Journal of Computational Science最新文献

{"title":"Dynamics in and dynamics of networks using DyNSimF","authors":"Maarten W.J. van den Ende ,&nbsp;Mathijs Maijer ,&nbsp;Mike H. Lees ,&nbsp;Han L.J. van der Maas","doi":"10.1016/j.jocs.2024.102376","DOIUrl":"10.1016/j.jocs.2024.102376","url":null,"abstract":"<div><p>Advances in formal theories, network science, and data collection technologies make complex-agent networks and adaptive networks increasingly powerful tools in the fields’ of complexity science and computational social science. We present DyNSimF; an open source package that facilitates the modelling of adaptive networks, capturing complex interacting dynamics <em>on</em> a network as well as dynamics <em>of</em> (the structure of) a network. Capable of complex agent-based simulations on a dynamic network, it is able to capture individual-level dynamics as well as dynamics of the network structure, and how these interact and evolve. By capturing the emergent behaviour resulting from the interactions of node states and network topology, we argue that DyNSimF will help modellers to gain a fundamentally better understanding of complex network systems. The package can handle both weighted and directional links, is computationally scalable and efficient, and includes a generic utility-based edge selection framework. DyNSimF provides a generic modelling framework for dynamics networks and includes visualisation methods and tools to aid in the analysis of models. It is designed to be extensible and aims to be easy to learn and work with, allowing non-experts to focus on model development, while being highly customisable and extensible to allow for complex custom models.</p></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"81 ","pages":"Article 102376"},"PeriodicalIF":3.1,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1877750324001698/pdfft?md5=bfde277a854940b4fe03df398c525d51&pid=1-s2.0-S1877750324001698-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141574178","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":"Analyzing dynamic patterns of information flow between bitcoin and economic uncertainty in light of public sentiments: A statistical behavior approach","authors":"Yalda Aryan ,&nbsp;Seyfollah Soleimani ,&nbsp;Abbas Shojaee","doi":"10.1016/j.jocs.2024.102374","DOIUrl":"https://doi.org/10.1016/j.jocs.2024.102374","url":null,"abstract":"<div><p>Modeling and analyzing interrelationships within the Bitcoin market, as a prominent cryptocurrency, leads to understanding hidden structures, effective management and informed decision-making. Regarding this matter, numerous studies have analyzed the time-varying spillover patterns in this ecosystem. Although spillover network analysis can elucidate the nature and strength of correlations, it may not be adept at handling the conditional interdependencies within intricate non-linear and dynamic essential behaviors of financial time series. This research tries to address the mentioned challenges by presenting a novel analytical model to investigate the dynamic communication patterns among Bitcoin, United States Economic Policy Uncertainty (US EPU) and public sentiments. Following this objective, rather than directly exploring the effect of original data series on each other, the approach decomposes them into sequences of meaningful statistical behaviors, at different lag-lead horizons. Subsequently, considering the significance of conditional dependencies, we extract and analyze the rules and patterns of information flow among the observed behaviors. The findings not only unveil a distinct flow pattern compared to the spillover network, but also offer valuable insights into dynamic interactions and dominant behaviors under various scenarios. One observation suggests that as the historical range of predictors increases in predicting future changes, their effectiveness or reliability decreases, while their number simultaneously increases. Moreover, the trend slope of Bitcoin functions as a notable behavior in propagating information, directly influencing both economic uncertainty and investor sentiment. The proposed model enhances the understanding of interaction between financial time series and provides useful perspectives for analysis and risk management.</p></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"81 ","pages":"Article 102374"},"PeriodicalIF":3.1,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141582681","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 computational study for simulating MHD duct flows at high Hartmann numbers using a stabilized finite element formulation with shock-capturing","authors":"Süleyman Cengizci ,&nbsp;Ömür Uğur","doi":"10.1016/j.jocs.2024.102381","DOIUrl":"10.1016/j.jocs.2024.102381","url":null,"abstract":"<div><p>The concern of this manuscript is stabilized finite element simulations of two-dimensional incompressible and viscous magnetohydrodynamic (MHD) duct flows governed by a coupled system of partial differential equations of the convection–diffusion type. In such flows, the high values of the Hartmann numbers (<span><math><mi>Ha</mi></math></span>) indicate that the convection process dominates the flow field. As is typical trouble encountered in convection-dominated flow simulations, classical discretization methods fail to function properly for MHD flows at high Hartmann numbers, yielding several numerical instabilities. In this study, the core computational tool we employ in order to overcome such challenges is the streamline-upwind/Petrov–Galerkin (SUPG) formulation. Beyond that, since the SUPG-stabilized formulation requires additional treatment where solutions experience rapid changes, we also enhance the SUPG-stabilized finite element formulation with a shock-capturing operator for achieving better solution profiles around sharp layers. The formulations are derived for both steady-state and time-dependent models. The proposed method, the so-called SUPG-YZ<span><math><mi>β</mi></math></span> formulation, techniques used, and in-house-developed finite element solvers are evaluated on a comprehensive set of numerical experiments. The test computations reveal that the SUPG-YZ<span><math><mi>β</mi></math></span> formulation yields quite good solution profiles near strong gradients without any significant numerical instabilities, even for quite challenging values of <span><math><mi>Ha</mi></math></span>, whereas the SUPG usually fails alone. Furthermore, by using only linear elements on relatively coarser meshes, the proposed formulation achieves this without the need for any adaptive mesh strategy, in contrast to most previously reported studies.</p></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"81 ","pages":"Article 102381"},"PeriodicalIF":3.1,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141736499","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":"Mean Absolute Directional Loss as a new loss function for machine learning problems in algorithmic investment strategies","authors":"Jakub Michańków ,&nbsp;Paweł Sakowski ,&nbsp;Robert Ślepaczuk","doi":"10.1016/j.jocs.2024.102375","DOIUrl":"https://doi.org/10.1016/j.jocs.2024.102375","url":null,"abstract":"<div><p>This paper investigates the issue of an adequate loss function in the optimization of machine learning models used in the forecasting of financial time series for the purpose of algorithmic investment strategies (AIS) construction. We propose the Mean Absolute Directional Loss (MADL) function, solving important problems of classical forecast error functions in extracting information from forecasts to create efficient buy/sell signals in algorithmic investment strategies. MADL places appropriate emphasis not only on the quality of the point forecast but also on its impact on the rate of achievement by the investment system based on it. The introduction and detailed description of the theoretical properties of this new MADL loss function are our main contributions to the literature. In the empirical part of the study, based on the data from two different asset classes (cryptocurrencies: Bitcoin and commodities: Crude Oil), we show that our new loss function enables us to select better hyperparameters for the LSTM model and obtain more efficient investment strategies, with regard to risk-adjusted return metrics on the out-of-sample data.</p></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"81 ","pages":"Article 102375"},"PeriodicalIF":3.1,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1877750324001686/pdfft?md5=9f17890a84f71a8db1da3a64a219374f&pid=1-s2.0-S1877750324001686-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141542691","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":"A succinct and approximate greedy algorithm for the Minimum Set Cover Problem","authors":"Jorge Delgado,&nbsp;Héctor Ferrada,&nbsp;Cristóbal A. Navarro","doi":"10.1016/j.jocs.2024.102378","DOIUrl":"https://doi.org/10.1016/j.jocs.2024.102378","url":null,"abstract":"<div><p>The Minimum Set Cover Problem (MSCP) is a combinatorial optimization problem belonging to the NP-Hard class in computer science. For this reason, there is no algorithm that in the worst case ensures finding an optimal solution in polynomial-time. For a given universe <span><math><mi>X</mi></math></span>, the popular greedy heuristic, called <span>Greedy-SetCover</span>, is the main theoretical contribution to obtain an approximate solution for the MSCP in polynomial-time, offering an optimal approximate ratio of <span><math><mrow><mo>(</mo><mo>ln</mo><mrow><mo>|</mo><mi>X</mi><mo>|</mo></mrow><mo>+</mo><mn>1</mn><mo>)</mo></mrow></math></span>. In this article, we propose an approximate algorithm for MSCP within a succinct representation of the input dataset, whose empirical performance improves <span>Greedy-SetCover</span> both in quality and execution time, while offering the same optimal approximation ratio for the problem. Our experiments show that the proposed algorithm is magnitudes of times faster than the aforementioned greedy one, obtaining on average a cardinality much closer to the optimal solution. Furthermore, because we work on a succinct representation that allows us to compute operations between sets using bitwise operators, we can process much larger datasets than state-of-the-art solutions. As a result, our proposal is also a suitable alternative for processing large datasets as required by the current Big Data era.</p></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"81 ","pages":"Article 102378"},"PeriodicalIF":3.1,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141582680","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":"Massively parallel Bayesian estimation with Sequential Monte Carlo sampling for simultaneous estimation of earthquake fault geometry and slip distribution","authors":"Kai Nakao ,&nbsp;Tsuyoshi Ichimura ,&nbsp;Kohei Fujita ,&nbsp;Takane Hori ,&nbsp;Tomokazu Kobayashi ,&nbsp;Hiroshi Munekane","doi":"10.1016/j.jocs.2024.102372","DOIUrl":"https://doi.org/10.1016/j.jocs.2024.102372","url":null,"abstract":"<div><p>In inverse analysis, Bayesian estimation is useful for understanding the reliability of the estimation result or prediction based on it because it can estimate not only optimal parameters but also their uncertainties. The estimation of an earthquake source fault based on observed crustal deformation is a typical inverse problem in the field of earthquake research. In this study, a method for simultaneous Bayesian estimation of earthquake fault plane geometry and spatially variable slip distribution on the plane has been developed. The developed method can be applied to stochastic models with arbitrary probability distribution settings, and it enables to incorporate appropriate constraints for slip distribution in the estimation process, which can lead to enhanced robustness and stability of estimation. Since this method is computationally more expensive than conventional methods, large-scale parallel computing was introduced to cope with the increased computational cost and a supercomputer was used for the analysis. To validate the proposed method, simultaneous Bayesian estimation of fault geometry and slip distribution with slip constraints was performed using the crustal deformation observed in the 2018 Hokkaido Eastern Iburi earthquake. Hierarchical parameterization and massively parallelized Bayesian inference used in this study have broad applicability not only in earthquake research but also in other scientific and engineering fields.</p></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"81 ","pages":"Article 102372"},"PeriodicalIF":3.1,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1877750324001650/pdfft?md5=8a72faa550ce1d73d785cc9ac78b5f0e&pid=1-s2.0-S1877750324001650-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141582678","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":"Fast convergence of SPH numerical solutions using robust algebraic multilevel","authors":"L.P. da Silva ,&nbsp;C.H. Marchi ,&nbsp;M. Meneguette ,&nbsp;R. Suero","doi":"10.1016/j.jocs.2024.102369","DOIUrl":"https://doi.org/10.1016/j.jocs.2024.102369","url":null,"abstract":"<div><p>In our study we solve 2D equations that model the mathematical phenomenon of steady state heat diffusion. The discretization of the equations is performed with the smoothed particle hydrodynamics (SPH) method and the resolution of the associated system of linear equations is determined with a modified solver that we call the Gauss–Seidel–Silva (G–S–S). The single level parallel G–S–S solver is compared to the algebraic multilevel (AML) with serial G–S–S smoother which has the ability to smooth the error of the numerical solutions and accelerate convergence due to its iterative formulation. The AML with serial G–S–S smoother is responsible for determining speed-ups of 4084 times for uniform and 5136 times for non-uniform particle discretization. We estimate a speed-up of 41082 times for the AML with parallel G–S–S smoother.</p></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"81 ","pages":"Article 102369"},"PeriodicalIF":3.1,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141542605","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":"Flee 3: Flexible agent-based simulation for forced migration","authors":"Maziar Ghorbani ,&nbsp;Diana Suleimenova ,&nbsp;Alireza Jahani ,&nbsp;Arindam Saha ,&nbsp;Yani Xue ,&nbsp;Kate Mintram ,&nbsp;Anastasia Anagnostou ,&nbsp;Auke Tas ,&nbsp;William Low ,&nbsp;Simon J.E. Taylor ,&nbsp;Derek Groen","doi":"10.1016/j.jocs.2024.102371","DOIUrl":"https://doi.org/10.1016/j.jocs.2024.102371","url":null,"abstract":"<div><p>Forced migration is a major humanitarian challenge today, with over 100 million people forcibly displaced due to conflicts, violence and other adverse events. The accurate forecasting of migration patterns helps humanitarian organisations to plan an effective humanitarian response in times of crisis, or to estimate the impact of possible conflict and/or intervention scenarios. While existing models are capable of providing such forecasts, they are strongly geared towards forecasting headline arrival numbers and lack the flexibility to explore migration patterns for specific groups, such as children or persons of a specific ethnicity or religion. Within this paper we present Flee 3, an agent-based simulation tool that aims to deliver migration forecasts in a more detailed, flexible and reconfigurable manner. The tool introduces adaptable rules for agent movement and creation, along with a more refined model that flexibly supports factors like food security, ethnicity, religion, gender and/or age. These improvements help broaden the applicability of the code, enabling us to begin building models for internal displacement and non-conflict-driven migration. We validate Flee 3 by applying it to ten historical conflicts in Asia and Africa and comparing our results with UNHCR refugee data. Our validation results show that the code achieves a validation error (averaged relative difference) of less than 0.6 in all cases, i.e. correctly forecasting over 70% of refugee arrivals, which is superior to its predecessor in all but one case. In addition, by exploiting the parallelised simulation code, we are able to simulate migration from a large scale conflict (Ukraine 2022) in less than an hour and with 80% parallel efficiency using 512 cores per run. To showcase the relevance of Flee to practitioners, we present two use cases: one involving an international migration research project and one involving an international NGO. Flee 3 is available at <span>https://github.com/djgroen/flee/releases/tag/v3.1</span><svg><path></path></svg> and documented on <span>https://flee.readthedocs.io</span><svg><path></path></svg>.</p></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"81 ","pages":"Article 102371"},"PeriodicalIF":3.1,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1877750324001649/pdfft?md5=3222e32ca7b5f79fc2d938f941e8bae9&pid=1-s2.0-S1877750324001649-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141542609","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":"Computation at the Cutting Edge of Science","authors":"","doi":"10.1016/j.jocs.2024.102379","DOIUrl":"10.1016/j.jocs.2024.102379","url":null,"abstract":"","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"81 ","pages":"Article 102379"},"PeriodicalIF":3.1,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141574276","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":"Machine learning approaches for assessing rechargeable battery state-of-charge in unmanned aircraft vehicle-eVTOL","authors":"M. Thien Phung ,&nbsp;Tri-Chan-Hung Nguyen ,&nbsp;M. Shaheer Akhtar ,&nbsp;O-Bong Yang","doi":"10.1016/j.jocs.2024.102380","DOIUrl":"https://doi.org/10.1016/j.jocs.2024.102380","url":null,"abstract":"<div><p>The long stability of electric vertical take-off and landing (eVTOL) aircraft is majorly associated with energy storage devices like batteries. Lithium-ion battery (LIB) is frequently used battery in most of eVTOL because they have high charge storage capacity, good health of battery and long-life cycles. To maintain the health of battery, the state-of-charge (SoC) and state-of-health (SoH) are the most important parameters. This study demonstrates the SoC evaluation of batteries in eVTOL aircrafts and then forecasts SoC of batteries using different machine learning (ML) approaches such as Support Vector Regression, Random Forest, Linear Regression. The experimental dataset was collected by an open portal at Carnegie Mellon University wherein over 15 million records including a hundred charge/discharge cycles, and several working conditions are available. SoC of batteries was first calculated by using collected dataset. Input parameters for SoC forecasting by ML models were prepared with different features such as voltage, current, charging/discharging energy and temperature. By feature selection analysis, E_Discharge and voltage were found to be the most effective features for SoC of battery. The experimental dataset was first split into 80 % of training and 20 % of testing and then applied for three ML models (Support Vector Regression, Random Forest, Linear Regression). As compared to other ML models, Random Forest presented the best performance having the lowest error values (<strong>RMSE ≈ 0.000985, R</strong>^{<strong>2</strong>} <strong>= 0.9996</strong>) due to non-linear relationship between every feature and SoC. The studies suggested that ML approach for battery’s SoC forecasting would provide promising methods to manage the health of battery for eVTOL aircraft.</p></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"81 ","pages":"Article 102380"},"PeriodicalIF":3.1,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141542604","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}