we present a 2D numerical model developed to simulate the dynamics of soft core-shell particles. To accommodate large particle deformations, the particle surface is represented as a thin shell composed of mass points that interact through elasto-plastic force laws governing their linear and angular relative displacements. Particle shape changes are controlled by these interactions, in conjunction with a uniform particle core stiffness. This model can be applied to simulate flexible beams and core-shell particles of arbitrary shape. We calibrate and verify this model by comparing the deformation of constrained beams under load with theoretical predictions. Subsequently, we explore the diametral compression of a single particle between two walls, focusing on the influence of the particle core stiffness and shell plasticity. Our findings indicate that increased core stiffness reduces particle volume change and promotes the development of flat contact areas with the walls. To further illustrate the model capabilities, we apply it to the uniaxial compaction of a granular material composed of core-shell particles. We show that, depending on the core stiffness and shell plastic threshold, the compaction leads to either a significant reduction of particle volumes or an improved pore filling due to particle shape changes. At high compaction, particle shapes vary such that elastic particles without core stiffness become mostly elongated, elastic particles with core stiffness form polygonal shapes, while plastic particles develop elliptical or highly irregular forms. Finally, we simulate the tensile fracture of a tissue composed of elastic or plastic cells, illustrating the model’s potential applicability to soft tissues that undergo both large cell deformations and fracture.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"321 ","pages":"Article 110030"},"PeriodicalIF":3.4,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034996","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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