Journal of Computational Science最新文献

{"title":"Impact of inlet velocity waveform shape on hemodynamics","authors":"Justen R. Geddes,&nbsp;Timothy D. King,&nbsp;Cyrus Tanade,&nbsp;William Ladd,&nbsp;Nusrat Sadia Khan,&nbsp;Amanda Randles","doi":"10.1016/j.jocs.2025.102579","DOIUrl":"10.1016/j.jocs.2025.102579","url":null,"abstract":"<div><div>Monitoring disease development in arteries, which supply oxygen and nutrients to the body, is crucial and can be assessed using hemodynamic metrics. Hemodynamic metrics can be calculated via computational fluid dynamic simulation of patient-specific geometries. These simulations are known to be heavily influenced by boundary conditions, such as time-dependent inlet flow. However, the effects of inlet flow profiles have not previously been quantified or understood. Here we quantify the effects of modulating temporal arterial waveforms on hemodynamic metrics. Building on our previous work that identified the minimum number of points of interest needed to characterize a left coronary artery inlet waveform, here, we extend this approach to pulmonary and carotid artery waveforms, pinpointing critical points of interest on these waveforms. Using a systematic variation of these points, we quantify the effects on hemodynamic metrics such as wall shear stress, oscillatory shear index, and relative residence time. We simulate using 1D Navier–Stokes and 3D lattice Boltzmann simulation approaches conducted on high performance compute clusters. The results pinpoint parts of the waveform that are most susceptible to perturbations and measurement error. The impacts of this work include the construction of a method that can be applied to other fluid simulations with pulsatile inlet conditions and the ability to distinguish the vital parts of a pulsatile inlet condition for computational fluid dynamic simulations and clinical metrics. This work is an extension of work published at the International Conference on Computational Science (ICCS-2024), (Geddes et al., 2024).</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"87 ","pages":"Article 102579"},"PeriodicalIF":3.1,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bearing-distance flocking with zone-based interactions in constrained dynamic environments","authors":"Hossein B. Jond","doi":"10.1016/j.jocs.2025.102574","DOIUrl":"10.1016/j.jocs.2025.102574","url":null,"abstract":"<div><div>This paper presents a novel zone-based flocking control approach suitable for dynamic multi-agent systems (MAS). Inspired by Reynolds behavioral rules for <em>boids</em>, flocking behavioral rules with the zones of repulsion, conflict, attraction, and surveillance are introduced. For each agent, using only bearing and distance measurements, behavioral contribution vectors quantify the local separation, local and global flock velocity alignment, local cohesion, obstacle avoidance and boundary conditions, and strategic separation for avoiding alien agents. The control strategy uses the local perception-based behavioral contribution vectors to guide each agent’s motion. Additionally, the control strategy incorporates a directionally aware obstacle avoidance mechanism that prioritizes obstacles in the agent’s forward path. Simulation results validate the effectiveness of the model in creating flexible, adaptable, and scalable flocking behavior. Asymptotic stability and convergence to a stable flocking configuration for any initial conditions provided the interaction graph is a spanning tree are demonstrated. The flocking model’s reliance on locally sensed bearing and distance measurements ensures scalability and robustness, particularly in scenarios where communication is unreliable or resource-intensive. This makes it well-suited for real-world applications demanding seamless operation in highly dynamic and distributed environments.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"87 ","pages":"Article 102574"},"PeriodicalIF":3.1,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design of a high-performance tensor–matrix multiplication with BLAS","authors":"Cem Savaş Başsoy","doi":"10.1016/j.jocs.2025.102568","DOIUrl":"10.1016/j.jocs.2025.102568","url":null,"abstract":"<div><div>The tensor–matrix multiplication (TTM) is a basic tensor operation required by various tensor methods such as the HOSVD. This paper presents flexible high-performance algorithms that compute the tensor–matrix product according to the Loops-over-GEMM (LOG) approach. The proposed algorithms can process dense tensors with any linear tensor layout, arbitrary tensor order and dimensions all of which can be runtime variable. The paper discusses two slicing methods with orthogonal parallelization strategies and propose four algorithms that call BLAS with subtensors or tensor slices. It also provides a simple heuristic which selects one of the four proposed algorithms at runtime. All algorithms have been evaluated on a large set of tensors with various tensor shapes and linear tensor layouts. In case of large tensor slices, our best-performing algorithm achieves a median performance of 2.47 TFLOPS on an Intel Xeon Gold 5318Y and 2.93 TFLOPS an AMD EPYC 9354. Furthermore, it outperforms batched GEMM implementation of Intel MKL by a factor of 2.57 with large tensor slices. Our runtime tests show that our best-performing algorithm is, on average, at least 6.21% and up to 334.31% faster than frameworks implementing state-of-the-art approaches, including actively developed libraries such as Libtorch and Eigen. For the majority of tensor shapes, it is on par with TBLIS which uses optimized kernels for the TTM computation. Our algorithm performs better than all other competing implementations for the majority of real world tensors from the SDRBench, reaching a speedup of 2x or more for some tensor instances. This work is an extended version of ”Fast and Layout-Oblivious Tensor–Matrix Multiplication with BLAS” (Başsoy 2024).</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"87 ","pages":"Article 102568"},"PeriodicalIF":3.1,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of new CORDIC algorithms implemented on FPGA for the Givens Rotator","authors":"Pawel Poczekajlo ,&nbsp;Leonid Moroz ,&nbsp;Ewa Deelman ,&nbsp;Pawel Gepner","doi":"10.1016/j.jocs.2025.102567","DOIUrl":"10.1016/j.jocs.2025.102567","url":null,"abstract":"<div><div>This article is an extended version of our conference paper ”Modified CORDIC Algorithm for Givens Rotator” (Poczekajlo et al., 2024) published at the International Conference on Computational Science (ICCS-2024). The CORDIC algorithm is an iterative method of computing trigonometric functions and rotating vectors without using complex calculations. This paper presents two modified CORDIC algorithms for implementing a Givens rotator on FPGA, improving upon classic CORDIC methods. The first approach introduces a selective iteration scheme with an optimized scaling factor, while the second, not published in the original ICCS-2024 paper, leverages a scaling-free methodology for improved precision. Implemented on an Altera Cyclone V FPGA, these algorithms demonstrate a 50% accuracy improvement and a 15% reduction in latency compared to standard methods. These findings contribute to enhanced FPGA-based trigonometric computations, particularly benefiting real-time signal processing and numerical linear algebra applications.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"87 ","pages":"Article 102567"},"PeriodicalIF":3.1,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A numerical investigation of convection-dominated convection diffusion problems using characteristic stabilized mixed finite element method","authors":"Baowei Lai ,&nbsp;Lanxin Sun ,&nbsp;Wenhuan Yang ,&nbsp;Lixiang Yu ,&nbsp;Zelian Ni ,&nbsp;Zhifeng Weng","doi":"10.1016/j.jocs.2025.102578","DOIUrl":"10.1016/j.jocs.2025.102578","url":null,"abstract":"<div><div>In this paper, we propose a characteristic stabilized mixed finite element method based on the lower regularity of the flux for convection dominated convection diffusion problems. The method combines the characteristic method with a stabilized mixed finite element method that uses the lowest equal-order pair for the velocity and pressure. The stabilization term is based on two local Gauss integrations for the velocity. Moreover, we obtain that the approximation of the pressure <span><math><mi>u</mi></math></span> is optimal in the <span><math><msup><mrow><mi>L</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>-norm and <span><math><msup><mrow><mi>H</mi></mrow><mrow><mn>1</mn></mrow></msup></math></span>-norm, the approximation of the velocity <span><math><mi>p</mi></math></span> is suboptimal in the <span><math><msup><mrow><mi>L</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>-norm. Finally, numerical experiments in 2D and 3D are presented to verify the theoretical results.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"87 ","pages":"Article 102578"},"PeriodicalIF":3.1,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cutting voxel projector a new approach to construct 3D cone beam CT operator","authors":"Vojtěch Kulvait ,&nbsp;Julian Moosmann ,&nbsp;Georg Rose","doi":"10.1016/j.jocs.2025.102573","DOIUrl":"10.1016/j.jocs.2025.102573","url":null,"abstract":"<div><div>We introduce a novel class of projectors for 3D cone beam tomographic reconstruction. Analytical formulas are derived to compute the relationship between the volume of a voxel projected onto a detector pixel and its contribution to the line integral of attenuation recorded by that pixel. Based on these formulas, we construct a near-exact projector and backprojector, particularly suited for algebraic reconstruction techniques and hierarchical reconstruction approaches with nonuniform voxel grids. Unlike traditional projectors, which assume a uniform grid with fixed voxel sizes, our method enables local refinement of voxels, allowing for adaptive grid resolution and improved reconstruction quality in regions of interest. We have implemented this cutting voxel projector along with a relaxed, speed-optimized version and compared them to two established projectors: a ray-tracing projector based on Siddon’s algorithm and a TT footprint projector. Our results demonstrate that the cutting voxel projector achieves higher accuracy than the TT projector, especially for large cone beam angles. Furthermore, the relaxed version of the cutting voxel projector offers a significant speed advantage, while maintaining comparable accuracy. In contrast, Siddon’s algorithm, tuned to achieve the same accuracy, is considerably slower than the cutting voxel projector. All algorithms are implemented in a GPU optimized open-source framework for algebraic reconstruction.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"87 ","pages":"Article 102573"},"PeriodicalIF":3.1,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143687958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-informed neural networks for microflows: Rarefied gas dynamics in cylinder arrays","authors":"Jean-Michel Tucny ,&nbsp;Marco Lauricella ,&nbsp;Mihir Durve ,&nbsp;Gianmarco Guglielmo ,&nbsp;Andrea Montessori ,&nbsp;Sauro Succi","doi":"10.1016/j.jocs.2025.102575","DOIUrl":"10.1016/j.jocs.2025.102575","url":null,"abstract":"<div><div>Accurate prediction of rarefied gas dynamics is crucial for optimizing flows through microelectromechanical systems, air filtration devices, and shale gas extraction. Traditional methods, such as discrete velocity and direct simulation Monte Carlo (DSMC), demand intensive memory and computation, especially for microflows in non-convex domains. Recently, physics-informed neural networks (PINNs) emerged as a meshless and adaptable alternative for solving non-linear partial differential equations. We trained a PINN using a limited number of DSMC-generated rarefied gas microflows in the transition regime <span><math><mrow><mo>(</mo><mn>0.1</mn><mspace></mspace><mo>&lt;</mo><mi>Kn</mi><mo>&lt;</mo><mspace></mspace><mn>3</mn><mo>)</mo></mrow></math></span>, incorporating continuity and Cauchy momentum exchange equations in the loss function. The PINN achieved under 2 % error on these residuals and effectively filtered DSMC’s intrinsic statistical noise. Predictions remained strong for a tested flow field with <span><math><mrow><mi>Kn</mi><mo>=</mo><mn>0.7</mn></mrow></math></span>, and showed limited extrapolation performance on a flow field with <span><math><mrow><mi>Kn</mi><mo>=</mo><mspace></mspace><mn>5</mn></mrow></math></span> with a local overshoot of about 20 %, while maintaining physical consistency. Notably, each DSMC field required <span><math><mrow><mo>∼</mo><mn>20</mn></mrow></math></span> hours on 4 graphics processing units (GPU), while the PINN training took <span><math><mrow><mo>&lt;</mo><mn>2</mn></mrow></math></span> hours on one GPU, with evaluations under <span><math><mn>2</mn></math></span> seconds.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"87 ","pages":"Article 102575"},"PeriodicalIF":3.1,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"State, parameters and hidden dynamics estimation with the Deep Kalman Filter: Regularization strategies","authors":"Erik Chinellato,&nbsp;Fabio Marcuzzi","doi":"10.1016/j.jocs.2025.102569","DOIUrl":"10.1016/j.jocs.2025.102569","url":null,"abstract":"<div><div>In this paper we present in detail the various regularization strategies adopted for a novel scientific machine learning extension of the well known Kalman Filter (KF) that we call the Deep Kalman Filter (DKF), briefly presented in the conference paper (Chinellato and Marcuzzi 2024) . It is based on a recent scientific machine learning paradigm, called algorithm unfolding/unrolling, that allows to create a neural network from the algebraic structure dictated by an analytical method of scientific computing. We show the <em>interpretable consistency</em> of DKF with the classic KF when this is optimal, and its improvements against the KF with both linear and nonlinear models in general. Indeed, the DKF learns parameters of a quite general reference model, comprising: corrector gains, predictor model parameters and eventual unmodeled dynamics. This goes well beyond the ability of the KF and its known extensions.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"87 ","pages":"Article 102569"},"PeriodicalIF":3.1,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143687957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy dissipation preserving physics informed neural network for Allen–Cahn equations","authors":"Mustafa Kütük,&nbsp;Hamdullah Yücel","doi":"10.1016/j.jocs.2025.102577","DOIUrl":"10.1016/j.jocs.2025.102577","url":null,"abstract":"<div><div>This paper investigates a numerical solution of Allen–Cahn equation with constant and degenerate mobility, with polynomial and logarithmic energy functionals, with deterministic and random initial functions, and with advective term in one, two, and three spatial dimensions, based on the physics-informed neural network (PINN). To improve the learning capacity of the PINN, we incorporate the energy dissipation property of the Allen–Cahn equation as a penalty term into the loss function of the network. To facilitate the learning process of random initials, we employ a continuous analogue of the initial random condition by utilizing the Fourier series expansion. Adaptive methods from traditional numerical analysis are also integrated to enhance the effectiveness of the proposed PINN. Numerical results indicate a consistent decrease in the discrete energy, while also revealing phenomena such as phase separation and metastability.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"87 ","pages":"Article 102577"},"PeriodicalIF":3.1,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the effectiveness of ROUGE as unbiased metric in Extractive vs. Abstractive summarization techniques","authors":"Alessia Auriemma Citarella ,&nbsp;Marcello Barbella ,&nbsp;Madalina G. Ciobanu ,&nbsp;Fabiola De Marco ,&nbsp;Luigi Di Biasi ,&nbsp;Genoveffa Tortora","doi":"10.1016/j.jocs.2025.102571","DOIUrl":"10.1016/j.jocs.2025.102571","url":null,"abstract":"<div><div>Approaches to Automatic Text Summarization try to extract key information from one or more input texts and generate summaries whilst preserving content meaning. These strategies are separated into two groups, Extractive and Abstractive, which differ in their work. The extractive summarization extracts sentences from the document text directly, whereas the abstractive summarization creates a summary by interpreting the text and rewriting sentences, often with new words. It is important to assess and confirm how similar a summary is to the original text independently of the particular TS algorithm adopted. The literature proposes various metrics and scores for evaluating text summarization results, and ROUGE (Recall-Oriented Understudy of Gisting Evaluation) is the most used. In this study, our main objective is to evaluate how the ROUGE metric performs when applied to both Extractive and Abstractive summarization algorithms. We aim to understand its effectiveness and reliability as an independent and unbiased metric in assessing the quality of summaries generated by these different approaches. We conducted a first experiment to compare the metric efficiency (ROUGE-1, ROUGE-2 and ROUGE-L) for evaluating Abstractive (word2vec, doc2vec, and glove) <span><math><mrow><mi>v</mi><mi>e</mi><mi>r</mi><mi>s</mi><mi>u</mi><mi>s</mi></mrow></math></span> Extractive Text Summarization algorithms (textRank, lsa, luhn, lexRank), and a second one to compare the obtained score for two different summary approaches: a simple execution of a summarization algorithm <span><math><mrow><mi>v</mi><mi>e</mi><mi>r</mi><mi>s</mi><mi>u</mi><mi>s</mi></mrow></math></span> a multiple execution of different algorithms on the same text. Based on our study, evaluating the ROUGE metric for Abstractive and Extractive algorithms revealed that it reaches similar results for the Abstractive and Extractive algorithms. Moreover, our findings indicate that multiple executions, based on the running of two text summarization algorithms sequentially on the same text, generally outperform single executions of a single text summarization algorithm.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"87 ","pages":"Article 102571"},"PeriodicalIF":3.1,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}