Journal of Computational Science最新文献_第7页

Quantitative assessment and dynamic characteristic measurement of regional resilience: From the perspective of post-earthquakes effects 地区复原力的定量评估和动态特征测量：从地震后影响的角度出发

IF 3.1 3区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Science

Pub Date : 2024-11-10 DOI: 10.1016/j.jocs.2024.102461

Suyue Han , Bin Liu , Jun Shu , Zuli He , Xinyu Xia , Ke Pan , Hourui Ren

Strong geological disasters have caused persistent losses in society, economy, and ecological environments. Given the unique geographical settings of the stricken areas, their resilience is prone to damage or even loss. Comprehensive risk assessment of natural disasters is the core content and important foundation for building regional resilience. Therefore, conducting dynamic characteristics analysis of resilience in mountainous disaster areas impacted by strong earthquake geological disasters is vital for ensuring the region's high-quality and sustainable development. This article takes the 51 stricken areas of Wenchuan earthquake as the research object. To this end, social, economic and ecological environmental data from 2008 to 2020 was hereby collected. Initially, a regional resilience assessment system based on "socio-economic-ecological environment" was established, considering the long-term and spatial heterogeneity of geological disasters. Secondly, the regional resilience assessment model was constructed using Spectral clustering-genetic algorithm-improved entropy weight method. Following that, the dynamic characteristics of regional resilience were quantitatively analyzed from two aspects, including change velocity state and change rate trend. Finally, based on the regional resilience characteristics, differentiated resilience enhancement strategies were proposed. Collectively, the results revealed that: (1) From a geological disaster standpoint, the risk in post-earthquake disaster areas exhibited a strikingly rapid decline, with the spatial distribution of geological disaster risk being notably higher in the central areas and diminishing towards the peripheries. (2) Overall, the regional resilience of the 51 stricken areas showed a "V-shaped" trend, with a significant upturn since 2012. (3) From the perspective of dynamic characteristics, more counties (cities) presented an upward trend. (4) The 51 stricken areas were hereby divided into the "benchmarking type", the "declination type", the "backward type", and the "potential type". In conclusion, the current study enhances the technical framework for evaluating regional resilience and provides technical support for the construction of resilient cities.

强烈的地质灾害给社会、经济和生态环境造成了持续的损失。由于灾区特殊的地理环境，其抗灾能力容易受到破坏甚至丧失。自然灾害综合风险评估是区域抗灾能力建设的核心内容和重要基础。因此，对受强震地质灾害影响的山区灾区进行抗灾能力动态特征分析，对于确保区域高质量、可持续发展至关重要。本文以汶川地震 51 个灾区为研究对象。为此，本文收集了 2008 年至 2020 年的社会、经济和生态环境数据。首先，考虑到地质灾害的长期性和空间异质性，建立了基于 "社会-经济-生态环境 "的区域抗灾能力评估体系。其次，利用光谱聚类-遗传算法-改进熵权法构建了区域抗灾能力评估模型。然后，从变化速度状态和变化速率趋势两个方面定量分析了区域抗灾能力的动态特征。最后，根据区域恢复力特征，提出了差异化的恢复力提升策略。研究结果表明(1) 从地质灾害角度看，震后灾区的地质灾害风险呈现出惊人的快速下降趋势，地质灾害风险的空间分布呈现出明显的中心区高、边缘区低的特点。(2）总体而言，51 个灾区的区域抗灾能力呈 "V "型趋势，自 2012 年以来显著回升。(3) 从动态特征来看，更多的县（市）呈上升趋势。(4）据此将 51 个贫困地区分为 "标杆型"、"衰退型"、"落后型 "和 "潜力型"。总之，本研究完善了区域韧性评价的技术框架，为韧性城市建设提供了技术支撑。

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引用次数: 0

A method for filling missing values in multivariate sequence bidirectional recurrent neural networks based on feature correlations 基于特征相关性的多元序列双向递归神经网络缺失值填充方法

IF 3.1 3区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Science

Pub Date : 2024-11-08 DOI: 10.1016/j.jocs.2024.102472

Xiaoying Pan , Hao Wang , Mingzhu Lei , Tong Ju , Lin Bai

Multivariate real-life time series data often contain missing values. These missing values often affect subsequent prediction tasks. Traditional imputation methods generally consider only some of the characteristics of multivariate time series data. This can easily lead to inaccurate filling results. In this paper, a feature correlation-based bidirectional recurrent network (BRNN-FR) is proposed to solve the problem of missing values in multivariate sequence data. First, this method involves the design of a bidirectional prediction network based on time intervals and the use of forward and reverse time series information between data points to obtain the characteristics of data changes with time to the greatest extent. Second, considering the correlation between features, a combined feature selection strategy based on the Pearson correlation coefficient and mutual information was proposed. A multiple regression model was established to predict between features. Finally, a bidirectional network ensemble filling algorithm based on the relationships between features is established to predict missing values. Comprehensive experiments on four public datasets show that the mean absolute error (MAE), root mean square error (RMSE) and maximum R2 value (R2_score) of the BRNN-FR algorithm in the direct imputation test are better than those of the other comparison methods in most cases. BRNN-FR also achieved a better area under the curve (AUC) in the indirect comparison experiment of two classifications of in-hospital death after filling the medical dataset. Using the AIR air quality dataset and the power transformer temperature dataset from the ETTH1 interpolation regression to predict the next 3 hours and 6 hours of average numerical results, most of the optimal regression results are obtained.

多变量真实时间序列数据通常包含缺失值。这些缺失值往往会影响后续的预测任务。传统的估算方法一般只考虑多元时间序列数据的部分特征。这很容易导致不准确的填补结果。本文提出了一种基于特征相关性的双向递归网络（BRNN-FR）来解决多变量序列数据中的缺失值问题。首先，该方法基于时间间隔设计双向预测网络，利用数据点之间的正向和反向时间序列信息，最大程度地获取数据随时间变化的特征。其次，考虑到特征之间的相关性，提出了基于皮尔逊相关系数和互信息的组合特征选择策略。建立多元回归模型来预测特征之间的关系。最后，建立了基于特征间关系的双向网络集合填充算法来预测缺失值。在四个公开数据集上的综合实验表明，BRNN-FR 算法在直接估算测试中的平均绝对误差（MAE）、均方根误差（RMSE）和最大 R2 值（R2_score）在大多数情况下都优于其他比较方法。BRNN-FR 在填充医疗数据集后的院内死亡两种分类的间接比较实验中也取得了较好的曲线下面积（AUC）。利用 AIR 空气质量数据集和 ETTH1 插值回归中的电力变压器温度数据集预测未来 3 小时和 6 小时的平均数值结果，获得了大部分最优回归结果。

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引用次数: 0

Applications of Text Mining techniques to extract meaningful information from gastroenterology medical reports 应用文本挖掘技术从消化内科医疗报告中提取有意义的信息

IF 3.1 3区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Science

Pub Date : 2024-11-05 DOI: 10.1016/j.jocs.2024.102458

Rosarina Vallelunga , Ileana Scarpino , Maria Chiara Martinis , Francesco Luzza , Chiara Zucco

Text mining techniques, particularly topic modeling, can be used for the automatic extraction of information from medical reports. The ability to autonomously analyze texts and identify topics within them can provide meaningful clinical insights that support physicians in diagnostic settings and enhance the characterization of intestinal diseases, leading to more efficient and automated systems.

This study evaluates the effectiveness of Latent Dirichlet Allocation (LDA) and BERTopic in modeling topics from colonoscopy reports related to Crohn’s Disease, Ulcerative Colitis, and Polyps. We compared these models in terms of their ability to identify clinically relevant topics, their influence on the performance of machine learning classifiers trained on the derived topic features, and their scalability.

Our analysis, based on average results across five iterations of train-test splits, showed that BERTopic generally outperformed LDA in clustering metrics, achieving Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), and Purity scores of 0.5637, 0.5953, and 0.8447, respectively, compared to LDA’s scores of 0.5349, 0.5254, and 0.8149. Additionally, classifiers trained on BERTopic-derived features exhibited improved predictive accuracy and F1-scores, with Logistic Regression reaching a mean accuracy of 0.8464 and a mean F1-score of 0.8507, compared to 0.8319 and 0.8351 for LDA-based features. Despite BERTopic’s overall superior performance, LDA demonstrated greater stability and interpretability, making it a viable option in scenarios where computational efficiency is a priority.

文本挖掘技术，尤其是主题建模，可用于从医疗报告中自动提取信息。本研究评估了潜在 Dirichlet 分配（LDA）和 BERTopic 对结肠镜检查报告中有关克罗恩病、溃疡性结肠炎和息肉的主题建模的有效性。我们比较了这些模型识别临床相关主题的能力、它们对根据衍生主题特征训练的机器学习分类器性能的影响以及它们的可扩展性。我们的分析基于训练-测试分裂五次迭代的平均结果，结果表明 BERTopic 在聚类指标上普遍优于 LDA，调整兰德指数 (ARI)、归一化互信息 (NMI) 和纯度得分分别为 0.5637、0.5953 和 0.8447，而 LDA 的得分分别为 0.5349、0.5254 和 0.8149。此外，基于 BERTopic 派生特征训练的分类器的预测准确率和 F1 分数也有所提高，逻辑回归的平均准确率为 0.8464，平均 F1 分数为 0.8507，而基于 LDA 特征的平均准确率和 F1 分数分别为 0.8319 和 0.8351。尽管 BERTopic 的整体性能更优越，但 LDA 表现出更高的稳定性和可解释性，使其在计算效率优先的情况下成为一种可行的选择。

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引用次数: 0

Hypoxia-driven hybrid phenotypes in Ewing sarcoma: Insights from computational epithelial-mesenchymal transition modeling 缺氧驱动的尤文肉瘤混合表型：计算上皮-间充质转化模型的启示

IF 3.1 3区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Science

Pub Date : 2024-10-29 DOI: 10.1016/j.jocs.2024.102464

Daner A. Silveira , Shantanu Gupta , André T. Brunetto , José Carlos Merino Mombach , Marialva Sinigaglia

Ewing sarcoma (ES) is an extremely aggressive pediatric tumor primarily propelled by the EWS::FLI1 fusion protein. This fusion protein plays a pivotal role in various biological processes within ES, including hypoxia and epithelial-mesenchymal transition (EMT). Hypoxia has been documented to trigger EMT, a process that can stabilize a hybrid cell state, enhancing metastatic potential and resistance to drugs. However, the precise mechanisms that sustain this hybrid phenotype during hypoxia in ES have remained enigmatic. Our study introduces a logical model for EMT in ES, underscoring the potential significance of the EWS::FLI1/miR-145 circuit in inducing hybrid states during hypoxia. Furthermore, our findings underscore the necessity for downregulating EWS::FLI1 to fully activate EMT under hypoxic conditions. This model aligns well with results derived from existing literature. These insights underscore the crucial role of EWS::FLI1 in inducing the hybrid state in ES during hypoxia.

尤文肉瘤（ES）是一种侵袭性极强的儿科肿瘤，主要由 EWS::FLI1 融合蛋白推动。这种融合蛋白在 ES 的各种生物过程中起着关键作用，包括缺氧和上皮-间质转化（EMT）。缺氧已被证实可引发 EMT，这一过程可稳定混合细胞状态，增强转移潜力和抗药性。然而，ES缺氧时维持这种混合表型的确切机制仍然是个谜。我们的研究引入了ES中EMT的逻辑模型，强调了EWS::FLI1/miR-145回路在缺氧时诱导混合状态的潜在意义。此外，我们的研究结果还强调，在缺氧条件下，必须下调 EWS::FLI1 才能完全激活 EMT。这一模型与现有文献的结果非常吻合。这些发现强调了 EWS::FLI1 在缺氧条件下诱导 ES 中的混合状态的关键作用。

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引用次数: 0

An information fusion approach based on weight correction and evidence theory 基于权重修正和证据理论的信息融合方法

IF 3.1 3区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Science

Pub Date : 2024-10-28 DOI: 10.1016/j.jocs.2024.102456

Xugang Xi , Yaqing Nie , Yu Zhou , Yun-Bo Zhao , Ting Wang , Yahong Chen , Lihua Li , Jian Yang

The combination rules of the Dempster-Shafer evidence theory can lead to illogical results for highly conflicting evidence from different information sources. We propose a general formula for conflict degree calculation from the perspective of modifying evidence sources as a weighted sum of conflict coefficients and Jousselme distance, and the new metric of focal element dispersion is defined by adaptively adjusting the ratio of these two metrics: if the focal element dispersion is too high, the impact of conflict coefficients is increased, and vice versa. We then define the concept of preference consistency and propose a formula for calculating this metric that redistributes the weights of individual pieces of evidence based on the preferences of all evidence. Finally, typical examples show that the proposed rules can manage conflicting evidence with better convergence and interference resistance.

对于来自不同信息源的高度冲突证据，Dempster-Shafer 证据理论的组合规则会导致不合逻辑的结果。我们从修改证据来源的角度出发，提出了冲突度计算的一般公式，即冲突系数和儒塞姆距离的加权和，并通过自适应地调整这两个指标的比例来定义新的焦点要素离散度指标：如果焦点要素离散度过高，冲突系数的影响就会增大，反之亦然。然后，我们定义了偏好一致性的概念，并提出了计算这一指标的公式，该公式可根据所有证据的偏好重新分配单个证据的权重。最后，典型的例子表明，所提出的规则能以更好的收敛性和抗干扰性管理冲突证据。

引用次数: 0

2D scale-3 fractional Euler wavelets optimization algorithm for fractional-order differential equations 分数阶微分方程的二维尺度-3 分数欧拉小波优化算法

IF 3.1 3区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Science

Pub Date : 2024-10-28 DOI: 10.1016/j.jocs.2024.102459

Fengying Zhou, Jiakun Zhang

A numerical scheme combining particle swarm optimization(PSO) optimization algorithm for solving fractional-order differential equations is developed by using 2D scale-3 fractional Euler wavelets, which are constructed via orthogonal Euler polynomials. The fractional integration formulas for scale-3 fractional Euler wavelets are derived under Riemann–Liouville fractional integral. The error estimation is given through the generalized fractional-order Taylor expansion and the convergence analysis of 2D scale-3 fractional Euler wavelets expansion is studied. According to the fractional integration formulas, together with the collocation method, a numerical technique is created by discretizing the fractional-order differential equation into a system of equations. Some linear and nonlinear problems are provided and PSO algorithm is applied to the numerical scheme. The numerical results are analyzed and compared with existing findings. This not only confirms the feasibility and effectuality of the proposed method but also demonstrates that PSO algorithm can enhance the numerical scheme’s performance.

利用通过正交欧拉多项式构造的二维标度-3分数欧拉小波，开发了一种结合粒子群优化（PSO）优化算法求解分数阶微分方程的数值方案。在黎曼-刘维尔分数积分下推导出了标度-3 分数欧拉小波的分数积分公式。通过广义分数阶泰勒展开给出了误差估计，并研究了二维标度-3 分数欧拉小波展开的收敛性分析。根据分数积分公式，结合配位法，通过将分数阶微分方程离散化为方程组，创建了一种数值技术。提供了一些线性和非线性问题，并将 PSO 算法应用于数值方案。对数值结果进行了分析，并与现有研究结果进行了比较。这不仅证实了所提方法的可行性和有效性，还证明了 PSO 算法可以提高数值方案的性能。

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引用次数: 0

BPMT: A hybrid model for secure and effective electronic medical record management system BPMT：安全有效的电子病历管理系统混合模式

IF 3.1 3区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Science

Pub Date : 2024-10-26 DOI: 10.1016/j.jocs.2024.102457

Mehar Nasreen, Sunil Kumar Singh

Designing an efficient distributed blockchain system to store and access rising electronic medical records (EMR) is still challenging. Although many techniques have been designed to utilize EMRs proficiently, they still suffer from accessing time, storage time, and throughput. In this work, we have proposed a Merkle and B+ tree-based hybrid model to improve the performance of the blockchain system. B+ tree has improved the system's performance because of its practical layout and balanced structure. At the same time, Merkle Tree has ensured the integrity and security of the information. The proposed (B+ Merkle tree) BPMT model has reduced the storage time of records, improved the system's throughput, and significantly reduced memory use. These enhancements are supported by a thorough complexity analysis and testing on several datasets, which validate the efficacy and scalability of the model. The outcomes validate the performance improvements of the model and lay the groundwork for further developments in blockchain-based EMR management.

设计一个高效的分布式区块链系统来存储和访问不断增加的电子病历（EMR）仍然具有挑战性。虽然已经设计了许多技术来熟练利用 EMR，但它们仍然在访问时间、存储时间和吞吐量方面存在问题。在这项工作中，我们提出了一种基于 Merkle 和 B+ 树的混合模型，以提高区块链系统的性能。B+ 树以其实用的布局和平衡的结构提高了系统的性能。同时，梅克尔树确保了信息的完整性和安全性。所提出的（B+ 默克尔树）BPMT 模型缩短了记录的存储时间，提高了系统的吞吐量，并大大减少了内存的使用。对这些改进进行了全面的复杂性分析，并在多个数据集上进行了测试，验证了该模型的有效性和可扩展性。这些成果验证了该模型的性能改进，并为基于区块链的医疗记录管理的进一步发展奠定了基础。

引用次数: 0

Enhanced heat transfer in ternary tangent hyperbolic nanofluids through non-darcy porous media 三元正切双曲纳米流体在非达西多孔介质中的强化传热

IF 3.1 3区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Science

Pub Date : 2024-10-24 DOI: 10.1016/j.jocs.2024.102462

Lim Yeou Jiann , Sharena Mohamad isa , Noraihan Afiqah Rawi , Sharidan Shafie , Ahmad Qushairi Mohamad , Dennis Ling Chaun Ching , Nur Azlina Mat Noor

The effectiveness of heat transfer fluids (HTFs) is pivotal in maximizing the efficiency and longevity of various devices, from small-scale applications to large industrial systems. A comprehensive understanding of the properties of innovative ternary heat transfer nanofluids (TNFs) is essential, particularly when utilized over a Darcy-Forchheimer porous medium. This study explores tangent hyperbolic thermal nanofluids (TNFs) made up of nanoparticles such as graphene, zirconium oxide and magnesium oxide and, suspended in an ethylene glycol base fluid, within a non-Darcy porous medium. Similarity variables are used to streamline the mathematical representation of fluid flow and heat transmission in TNFs. Then, semi-analytical solutions to the reduced governing equations are obtained using the homotopy analysis method. The influence of tri-nanoparticles, porosity, and the Forchheimer parameter on skin friction, fluid flow dynamics, heat transfer rates, and the Nusselt number is investigated. The Forchheimer parameter lowers the Nusselt number by 27.80 % for TNFs, 21.27 % for the hybrid nanofluid, and 21.08 % for the nanofluid. As a result, the temperature within TNFs is more evenly distributed. TNFs can transfer more heat by raising the medium’s porosity and the tri-nanoparticle volume fraction. These results unveil groundbreaking insights into enhancing the efficiency of heat transfer fluids. The introduction of a porous medium emerges as an alternate strategy to boost the performance of TNFs.

从小型应用到大型工业系统，导热液体（HTFs）的有效性对于最大限度地提高各种设备的效率和寿命至关重要。全面了解创新型三元传热纳米流体（TNFs）的特性至关重要，尤其是在达西-福克海默（Darcy-Forchheimer）多孔介质上使用时。本研究探讨了在非达西多孔介质中，由石墨烯、氧化锆和氧化镁等纳米颗粒组成的切线双曲传热纳米流体（TNFs），它们悬浮在乙二醇基液中。相似变量用于简化 TNF 中流体流动和热传递的数学表示。然后，利用同调分析方法获得了简化控制方程的半解析解。研究了三纳米颗粒、孔隙率和 Forchheimer 参数对表皮摩擦、流体流动动力学、传热率和努塞尔特数的影响。Forchheimer 参数使 TNFs 的努塞尔特数降低了 27.80%，使混合纳米流体的努塞尔特数降低了 21.27%，使纳米流体的努塞尔特数降低了 21.08%。因此，TNFs 内部的温度分布更加均匀。通过提高介质的孔隙率和三纳米粒子的体积分数，TNFs 可以传递更多的热量。这些结果揭示了提高传热流体效率的突破性见解。引入多孔介质是提高 TNF 性能的另一种策略。

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引用次数: 0

Modeling and simulation of Fuzzy-rule based WBAN using multi-level fuzzy colored petri-nets and reinforcement learning 利用多级模糊彩色 petri 网和强化学习对基于模糊规则的 WBAN 进行建模和仿真

IF 3.1 3区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Science

Pub Date : 2024-10-15 DOI: 10.1016/j.jocs.2024.102455

Negar Majma , Seyed Morteza Babamir

By developing IoTs (Internet of Things), a new concept is arising in WBANs (Wireless Body Area Networks), called IoBs (Internet of Bodies). WBANs are the safety-critical devices that use rule-based knowledge with fuzzy variables for monitoring patients. There are two issues with the knowledgebase: (1) appropriate knowledge representation and inference and (2) handling the situations for which there is no rule in the knowledgebase. In this paper, for the first issue we present MFCPN (Multi-level Fuzzy Colored Petri-nets) and for the second one we present a RL (Reinforcement Learning) mechanism for decision making based on the past device’s feedback to the environment. Through a few scenarios and two datasets, the proposed approach has been applied to pacemakers to show the approach's effectiveness. The comparison between decision making by our approach and two real datasets indicates the proposed approach has 79.3 % accuracy.

随着物联网（IoTs）的发展，WBANs（无线体域网）中出现了一个新概念，即 IoBs（人体互联网）。WBANs 是一种安全关键型设备，使用基于规则的知识和模糊变量来监控病人。知识库有两个问题：（1）适当的知识表示和推理；（2）处理知识库中没有规则的情况。在本文中，针对第一个问题，我们提出了 MFCPN（多层次模糊彩色 Petri 网）；针对第二个问题，我们提出了一种 RL（强化学习）机制，用于根据过去设备对环境的反馈做出决策。通过一些场景和两个数据集，我们将提出的方法应用于心脏起搏器，以展示该方法的有效性。我们的方法与两个真实数据集的决策比较表明，我们提出的方法具有 79.3% 的准确率。

引用次数: 0

Generative model-based framework for parameter estimation and uncertainty quantification applied to a compartmental model in epidemiology 基于生成模型的参数估计和不确定性量化框架应用于流行病学中的分区模型

IF 3.1 3区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Science

Pub Date : 2024-10-15 DOI: 10.1016/j.jocs.2024.102451

Vinicius L.S. Silva , Claire E. Heaney , Nenko Nenov , Christopher C. Pain

We propose a new method in which a generative network (GN) set within a reduced-order model (ROM) framework is used to solve inverse problems for partial differential equations (PDE). The aim is to match available measurements and estimate the corresponding uncertainties associated with the states and parameters of a numerical physical simulation. We train the GN using only unconditional simulations of the discretized PDE model. A GN is used here to exploit the ability of these methods to generate realistic outputs from complex probability distributions, such as the ones that represent the possible states and parameters of a physical problem. We compare the proposed method with the gold standard Markov chain Monte Carlo (MCMC). Additionally, we suggest the use of a novel type of time-stepping regularization to improve the representativeness of the physical solution, and we present a new way of evaluating the GN training, taking advantage of the real/generated sample structure. We apply the proposed approaches to a spatio-temporal compartmental model in epidemiology. The results show that the proposed GN-based ROM can efficiently quantify uncertainty and accurately match the measurements and the gold standard. This is achieved using only a limited number of unconditional simulations from the full-order numerical PDE model (40 simulations). The GN-based ROM operates 60 times faster than the gold standard (MCMC), while producing uncertainties that closely match those generated by the MCMC approach. The proposed method is a general framework for quantifying uncertainties in numerical physical simulations and is not restricted to the specific physics of this application.

我们提出了一种新方法，即在还原阶模型（ROM）框架内使用生成网络（GN）集来解决偏微分方程（PDE）的逆问题。其目的是匹配可用的测量数据，并估计与数值物理模拟的状态和参数相关的相应不确定性。我们仅使用离散 PDE 模型的无条件模拟来训练 GN。这里使用 GN 是为了利用这些方法从复杂概率分布（如代表物理问题可能状态和参数的概率分布）生成真实输出的能力。我们将提议的方法与黄金标准马尔科夫链蒙特卡罗（MCMC）进行了比较。此外，我们还建议使用一种新型的时间步进正则化来提高物理解决方案的代表性，并提出了一种利用真实/生成样本结构来评估 GN 训练的新方法。我们将提出的方法应用于流行病学中的时空分区模型。结果表明，所提出的基于 GN 的 ROM 可以有效量化不确定性，并准确匹配测量结果和金标准。只需对全阶数值 PDE 模型进行数量有限的无条件模拟（40 次模拟），就能实现这一目标。基于 GN 的 ROM 运行速度比黄金标准（MCMC）快 60 倍，同时产生的不确定性与 MCMC 方法产生的不确定性非常接近。所提出的方法是量化数值物理模拟不确定性的通用框架，并不局限于该应用的特定物理。

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引用次数: 0