Computer Physics Communications最新文献

{"title":"Integrating physics and data-driven approaches: An explainable and uncertainty-aware hybrid model for wind turbine power prediction","authors":"Alfonso Gijón ,&nbsp;Simone Eiraudo ,&nbsp;Antonio Manjavacas ,&nbsp;Daniele Salvatore Schiera ,&nbsp;Miguel Molina-Solana ,&nbsp;Juan Gómez-Romero","doi":"10.1016/j.cpc.2025.109761","DOIUrl":"10.1016/j.cpc.2025.109761","url":null,"abstract":"<div><div>The rapid growth of the wind energy sector underscores the urgent need to optimize turbine operations and ensure effective maintenance through early fault detection systems. While traditional empirical and physics-based models offer approximate predictions of power generation based on wind speed, they often fail to capture the complex, non-linear relationships between other input variables and the resulting power output. Data-driven machine learning methods present a promising avenue for improving wind turbine modeling by leveraging large datasets, enhancing prediction accuracy but often at the cost of interpretability. In this study, we propose a hybrid semi-parametric model that combines the strengths of both approaches, applied to a dataset from a wind farm with four turbines. The model integrates a physics-inspired submodel, providing a reasonable approximation of power generation, with a non-parametric submodel that predicts the residuals. This non-parametric submodel is trained on a broader range of variables to account for phenomena not captured by the physics-based component. The hybrid model achieves a 37% improvement in prediction accuracy over the physics-based model and performs comparably to a purely data-driven reference model, while offering additional advantages in terms of explainability and robustness. To further enhance interpretability, SHAP values are used to analyze the influence of input features on the residual submodel's output. Additionally, prediction uncertainties are quantified using a conformalized quantile regression method. The combination of these techniques, alongside the physics grounding of the parametric submodel, provides a flexible, accurate, and reliable framework. Ultimately, this study opens the door for evaluating the impact of unmodeled phenomena on wind turbine power generation, offering a basis for potential optimization.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"316 ","pages":"Article 109761"},"PeriodicalIF":7.2,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144672337","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}

{"title":"Machine learning assisted canonical sampling (Mlacs)","authors":"Aloïs Castellano ,&nbsp;Romuald Béjaud ,&nbsp;Pauline Richard ,&nbsp;Olivier Nadeau ,&nbsp;Clément Duval ,&nbsp;Grégory Geneste ,&nbsp;Gabriel Antonius ,&nbsp;Johann Bouchet ,&nbsp;Antoine Levitt ,&nbsp;Gabriel Stoltz ,&nbsp;François Bottin","doi":"10.1016/j.cpc.2025.109730","DOIUrl":"10.1016/j.cpc.2025.109730","url":null,"abstract":"&lt;div&gt;&lt;div&gt;The acceleration of material property calculations while maintaining &lt;em&gt;ab initio&lt;/em&gt; accuracy (1 meV/atom) is one of the major challenges in computational physics. In this paper, we introduce a Python package enhancing the computation of (finite temperature) material properties at the &lt;em&gt;ab initio&lt;/em&gt; level using machine learning interatomic potentials (MLIP). The Machine Learning Assisted Canonical Sampling (&lt;span&gt;Mlacs&lt;/span&gt;) method, grounded in a self-consistent variational approach, iteratively trains a MLIP using an active learning strategy in order to significantly reduce the computational cost of &lt;em&gt;ab initio&lt;/em&gt; simulations.&lt;/div&gt;&lt;div&gt;&lt;span&gt;Mlacs&lt;/span&gt; offers a modular and user-friendly interface that seamlessly integrates Density Functional Theory (DFT) codes, MLIP potentials, and molecular dynamics packages, enabling a wide range of applications, while maintaining a near-DFT accuracy. These include sampling the canonical ensemble of a system, performing free energy calculations, transition path sampling, and geometry optimization, all by utilizing surrogate MLIP potentials, in place of &lt;em&gt;ab initio&lt;/em&gt; calculations.&lt;/div&gt;&lt;div&gt;This paper provides a comprehensive overview of the theoretical foundations and implementation of the &lt;span&gt;Mlacs&lt;/span&gt; method. We also demonstrate its accuracy and efficiency through various examples, showcasing the capabilities of the &lt;span&gt;Mlacs&lt;/span&gt; package.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Program summary&lt;/h3&gt;&lt;div&gt;&lt;em&gt;Program title:&lt;/em&gt; &lt;span&gt;Mlacs&lt;/span&gt;&lt;/div&gt;&lt;div&gt;&lt;em&gt;CPC Library link to program files:&lt;/em&gt; &lt;span&gt;&lt;span&gt;https://doi.org/10.17632/vtfzjnc6cr.1&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;&lt;/div&gt;&lt;div&gt;&lt;em&gt;Licensing provisions:&lt;/em&gt; GNU General Public License, version 3&lt;/div&gt;&lt;div&gt;&lt;em&gt;Programming language:&lt;/em&gt; Python&lt;/div&gt;&lt;div&gt;&lt;em&gt;Nature of problem:&lt;/em&gt; Numerous material properties, whether related to the ground state or finite temperature thermodynamic quantities, cannot be deduced from classical simulations and require accurate but highly demanding &lt;em&gt;ab initio&lt;/em&gt; calculations. Enhancing the efficiency of these simulations while preserving a near-&lt;em&gt;ab initio&lt;/em&gt; accuracy is one of the biggest challenges in modern computational physics.&lt;/div&gt;&lt;div&gt;&lt;em&gt;Solution method:&lt;/em&gt; The emergence of MLIP potentials enables us to tackle this issue. The method implemented in &lt;span&gt;Mlacs&lt;/span&gt; allows for the acceleration of &lt;em&gt;ab initio&lt;/em&gt; calculations by training a MLIP potential on the fly. At the end of the simulation, &lt;span&gt;Mlacs&lt;/span&gt; produces an optimal local surrogate potential, a database that includes a sample of representative atomic configurations with their statistical weights, as well as information on convergence control and thermodynamic quantities.&lt;/div&gt;&lt;div&gt;&lt;em&gt;Additional comments:&lt;/em&gt; The seminal version is defined in [1]. The new version [2], &lt;span&gt;Mlacs&lt;/span&gt; v1.0.2, works on various architectures and includes several new features.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;References&lt;/h3&gt;&lt;div&gt;&lt;ul&gt;","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"316 ","pages":"Article 109730"},"PeriodicalIF":7.2,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679111","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}

{"title":"Generative flow induced neural architecture search: Towards discovering optimal architecture in wavelet neural operator","authors":"Hartej Soin ,&nbsp;Tapas Tripura ,&nbsp;Souvik Chakraborty","doi":"10.1016/j.cpc.2025.109755","DOIUrl":"10.1016/j.cpc.2025.109755","url":null,"abstract":"<div><div>We propose a generative flow-induced neural architecture search algorithm. The proposed approach devises simple feed-forward neural networks to learn stochastic policies to generate sequences of architecture hyperparameters such that the generated states are in proportion to the reward from the terminal state. We demonstrate the efficacy of the proposed search algorithm on the wavelet neural operator (WNO), where we learn a policy to generate a sequence of hyperparameters like wavelet basis and activation operators for wavelet integral blocks. While the trajectory of the generated wavelet basis and activation sequence is cast as flow, the policy is learned by minimizing the flow violation between each state in the trajectory and maximizing the reward from the terminal state. In the terminal state, we train WNO simultaneously to guide the search. We propose using the negative exponent of the WNO loss on the validation dataset as the reward function. While the grid search-based neural architecture generation algorithms foresee every combination, the proposed framework generates the most probable sequence based on the positive reward from the terminal state, thereby reducing exploration time. Compared to reinforcement learning schemes, where complete episodic training is required to get the reward, the proposed algorithm generates the hyperparameter trajectory sequentially. Through four fluid mechanics-oriented problems, we illustrate that the learned policies can sample the best-performing architecture of the neural operator, thereby improving the performance of the vanilla wavelet neural operator. We compare the performance of the proposed flow-based search strategy with that of a Monte Carlo Tree Search (MCTS) -based algorithm and observe an improvement of ≥23% in the resulting optimal architecture.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"316 ","pages":"Article 109755"},"PeriodicalIF":7.2,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144654901","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}

{"title":"Best of both worlds: Enforcing detailed balance in machine learning models of transition rates","authors":"Anjana Anu Talapatra ,&nbsp;Anup Pandey ,&nbsp;Matthew S. Wilson ,&nbsp;Ying Wai Li ,&nbsp;Ghanshyam Pilania ,&nbsp;Blas Pedro Uberuaga ,&nbsp;Danny Perez","doi":"10.1016/j.cpc.2025.109752","DOIUrl":"10.1016/j.cpc.2025.109752","url":null,"abstract":"<div><div>The slow microstructural evolution of materials often plays a key role in determining material properties. When the unit steps of the evolution process are slow, direct simulation approaches such as molecular dynamics become prohibitive and Kinetic Monte-Carlo (kMC) algorithms, where the state-to-state evolution of the system is represented in terms of a continuous-time Markov chain, are instead frequently relied upon to efficiently predict long-time evolution. The accuracy of kMC simulations however relies on the complete and accurate knowledge of reaction pathways and corresponding kinetics. This requirement becomes extremely stringent in complex systems such as concentrated alloys where the astronomical number of local atomic configurations makes the <em>a priori</em> tabulation of all possible transitions impractical. Machine learning models of transition kinetics have been used to mitigate this problem by enabling the efficient on-the-fly prediction of kinetic parameters. While conventional KMC methods based on transition state theory naturally yield reversible dynamics that exactly obey the detailed balance criterion, providing strong guarantees on the properties of the stationary distribution, many recently-proposed ML-based approaches to barrier predictions provide no such guarantees. In this study, we derive conditions under which physics-informed ML architectures exactly enforce the detailed balance condition by construction, even when relying on non-extensive descriptions of states in terms of local environments around mobile defects. Using the diffusion of a vacancy in a concentrated alloy as an example, we show that such ML architectures also exhibit superior performance in terms of prediction accuracy, demonstrating that the imposition of physical constraints can facilitate the accurate learning of barriers at no increase in computational cost.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"316 ","pages":"Article 109752"},"PeriodicalIF":7.2,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679112","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}

{"title":"Learnable activation functions in physics-informed neural networks for solving partial differential equations","authors":"Afrah Farea,&nbsp;Mustafa Serdar Celebi","doi":"10.1016/j.cpc.2025.109753","DOIUrl":"10.1016/j.cpc.2025.109753","url":null,"abstract":"<div><div>Physics-Informed Neural Networks (PINNs) have emerged as a promising approach for solving Partial Differential Equations (PDEs). However, they face challenges related to spectral bias (the tendency to learn low-frequency components while struggling with high-frequency features) and unstable convergence dynamics (mainly stemming from the multi-objective nature of the PINN loss function). These limitations impact their accuracy for solving problems involving rapid oscillations, sharp gradients, and complex boundary behaviors. We systematically investigate learnable activation functions as a solution to these challenges, comparing Multilayer Perceptrons (MLPs) using fixed and learnable activation functions against Kolmogorov-Arnold Networks (KANs) that employ learnable basis functions. Our evaluation spans diverse PDE types, including linear and non-linear wave problems, mixed-physics systems, and fluid dynamics. Using empirical Neural Tangent Kernel (NTK) analysis and Hessian eigenvalue decomposition, we assess spectral bias and convergence stability of the models. Our results reveal a trade-off between expressivity and training convergence stability. While learnable activation functions work well in simpler architectures, they encounter scalability issues in complex networks due to the higher functional dimensionality. Counterintuitively, we find that low spectral bias alone does not guarantee better accuracy, as functions with broader NTK eigenvalue spectra may exhibit convergence instability. We demonstrate that activation function selection remains inherently problem-specific, with different bases showing distinct advantages for particular PDE characteristics. We believe these insights will help in the design of more robust neural PDE solvers.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"315 ","pages":"Article 109753"},"PeriodicalIF":7.2,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670689","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}

{"title":"A new gradient descent divergence cleaning method with optimized high-order low-dissipation TENO schemes for ideal magnetohydrodynamic simulations","authors":"Zeyu Chen ,&nbsp;Kuangxu Chen","doi":"10.1016/j.cpc.2025.109744","DOIUrl":"10.1016/j.cpc.2025.109744","url":null,"abstract":"<div><div>In this study, we present a new Gradient Descent Divergence Cleaning (GDDC) method, integrated with optimized high-order low-dissipation Target Essentially Non-Oscillatory (TENO) schemes, for the simulation of ideal magnetohydrodynamic (MHD) flows while ensuring a robust divergence-free constraint. The GDDC method constructs a loss function that quantifies the total divergence error. By computing the gradient of this loss function with respect to the magnetic field, the method integrates seamlessly into the Runge-Kutta time-stepping scheme to enable divergence cleaning consistent with the spatial discretization in a discrete sense. Unlike traditional divergence cleaning methods that depend on a non-physical variable <em>ψ</em> and associated parameters controlling its transport velocity and decay rate, the GDDC method introduces only a single parameter, <em>η</em>, and preserves the original hyperbolic eigensystem of the ideal MHD equations. Numerical experiments demonstrate that the method is highly insensitive to variations of <em>η</em> across three orders of magnitude. Additionally, we build the GDDC method upon the optimized TENO (TENO-OPT) schemes. Leveraging the TENO schemes' inherent ability to detect discontinuities, dynamic linear weights are used to combine sub-stencils into an optimal large stencil that avoids crossing discontinuities. Extensive numerical tests demonstrate that the GDDC method coupled with the 5th- and 6th-order TENO-OPT schemes effectively captures discontinuities in ideal MHD flows with low dissipation and without spurious oscillations. We developed our code using NVIDIA's CUDA Fortran and evaluated its performance on a single NVIDIA A800 GPU. For an ideal MHD simulation that employs a 5th-order TENO-OPT scheme, each Runge-Kutta time-stepping stage took approximately 1.118 seconds at a grid resolution of 0.268 billion points. Both computational time and GPU memory usage increase linearly with grid size, with roughly 4 million grid points requiring 1 GB of memory. Based on these scaling trends, the enhanced memory capacity of the NVIDIA H200 GPU could theoretically support up to approximately 0.560 billion grid points. The code is available on <span><span>https://github.com/FallenCastle/MHD_GDDC_GPU</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"315 ","pages":"Article 109744"},"PeriodicalIF":7.2,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657201","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}

{"title":"WaterLily.jl: A differentiable and backend-agnostic Julia solver for incompressible viscous flow around dynamic bodies","authors":"Gabriel D. Weymouth ,&nbsp;Bernat Font","doi":"10.1016/j.cpc.2025.109748","DOIUrl":"10.1016/j.cpc.2025.109748","url":null,"abstract":"<div><div>Integrating computational fluid dynamics (CFD) solvers into optimization and machine-learning frameworks is hampered by the rigidity of classic computational languages and the slow performance of more flexible high-level languages. In this work, we introduce WaterLily.jl: an open-source incompressible viscous flow solver written in the Julia language. An immersed boundary method is used to enforce the effect of solid boundaries on flow past complex geometries with arbitrary motions. The small code base is multidimensional, multiplatform and backend-agnostic, i.e. it supports serial and multithreaded CPU execution, and GPUs of different vendors. Additionally, the pure-Julia implementation allows the solver to be fully differentiable using automatic differentiation. The computational cost per time step and grid point remains constant with increasing grid size on CPU backends, and we measure up to two orders of magnitude speed-up on a supercomputer GPU compared to serial CPU execution. This leads to comparable performance with low-level CFD solvers written in C and Fortran on research-scale problems, opening up exciting possible future applications on the cutting edge of machine-learning research.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"315 ","pages":"Article 109748"},"PeriodicalIF":7.2,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670688","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}

{"title":"Spatio-temporal neural operator on complex geometries","authors":"Xiaoyang Fu ,&nbsp;Gengxiang Chen ,&nbsp;Yingguang Li ,&nbsp;Xu Liu ,&nbsp;Lu Chen ,&nbsp;Qinglu Meng ,&nbsp;Changqing Liu ,&nbsp;Xiaozhong Hao","doi":"10.1016/j.cpc.2025.109754","DOIUrl":"10.1016/j.cpc.2025.109754","url":null,"abstract":"<div><div>In engineering and Artificial Intelligence (AI) scenarios, learning spatio-temporal dynamics is not only associated with high-resolution time series, but also often accompanied by complex computational domains. Consequently, learning high-dimensional spatio-temporal dynamics on complex geometries remains a significant challenge in both machine learning and engineering fields. Recently, Neural Operators have emerged as the lasted method which can learn mapping between functions with a discretisation resolution invariant network framework, and have increasingly been applied in engineering scenarios involving spatio-temporal dynamics. However, most existing neural operators require uniform grid data defined over regular spatio-temporal domains, making them inapplicable for engineering problems involving complex geometries. To address this limitation, we propose a Spatio-Temporal Neural Operator (STNO) that can learn mappings between functions defined simultaneously in the temporal domain and the complex geometric domain. The proposed STNO features a spatio-temporal iterative kernel integration module that separately encodes high-dimensional spatial information and temporal information into different low-dimensional frequency spaces, thus enabling efficient parameterised learning. Additionally, the model structure of STNO is independent of the discretisation resolution in both temporal and spatial domains. Experiments on several engineering case studies demonstrate the effectiveness and generality of the proposed STNO in learning spatio-temporal dynamics on complex geometries.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"315 ","pages":"Article 109754"},"PeriodicalIF":7.2,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657203","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}

{"title":"Efficient computation of particle-fluid and particle-particle interactions in compressible flow","authors":"Anna Schwarz ,&nbsp;Patrick Kopper ,&nbsp;Emilian de Staercke ,&nbsp;Andrea Beck","doi":"10.1016/j.cpc.2025.109743","DOIUrl":"10.1016/j.cpc.2025.109743","url":null,"abstract":"<div><div>Particle collisions are the primary mechanism of inter-particle momentum and energy exchange for dense particle-laden flow. Accurate approximation of this collision operator in four-way coupled Euler–Lagrange approaches remains challenging due to the associated computational cost. Adopting a deterministic collision model and a hard-sphere (binary collision) approach eases time step constraints but imposes non-locality on distributed memory architectures, necessitating the inclusion of collision partners from each grid element in the vicinity. Retaining high-order accuracy and parallel efficiency also ties into the correct and compact treatment of the particle-fluid coupling, where adequate kernels are required to effectively project the momentum and thermal energy exchange terms of the particles to the Eulerian grid. In this work, we present an efficient particle collision and projection operator based on an MPI+MPI hybrid approach to enable time-resolved and high-order accurate simulations of compressible, four-way coupled particle-laden flows at dense concentrations. A distinct feature of the proposed particle collision algorithm is the efficient calculation of exact binary inter-particle collisions on arbitrary core counts by facilitating intranode data exchange through direct load/store operations and internode communication using one-sided communication. Combining the particle operator with a hybrid discretization operator based on a high-order discontinuous Galerkin method and a localized low-order finite volume operator allows an accurate treatment of highly compressible particle-laden flows. The approach is extensively validated against a range of benchmark problems. Contrary to literature, the scaling properties are demonstrated on state-of-the-art high performance computing systems, encompassing one-way to four-way coupled simulations. Finally, the proposed algorithm is compatible with unstructured, curved high-order grids which permits the handling of complex geometries as is emphasized by application of the framework to large-scale application cases.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"315 ","pages":"Article 109743"},"PeriodicalIF":7.2,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634071","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}

{"title":"A modified Cayley transform for SU(3) molecular dynamics simulations","authors":"Kevin Schäfers ,&nbsp;Michael Peardon ,&nbsp;Michael Günther","doi":"10.1016/j.cpc.2025.109740","DOIUrl":"10.1016/j.cpc.2025.109740","url":null,"abstract":"<div><div>We propose a modification to the Cayley transform that defines a suitable local parameterization for the special unitary group SU(3). The new mapping is used to construct splitting methods for separable Hamiltonian systems whose phase space is the cotangent bundle of SU(3) or, more general, <span><math><msup><mrow><mtext>SU(3)</mtext></mrow><mrow><mi>N</mi></mrow></msup><mo>,</mo><mspace></mspace><mi>N</mi><mo>∈</mo><mi>N</mi></math></span>. Special attention is given to the hybrid Monte Carlo algorithm for gauge field generation in lattice quantum chromodynamics. We show that the use of the modified Cayley transform instead of the matrix exponential neither affects the time-reversibility nor the volume-preservation of the splitting method. Furthermore, the advantages and disadvantages of the Cayley-based algorithms are discussed and illustrated in pure gauge field simulations.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"315 ","pages":"Article 109740"},"PeriodicalIF":7.2,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144623571","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}