Computer Physics Communications最新文献

{"title":"From edge detection to deep learning: image processing methods for seismic horizon tracking","authors":"Guoxu Chen ,&nbsp;Wanchang Liu ,&nbsp;Suzanne Hurter ,&nbsp;Ping Zhao ,&nbsp;Mingming Zhang ,&nbsp;Shengkang Liu","doi":"10.1016/j.cpc.2025.109717","DOIUrl":"10.1016/j.cpc.2025.109717","url":null,"abstract":"<div><div>Seismic horizon tracking is a fundamental and critical step in seismic interpretation. Efficient and robust identification of seismic horizons can significantly enhance the accuracy and efficiency of geological modeling. In light of this, this study investigates seismic horizon identification from an image processing perspective: Edge detection facilitates the extraction of overall reflectors and is suitable for preliminary interpretation, while deep learning-based segmentation is employed to identify target horizons in detailed interpretation stages. This research takes the three-dimensional seismic data from the Condabri area of the Surat Basin, Australia, as a case. It begins with preprocessing the seismic data, including amplitude gain recovery to enhance deep seismic imaging quality and 3D seismic data filtering to suppress random noise, thus facilitating subsequent seismic interpretation and analysis. After preprocessing, the overall seismic horizons of the study area were extracted using an improved Canny edge detection algorithm. The results show that the improved Canny edge detection method can effectively improve the detection ability of seismic events compared with the original operator. The edge detection results provided a foundation for further detailed interpretation by highlighting the distribution of seismic horizons. Based on these results and well-log interpretations, the target horizons were manually interpreted as training labels for the subsequent improved UNet model. Comparative and ablation experiments were also carried out to evaluate the model's performance in horizon identification. The findings demonstrated that the proposed model outperformed the original UNet model and others, such as SegNet and UNet++, showing improvements across various evaluation metrics. The prediction results on the test set further validate the model’s capability to identify continuous seismic horizons across fault zones accurately. Following vectorization, the predicted horizons exhibit high consistency with manually interpreted results, indicating the model's effectiveness in capturing complex stratigraphic features. This study provides a practical and scalable approach for improving the efficiency and accuracy of seismic interpretation workflows.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"315 ","pages":"Article 109717"},"PeriodicalIF":7.2,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313717","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":"Tadah! a Swiss army knife for developing and deployment of machine learning interatomic potentials","authors":"Marcin Kirsz ,&nbsp;Ayobami Daramola ,&nbsp;Andreas Hermann ,&nbsp;Hongxiang Zong ,&nbsp;Graeme J. Ackland","doi":"10.1016/j.cpc.2025.109701","DOIUrl":"10.1016/j.cpc.2025.109701","url":null,"abstract":"&lt;div&gt;&lt;div&gt;The &lt;em&gt;Tadah!&lt;/em&gt; code provides a versatile platform for developing and optimizing Machine Learning Interatomic Potentials (MLIPs). By integrating composite descriptors, it allows for a nuanced representation of system interactions, customized with unique cutoff functions and interaction distances. &lt;em&gt;Tadah!&lt;/em&gt; supports Bayesian Linear Regression (BLR) and Kernel Ridge Regression (KRR) to enhance model accuracy and uncertainty management. A key feature is its hyperparameter optimization cycle, iteratively refining model architecture to improve transferability. This approach incorporates performance constraints, aligning predictions with experimental and theoretical data. &lt;em&gt;Tadah!&lt;/em&gt; provides an interface for LAMMPS, enabling the deployment of MLIPs in molecular dynamics simulations. It is designed for broad accessibility, supporting parallel computations on desktop and HPC systems. &lt;em&gt;Tadah!&lt;/em&gt; leverages a modular C++ codebase, utilizing both compile-time and runtime polymorphism for flexibility and efficiency. Neural network support and predefined bonding schemes are potential future developments, and &lt;em&gt;Tadah!&lt;/em&gt; remains open to community-driven feature expansion. Comprehensive documentation and command-line tools further streamline the development and application of MLIPs.&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; Tadah!&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/vy6y3tjdr3.1&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;Developer's repository link:&lt;/em&gt; &lt;span&gt;&lt;span&gt;https://git.ecdf.ed.ac.uk/tadah&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; GPLv3&lt;/div&gt;&lt;div&gt;&lt;em&gt;Programming language:&lt;/em&gt; C++&lt;/div&gt;&lt;div&gt;&lt;em&gt;Supplementary material:&lt;/em&gt; Installation instructions and usage examples are available in the &lt;em&gt;Tadah!&lt;/em&gt; online documentation at &lt;span&gt;&lt;span&gt;https://tadah.readthedocs.io&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;Nature of problem:&lt;/em&gt; Atomistic modelling, particularly molecular dynamics, is among the most popular techniques used in physics and chemistry research. Accurate and efficient methods are required to generate forces for such simulations. Quantum mechanical calculation of the electronic structure is the “gold standard” here, but is restricted to relatively small systems. Hence, interatomic potentials have a role in allowing large scale simulations, provided they have adequate accuracy.&lt;/div&gt;&lt;div&gt;Over the past two decades, the paradigm has shifted from developing interatomic potentials using physics-informed functional forms to generating machine learning interatomic potentials (MLIPs) with more flexible mathematical forms, albeit lacking clear interpretability.&lt;/div&gt;&lt;div&gt;MLIPs typically lack physical insights in trained models, requiring comprehensive datasets from methods like density functional theory during model parametrization. While training on energies and derivatives may e","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"315 ","pages":"Article 109701"},"PeriodicalIF":7.2,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321983","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 divertor tokamak plasma equilibrium solver based on flux coordinates","authors":"Tianzuo Dong ,&nbsp;Youwen Sun ,&nbsp;Minyou Ye","doi":"10.1016/j.cpc.2025.109715","DOIUrl":"10.1016/j.cpc.2025.109715","url":null,"abstract":"<div><div>An inverse equilibrium solver named FDEQ (Flux coordinates based Divertor configuration Equilibrium solver) is developed for solving divertor tokamak equilibrium in flux coordinates based on the finite difference method. The code utilizes a flux coordinates that can align with magnetic flux surfaces in both scrape-off layer (SOL) region with open field lines and core region with closed magnetic flux surfaces. Local coordinate transformation and local fitting are used to accurately handle the singularities at x-point and magnetic axis. A stable finite difference format is constructed in flux coordinates to calculate the metrics of the flux coordinate more accurately, resulting in stable convergence and superior computational efficiency. This code provides a flexible tool to generate divertor equilibrium for studying the effects of singularity at x-points and SOL on plasma stability and transport.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"315 ","pages":"Article 109715"},"PeriodicalIF":7.2,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297874","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":"Extracting the nearest canonical equilibrium distribution via natural gradient descent method","authors":"Chao Li ,&nbsp;Xiaotao Xiao ,&nbsp;Lei Ye ,&nbsp;Zhibin Guo","doi":"10.1016/j.cpc.2025.109714","DOIUrl":"10.1016/j.cpc.2025.109714","url":null,"abstract":"<div><div>This paper presents an efficient method for numerically extracting the nearest canonical equilibrium distribution <span><math><msub><mrow><mi>f</mi></mrow><mrow><mtext>NE</mtext></mrow></msub></math></span> from an arbitrary axisymmetric distribution function of tokamak plasmas by formulating the problem as an optimization task for the discrete form of the gyrokinetic Vlasov equation. An iterative scheme utilizing natural gradient descent is employed to obtain <span><math><msub><mrow><mi>f</mi></mrow><mrow><mtext>NE</mtext></mrow></msub></math></span> with a specified numerical accuracy. This approach incorporates an enhancement algorithm in order to accelerate the convergence process for the phase space points near the trapped-passing boundary. It is found that the numerical accuracy of the new method is significantly higher than that of the commonly used direct orbit average method and overcomes the stiff numerical difficulties near the trapped-passing boundaries. Possible applications of this algorithm are also discussed.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"315 ","pages":"Article 109714"},"PeriodicalIF":7.2,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321982","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":"Physics aware machine learning for micromagnetic energy minimization: Recent algorithmic developments","authors":"Sebastian Schaffer ,&nbsp;Thomas Schrefl ,&nbsp;Harald Oezelt ,&nbsp;Norbert J. Mauser ,&nbsp;Lukas Exl","doi":"10.1016/j.cpc.2025.109719","DOIUrl":"10.1016/j.cpc.2025.109719","url":null,"abstract":"<div><div>In this work, we explore advanced machine learning techniques for minimizing Gibbs free energy in full 3D micromagnetic simulations. Building on Brown's bounds for magnetostatic self-energy, we revisit their application in the context of variational formulations of the transmission problems for the scalar and vector potential. To overcome the computational challenges posed by whole-space integrals, we reformulate these bounds on a finite domain, making the method more efficient and scalable for numerical simulation. Our approach utilizes an alternating optimization scheme for joint minimization of Brown's energy bounds and the Gibbs free energy. The Cayley transform is employed to rigorously enforce the unit norm constraint, while <em>R</em>-functions are used to impose essential boundary conditions in the computation of magnetostatic fields. Our results highlight the potential of mesh-free Physics-Informed Neural Networks (PINNs) and Extreme Learning Machines (ELMs) when integrated with hard constraints, providing highly accurate approximations. These methods exhibit competitive performance compared to traditional numerical approaches, showing significant promise in computing magnetostatic fields and the application for energy minimization, such as the computation of hysteresis curves. This work opens the path for future directions of research on more complex geometries, such as grain structure models, and the application to large scale problem settings which are intractable with traditional numerical methods.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"315 ","pages":"Article 109719"},"PeriodicalIF":7.2,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144307930","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":"IsoME: Streamlining high-precision Eliashberg calculations","authors":"Eva Kogler ,&nbsp;Dominik Spath ,&nbsp;Roman Lucrezi ,&nbsp;Hitoshi Mori ,&nbsp;Zien Zhu ,&nbsp;Zhenglu Li ,&nbsp;Elena R. Margine ,&nbsp;Christoph Heil","doi":"10.1016/j.cpc.2025.109720","DOIUrl":"10.1016/j.cpc.2025.109720","url":null,"abstract":"&lt;div&gt;&lt;div&gt;This paper introduces the Julia package &lt;span&gt;IsoME&lt;/span&gt;, an easy-to-use yet accurate and robust computational tool designed to calculate superconducting properties. Multiple levels of approximation are supported, ranging from the basic McMillan-Allen-Dynes formula and its machine learning-enhanced variant to Eliashberg theory, including static Coulomb interactions derived from &lt;em&gt;GW&lt;/em&gt; calculations, offering a fully &lt;em&gt;ab initio&lt;/em&gt; approach to determine superconducting properties, such as the critical superconducting temperature (&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mtext&gt;c&lt;/mtext&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;) and the superconducting gap function (Δ). We validate &lt;span&gt;IsoME&lt;/span&gt; by benchmarking it against various materials, demonstrating its versatility and performance across different theoretical levels. The findings indicate that the previously held assumption that Eliashberg theory overestimates &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mtext&gt;c&lt;/mtext&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; is no longer valid when &lt;span&gt;&lt;math&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;μ&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mo&gt;⁎&lt;/mo&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;/math&gt;&lt;/span&gt; is appropriately adjusted to account for the finite Matsubara frequency cutoff. Furthermore, we conclude that the constant density of states (DOS) approximation remains accurate in most cases. By unifying multiple approximation schemes within a single framework, &lt;span&gt;IsoME&lt;/span&gt; combines first-principles precision with computational efficiency, enabling seamless integration into high-throughput workflows through its &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mtext&gt;c&lt;/mtext&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; search mode. This makes &lt;span&gt;IsoME&lt;/span&gt; a powerful and reliable tool for advancing superconductivity research.&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;IsoME&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/frwsdxf44s.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;Developer's repository link:&lt;/em&gt; &lt;span&gt;&lt;span&gt;https://github.com/cheil/IsoME.jl&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; MIT license&lt;/div&gt;&lt;div&gt;&lt;em&gt;Programming language:&lt;/em&gt; Julia 1.10 or higher&lt;/div&gt;&lt;div&gt;&lt;em&gt;Supplementary material:&lt;/em&gt; &lt;span&gt;&lt;span&gt;https://cheil.github.io/IsoME.jl&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;Nature of problem:&lt;/em&gt; The challenge addressed by &lt;span&gt;IsoME&lt;/span&gt; is the rigorous, first-principles calculation of superconducting properties, particularly the critical temperature (&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mtext&gt;c&lt;/mtext&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;) and detailed self-energy components. Predicting these properties involves solving the highly nonlinear, coupled Migdal-Eliashberg equations that capture the interplay between electron-phonon interactions and Coulomb repulsion. This is nontrivial because the equations require careful treatment of frequency-dependent interactions","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"315 ","pages":"Article 109720"},"PeriodicalIF":7.2,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297875","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":"Design and implementation of a novel approach for long-lived particle reconstruction in LHCb experiment at CERN","authors":"Piotr Kulczycki ,&nbsp;Grzegorz Gołaszewski ,&nbsp;Tomasz Szumlak ,&nbsp;Szymon Łukasik","doi":"10.1016/j.cpc.2025.109721","DOIUrl":"10.1016/j.cpc.2025.109721","url":null,"abstract":"<div><div>The subject of this paper is the improvement of the procedure to reconstruct long-lived particles in the LHCb (Large Hadron Collider beauty) experiment, one of the main experiments conducted by the European Organization for Nuclear Research CERN in Geneva. The goal of this procedure is to detect or, in other words, reconstruct long-lived particle tracks, based on observations obtained from measurement modules placed far apart, with a primary focus of eliminating erroneously created tracks, the so-called ghost-tracks. The investigated method is carried out by pairing proper trajectories and two autonomous filtering algorithms that are based on the nonparametric estimation of a distribution density as well as an autoencoder, a neural network designated for unsupervised learning. The values obtained from these two algorithms founded on complementary methodologies, mathematical statistics and computational intelligence, are combined with the t-norm. As a result, the number of ghost-tracks is reduced threefold when compared with the method primarily used. To evaluate the results of the designed procedure, a measure of the reconstruction quality, without the information whether a given track is real or only a ghost, is additionally created and effectively applied. Illustrative interpretations make it easier to potentially employ the method elaborated here to research tasks of similar conditions.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"315 ","pages":"Article 109721"},"PeriodicalIF":7.2,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144580152","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":"Predicting radiation distribution via neural operator trained on basis function-generated data","authors":"Ankang Hu ,&nbsp;Kaiwen Li ,&nbsp;Rui Qiu ,&nbsp;Junli Li","doi":"10.1016/j.cpc.2025.109710","DOIUrl":"10.1016/j.cpc.2025.109710","url":null,"abstract":"<div><div>The prediction of radiation-related quantity distributions through the solution of the transport equation is a computationally intensive process that necessitates substantial computational resources. Neural networks hold promise for rapid prediction; however, their utility is constrained by the scarcity of training datasets. In this study, we introduce a method using the Fourier Neural Operator (FNO) to predict radiation distributions, mitigating the challenges associated with limited datasets by generating data through basis functions. Our numerical experiments use the prediction of photon-deposited energy distributions in PET-CT examinations as an example. FNOs trained on datasets generated by basis functions show performance comparable to those trained on data derived from CT images. Specifically, the Mean Absolute Errors (MAEs) of FNOs trained on basis function-generated datasets are less than 65% of the MAEs of 3D U-Nets trained on CT images, which are commonly utilized for dose distribution prediction in the field of nuclear medicine. The inference time of FNOs is approximately 0.1 seconds, which is significantly quicker than the time taken for Monte Carlo simulations. Our findings underscore the generalization abilities of FNOs trained on basis function-generated data. This indicates a practical approach for the rapid prediction of radiation fields. Moreover, it suggests that the strategy of generating datasets using basis functions can effectively overcome the limitations caused by the scarcity of available datasets.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"315 ","pages":"Article 109710"},"PeriodicalIF":7.2,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271686","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":"An unconditionally stable adaptive finite difference scheme for the Allen–Cahn equation","authors":"Hyundong Kim ,&nbsp;Seokjun Ham ,&nbsp;Soobin Kwak ,&nbsp;Junseok Kim","doi":"10.1016/j.cpc.2025.109712","DOIUrl":"10.1016/j.cpc.2025.109712","url":null,"abstract":"<div><div>We propose an unconditionally stable adaptive finite difference scheme for the Allen–Cahn (AC) equation. The AC equation is a reaction-diffusion equation used to model phase separation in multi-component alloy systems. It describes the temporal evolution of the order parameter, which denotes different phases, and incorporates both diffusion and nonlinear reaction terms to capture the interfacial dynamics between phases. A fundamental aspect of the dynamics of the AC equation is motion by mean curvature, which implies that an initial interface shrinks as time progresses. Therefore, it is highly efficient to reduce the computational domain as the interface shrinks. We use an operator splitting technique with a finite difference method and a closed-form solution. We conduct computational tests to validate the effectiveness of the proposed approach. The computational tests demonstrate that the proposed algorithm is effective, reliable, and robust across various test cases.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"315 ","pages":"Article 109712"},"PeriodicalIF":7.2,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271687","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 identification of potential sources of bias in measurements of prompt-fission neutron spectra","authors":"Noah A.W. Walton ,&nbsp;Denise Neudecker ,&nbsp;Scott A. Vander Wiel ,&nbsp;Michael J. Grosskopf ,&nbsp;Keegan J. Kelly","doi":"10.1016/j.cpc.2025.109698","DOIUrl":"10.1016/j.cpc.2025.109698","url":null,"abstract":"<div><div>Unrecognized sources of uncertainty (USU) can bias the reported mean and/or covariance of experimental nuclear data. These biases, in turn, can propagate through evaluated nuclear data to application simulations or may poorly inform nuclear theory that is fitted to the experimental data. Such unknown sources of bias must be tied to the inherent physical constituents of the measurements such as the characteristics of a detector response or a background reduction technique. In this article, a sparse Bayesian learning model is used to support experts in their efforts to identify and characterize USU in experimental prompt fission neutron spectra (PFNS) for spontaneous fissioning of ²⁵²Cf by linking observed biases to features of the measurement system. Three different bias components were found. The first acts as a verification case for the algorithm as it identifies a bias coming from a well-known source related to the use of ⁶Li in the neutron detection system. The second two cases demonstrate how this method can benefit the evaluation of experimental nuclear data by identifying, quantifying, and relating unknown biases to potential causes.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"315 ","pages":"Article 109698"},"PeriodicalIF":7.2,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144263343","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}