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Coevolutionary strategies at the collective level for improved generalism 提高通才水平的集体共同进化策略
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-02-06 DOI: 10.1017/dce.2023.1
P. Grudniewski, A. Sobey
Abstract In many complex practical optimization cases, the dominant characteristics of the problem are often not known prior. Therefore, there is a need to develop general solvers as it is not always possible to tailor a specialized approach to each application. The previously developed multilevel selection genetic algorithm (MLSGA) already shows good performance on a range of problems due to its diversity-first approach, which is rare among evolutionary algorithms. To increase the generality of its performance, this paper proposes utilization of multiple distinct evolutionary strategies simultaneously, similarly to algorithm selection, but with coevolutionary mechanisms between the subpopulations. This distinctive approach to coevolution provides less regular communication between subpopulations with competition between collectives rather than individuals. This encourages the collectives to act more independently creating a unique subregional search, leading to the development of coevolutionary MLSGA (cMLSGA). To test this methodology, nine genetic algorithms are selected to generate several variants of cMLSGA, which incorporates these approaches at the individual level. The mechanisms are tested on 100 different functions and benchmarked against the 9 state-of-the-art competitors to evaluate the generality of each approach. The results show that the diversity divergence in the principles of working of the selected coevolutionary approaches is more important than their individual performances. The proposed methodology has the most uniform performance on the divergent problem types, from across the tested state of the art, leading to an algorithm more likely to solve complex problems with limited knowledge about the search space, but is outperformed by more specialized solvers on simpler benchmarking studies.
摘要在许多复杂的实际优化案例中,问题的主要特征往往是事先不知道的。因此,需要开发通用求解器,因为不可能总是为每个应用程序定制专门的方法。先前开发的多级选择遗传算法(MLSGA)由于其多样性优先的方法,已经在一系列问题上表现出良好的性能,这在进化算法中是罕见的。为了提高其性能的通用性,本文提出同时使用多个不同的进化策略,类似于算法选择,但子种群之间具有共同进化机制。这种独特的共同进化方法提供了亚种群之间不太规律的交流,集体之间而不是个人之间的竞争。这鼓励集体更加独立地行动,创造一个独特的次区域搜索,导致共同进化MLSGA(cMLSGA)的发展。为了测试这种方法,选择了九种遗传算法来生成cMLSGA的几种变体,该变体在个体水平上结合了这些方法。这些机制在100个不同的功能上进行了测试,并与9个最先进的竞争对手进行了对比,以评估每种方法的通用性。结果表明,所选择的共同进化方法的工作原理的多样性差异比它们的个体表现更重要。在测试的现有技术中,所提出的方法在不同的问题类型上具有最一致的性能,这使得算法更有可能在搜索空间知识有限的情况下解决复杂问题,但在更简单的基准测试研究中,更专业的解算器的表现更出色。
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引用次数: 1
Multiphase segmentation of digital material images 数字材料图像的多相分割
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-02-02 DOI: 10.1017/dce.2022.40
R. Saxena, R. Day-Stirrat, Chaitanya Pradhan
Abstract Multiphase segmentation of pore-scale features and identification of mineralogy from digital images of materials is critical for many applications in the natural resources sector. However, the materials involved (rocks, catalyst pellets, and synthetic alloys) have complex and unpredictable composition. Algorithms that can be extended for multiphase segmentation of images of these materials are relatively few and very human-intensive. Challenges lie in designing algorithms that are context free, can function with less training data, and can handle the unpredictability of material composition. Semisupervised algorithms have shown success in classification in situations characterized by limited training data; they use unlabeled data in addition to labeled data to produce classification. The segmentation obtained can be more accurate than fully supervised learning approaches. This work proposes using a semisupervised clustering algorithm named Continuous Iterative Guided Spectral Class Rejection (CIGSCR) toward multiphase segmentation of digital scans of materials. CIGSCR harnesses spectral cohesion, splitting the intensity histogram of the input image down into clusters. This splitting provides the foundation for classification strategies that can be implemented as postprocessing steps to get the final segmentation. One classification strategy is presented. Micro-computed tomography scans of rocks are used to present the results. It is demonstrated that CIGSCR successfully enables distinguishing features up to the uniqueness of grayscale values, and extracting features present in full image stacks (3D), including features not presented in the training data. Results including instances of success and limitations are presented. Scalability to data sizes $ mathcal{O}left({10}^9right) $ voxels is briefly discussed.
孔隙尺度特征的多相分割和材料数字图像的矿物学识别对于自然资源领域的许多应用至关重要。然而,所涉及的材料(岩石、催化剂颗粒和合成合金)具有复杂和不可预测的成分。可以扩展用于这些材料图像的多相分割的算法相对较少,并且非常耗费人力。挑战在于设计与上下文无关的算法,可以使用较少的训练数据,并且可以处理材料组成的不可预测性。在训练数据有限的情况下,半监督算法在分类方面取得了成功;除了有标记的数据外,他们还使用未标记的数据来进行分类。所获得的分割比完全监督学习方法更准确。这项工作提出了一种半监督聚类算法,称为连续迭代制导光谱类拒绝(CIGSCR),用于材料数字扫描的多相分割。CIGSCR利用光谱内聚,将输入图像的强度直方图分解成簇。这种分割为分类策略提供了基础,分类策略可以作为获得最终分割的后处理步骤来实现。提出了一种分类策略。岩石的微型计算机断层扫描被用来呈现结果。实验证明,CIGSCR能够成功地区分特征,直至灰度值的唯一性,并提取出完整图像堆栈(3D)中存在的特征,包括训练数据中未出现的特征。结果包括成功的实例和局限性。简要讨论了数据大小$ mathcal{O}left({10}^9right) $体素的可伸缩性。
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引用次数: 1
Mean flow reconstruction of unsteady flows using physics-informed neural networks 基于物理信息的神经网络的非定常流平均流重建
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-01-25 DOI: 10.1017/dce.2022.37
Lukasz Sliwinski, Georgios Rigas
Abstract Data assimilation of flow measurements is an essential tool for extracting information in fluid dynamics problems. Recent works have shown that the physics-informed neural networks (PINNs) enable the reconstruction of unsteady fluid flows, governed by the Navier–Stokes equations, if the network is given enough flow measurements that are appropriately distributed in time and space. In many practical applications, however, experimental measurements involve only time-averaged quantities or their higher order statistics which are governed by the under-determined Reynolds-averaged Navier–Stokes (RANS) equations. In this study, we perform PINN-based reconstruction of time-averaged quantities of an unsteady flow from sparse velocity data. The applied technique leverages the time-averaged velocity data to infer unknown closure quantities (curl of unsteady RANS forcing), as well as to interpolate the fields from sparse measurements. Furthermore, the method’s capabilities are extended further to the assimilation of Reynolds stresses where PINNs successfully interpolate the data to complete the velocity as well as the stresses fields and gain insight into the pressure field of the investigated flow.
流量测量数据同化是流体动力学问题中提取信息的重要工具。最近的研究表明,如果给定足够的流量测量值,并在时间和空间上适当分布,那么物理信息神经网络(pinn)能够重建由Navier-Stokes方程控制的非定常流体流动。然而,在许多实际应用中,实验测量只涉及时间平均量或它们的高阶统计量,这些量由欠定的reynolds -average Navier-Stokes (RANS)方程控制。在本研究中,我们利用稀疏速度数据对非定常流的时间平均量进行了基于ppin的重建。应用技术利用时间平均速度数据来推断未知的闭合量(非定常RANS强迫旋度),以及从稀疏测量中插值场。此外,该方法的功能进一步扩展到雷诺兹应力的同化,其中pinn成功地插值数据以完成速度和应力场,并深入了解所研究流动的压力场。
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引用次数: 3
Democratizing electricity distribution network analysis 配电网络分析民主化
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-01-10 DOI: 10.1017/dce.2022.41
M. Neaimeh, M. Deakin, Ryan Jenkinson, Oscar Giles
Abstract The uptake of electric vehicles (EVs) and renewable energy technologies is changing the magnitude, variability, and direction of power flows in electricity networks. To ensure a successful transition to a net zero energy system, it will be necessary for a wide range of stakeholders to understand the impacts of these changing flows on networks. However, there is a gap between those with the data and capabilities to understand electricity networks, such as network operators, and those working on adjacent parts of the energy transition jigsaw, such as electricity suppliers and EV charging infrastructure operators. This paper describes the electric vehicle network analysis tool (EVENT), developed to help make network analysis accessible to a wider range of stakeholders in the energy ecosystem who might not have the bandwidth to curate and integrate disparate datasets and carry out electricity network simulations. EVENT analyses the potential impacts of low-carbon technologies on congestion in electricity networks, helping to inform the design of products and services. To demonstrate EVENT’s potential, we use an extensive smart meter dataset provided by an energy supplier to assess the impacts of electricity smart tariffs on networks. Results suggest both network operators and energy suppliers will have to work much more closely together to ensure that the flexibility of customers to support the energy system can be maximized, while respecting safety and security constraints within networks. EVENT’s modular and open-source approach enables integration of new methods and data, future-proofing the tool for long-term impact.
摘要电动汽车(EV)和可再生能源技术的普及正在改变电网中电力流动的规模、可变性和方向。为了确保成功过渡到净零能源系统,广泛的利益相关者有必要了解这些不断变化的流量对网络的影响。然而,那些有数据和能力了解电力网络的人,如网络运营商,与那些在能源转型拼图的相邻部分工作的人,例如电力供应商和电动汽车充电基础设施运营商之间存在差距。本文描述了电动汽车网络分析工具(EVENT),该工具旨在帮助能源生态系统中更广泛的利益相关者进行网络分析,这些利益相关者可能没有带宽来策划和集成不同的数据集并进行电网模拟。EVENT分析了低碳技术对电网拥堵的潜在影响,有助于为产品和服务的设计提供信息。为了展示EVENT的潜力,我们使用能源供应商提供的广泛的智能电表数据集来评估电力智能电价对网络的影响。结果表明,网络运营商和能源供应商必须更加紧密地合作,以确保客户支持能源系统的灵活性最大化,同时尊重网络内的安全和安保约束。EVENT的模块化和开源方法实现了新方法和数据的集成,为工具的长期影响提供了经得起未来考验的能力。
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引用次数: 1
Active-learning-based nonintrusive model order reduction 基于主动学习的非侵入式模型降阶
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-01-09 DOI: 10.1017/dce.2022.39
Qinyu Zhuang, Dirk Hartmann, H. Bungartz, Juan M Lorenzi
Abstract Model order reduction (MOR) can provide low-dimensional numerical models for fast simulation. Unlike intrusive methods, nonintrusive methods are attractive because they can be applied even without access to full order models (FOMs). Since nonintrusive MOR methods strongly rely on snapshots of the FOMs, constructing good snapshot sets becomes crucial. In this work, we propose a novel active-learning-based approach for use in conjunction with nonintrusive MOR methods. It is based on two crucial novelties. First, our approach uses joint space sampling to prepare a data pool of the training data. The training data are selected from the data pool using a greedy strategy supported by an error estimator based on Gaussian process regression. Second, we introduce a case-independent validation strategy based on probably approximately correct learning. While the methods proposed here can be applied to different MOR methods, we test them here with artificial neural networks and operator inference.
摘要模型降阶(MOR)可以为快速模拟提供低维数值模型。与侵入式方法不同,非侵入式方法很有吸引力,因为即使不访问全阶模型(FOM),它们也可以应用。由于非侵入性MOR方法强烈依赖于FOM的快照,因此构建良好的快照集变得至关重要。在这项工作中,我们提出了一种新的基于主动学习的方法,与非侵入式MOR方法结合使用。它基于两个关键的新颖性。首先,我们的方法使用联合空间采样来准备训练数据的数据池。使用由基于高斯过程回归的误差估计器支持的贪婪策略从数据池中选择训练数据。其次,我们引入了一种基于近似正确学习的案例独立验证策略。虽然这里提出的方法可以应用于不同的MOR方法,但我们在这里用人工神经网络和算子推理对它们进行了测试。
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引用次数: 2
Quantum computing for data-centric engineering and science 以数据为中心的工程和科学的量子计算
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-12-02 DOI: 10.1017/dce.2022.36
Steven Herbert
Abstract In this perspective, I give my answer to the question of how quantum computing will impact on data-intensive applications in engineering and science. I focus on quantum Monte Carlo integration as a likely source of (relatively) near-term quantum advantage, but also discuss some other ideas that have garnered widespread interest.
从这个角度来看,我给出了量子计算将如何影响工程和科学中的数据密集型应用的问题的答案。我将重点放在量子蒙特卡罗集成上,认为它可能是(相对)近期量子优势的来源,但也讨论了其他一些引起广泛兴趣的想法。
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引用次数: 3
Virtual sensing in an onshore wind turbine tower using a Gaussian process latent force model 基于高斯过程潜在力模型的陆上风力机塔架虚拟传感
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-11-28 DOI: 10.1017/dce.2022.38
Joaquin Bilbao, E. Lourens, A. Schulze, L. Ziegler
Abstract Wind turbine towers are subjected to highly varying internal loads, characterized by large uncertainty. The uncertainty stems from many factors, including what the actual wind fields experienced over time will be, modeling uncertainties given the various operational states of the turbine with and without controller interaction, the influence of aerodynamic damping, and so forth. To monitor the true experienced loading and assess the fatigue, strain sensors can be installed at fatigue-critical locations on the turbine structure. A more cost-effective and practical solution is to predict the strain response of the structure based only on a number of acceleration measurements. In this contribution, an approach is followed where the dynamic strains in an existing onshore wind turbine tower are predicted using a Gaussian process latent force model. By employing this model, both the applied dynamic loading and strain response are estimated based on the acceleration data. The predicted dynamic strains are validated using strain gauges installed near the bottom of the tower. Fatigue is subsequently assessed by comparing the damage equivalent loads calculated with the predicted as opposed to the measured strains. The results confirm the usefulness of the method for continuous tracking of fatigue life consumption in onshore wind turbine towers.
风力发电塔架承受高度变化的内部荷载,具有很大的不确定性。不确定性源于许多因素,包括随着时间的推移,实际风场将会是什么,在有和没有控制器交互的情况下,涡轮的各种运行状态下建模的不确定性,气动阻尼的影响等等。为了监测真实的经验载荷和评估疲劳,应变传感器可以安装在涡轮结构的疲劳临界位置。一种更经济、更实用的方法是仅根据若干加速度测量来预测结构的应变响应。在这一贡献中,采用了一种方法,其中使用高斯过程潜在力模型预测了现有陆上风力涡轮机塔架的动态应变。利用该模型,根据加速度数据估计了所施加的动载荷和应变响应。利用安装在塔底附近的应变片验证了预测的动态应变。随后,通过将计算的损伤等效载荷与预测的损伤等效载荷与测量的应变进行比较来评估疲劳。结果证实了该方法对陆上风力发电机组塔架疲劳寿命消耗连续跟踪的有效性。
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引用次数: 3
An approach for system analysis with model-based systems engineering and graph data engineering 基于模型的系统工程和图形数据工程的系统分析方法
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-11-14 DOI: 10.1017/dce.2022.33
F. Schummer, Maximillian Hyba
Abstract Model-based systems engineering (MBSE) aims at creating a model of a system under development, covering the complete system with a level of detail that allows to define and understand its behavior and enables to define any interface and work package based on the model. Once the model is established, further benefits can be reaped, such as the analysis of complex technical correlations within the system. Various insights can be gained by displaying the model as a formal graph and querying it. To enable such queries, a graph schema is necessary, which allows to transfer the model into a graph database. In the course of this paper, we discuss the design of a graph schema and MBSE modeling approach, enabling deep going system analysis and anomaly resolution in complex embedded systems with a focus on testing and anomaly resolution. The schema and modeling approach are designed to answer questions such as What happens if there is an electrical short in a component? Which other components are now offline and which data cannot be gathered anymore? If a component becomes unresponsive, which alternative routes can be established to obtain data processed by it. We build on the use case of qualification and operations of a small spacecraft. Structural elements of the MBSE model are transferred to a graph database where analyses are conducted on the system. The schema is implemented by means of an adapter for MagicDraw to Neo4J. A selection of complex analyses is shown in the example of the MOVE-II space mission.
摘要基于模型的系统工程(MBSE)旨在创建正在开发的系统的模型,以一定程度的细节覆盖整个系统,从而定义和理解其行为,并能够基于模型定义任何接口和工作包。一旦建立了模型,就可以获得进一步的好处,例如分析系统内复杂的技术相关性。通过将模型显示为形式图并对其进行查询,可以获得各种见解。要启用此类查询,需要一个图模式,它可以将模型转移到图数据库中。在本文的过程中,我们讨论了图模式和MBSE建模方法的设计,以实现复杂嵌入式系统中的深入系统分析和异常解决,重点是测试和异常解决。模式和建模方法旨在回答以下问题:如果组件中存在电气短路,会发生什么?哪些其他组件现在处于脱机状态,哪些数据无法再收集?如果一个组件变得没有反应,可以建立哪些替代路线来获得它处理的数据。我们以小型航天器的鉴定和操作用例为基础。MBSE模型的结构元素被转移到图形数据库中,在该数据库中对系统进行分析。该模式是通过MagicDraw到Neo4J的适配器实现的。MOVE-II太空任务的例子显示了一系列复杂的分析。
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引用次数: 0
Bayesian parameter inference for shallow subsurface modeling using field data and impacts on geothermal planning 利用现场数据进行浅层地下建模的贝叶斯参数推断及其对地热规划的影响
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-11-02 DOI: 10.1017/dce.2022.32
M. Kreitmair, N. Makasis, K. Menberg, A. Bidarmaghz, G. Farr, D. Boon, R. Choudhary
Abstract Understanding the subsurface is crucial in building a sustainable future, particularly for urban centers. Importantly, the thermal effects that anthropogenic infrastructure, such as buildings, tunnels, and ground heat exchangers, can have on this shared resource need to be well understood to avoid issues, such as overheating the ground, and to identify opportunities, such as extracting and utilizing excess heat. However, obtaining data for the subsurface can be costly, typically requiring the drilling of boreholes. Bayesian statistical methodologies can be used towards overcoming this, by inferring information about the ground by combining field data and numerical modeling, while quantifying associated uncertainties. This work utilizes data obtained in the city of Cardiff, UK, to evaluate the applicability of a Bayesian calibration (using GP surrogates) approach to measured data and associated challenges (previously not tested) and to obtain insights on the subsurface of the area. The importance of the data set size is analyzed, showing that more data are required in realistic (field data), compared to controlled conditions (numerically-generated data), highlighting the importance of identifying data points that contain the most information. Heterogeneity of the ground (i.e., input parameters), which can be particularly prominent in large-scale subsurface domains, is also investigated, showing that the calibration methodology can still yield reasonably accurate results under heterogeneous conditions. Finally, the impact of considering uncertainty in subsurface properties is demonstrated in an existing shallow geothermal system in the area, showing a higher than utilized ground capacity, and the potential for a larger scale system given sufficient demand.
摘要了解地下环境对于建设可持续的未来至关重要,尤其是对于城市中心而言。重要的是,需要充分了解建筑物、隧道和地面换热器等人为基础设施对这一共享资源的热影响,以避免出现地面过热等问题,并确定提取和利用多余热量等机会。然而,获取地下数据可能成本高昂,通常需要钻孔。贝叶斯统计方法可用于克服这一问题,通过结合现场数据和数值建模推断地面信息,同时量化相关的不确定性。这项工作利用在英国加的夫市获得的数据,评估贝叶斯校准(使用GP替代品)方法对测量数据和相关挑战(以前未测试)的适用性,并获得该地区地下的见解。分析了数据集大小的重要性,表明与受控条件(数字生成的数据)相比,现实情况下(现场数据)需要更多的数据,突出了识别包含最多信息的数据点的重要性。还研究了地面的不均匀性(即输入参数),这在大规模地下区域中可能特别突出,表明校准方法在不均匀条件下仍然可以产生相当准确的结果。最后,在该地区现有的浅层地热系统中证明了考虑地下性质不确定性的影响,显示出高于利用的地面容量,以及在需求充足的情况下建立更大规模系统的潜力。
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引用次数: 3
Design of a Ni-based superalloy for laser repair applications using probabilistic neural network identification 基于概率神经网络识别的镍基高温合金激光修复设计
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-10-10 DOI: 10.1017/dce.2022.31
Freddie Markanday, G. Conduit, B. Conduit, J. Pürstl, K. Christofidou, L. Chechik, G. Baxter, C. Heason, H. Stone
Abstract A neural network framework is used to design a new Ni-based superalloy that surpasses the performance of IN718 for laser-blown-powder directed-energy-deposition repair applications. The framework utilized a large database comprising physical and thermodynamic properties for different alloy compositions to learn both composition to property and also property to property relationships. The alloy composition space was based on IN718, although, W was additionally included and the limiting Al and Co content were allowed to increase compared standard IN718, thereby allowing the alloy to approach the composition of ATI 718Plus® (718Plus). The composition with the highest probability of satisfying target properties including phase stability, solidification strain, and tensile strength was identified. The alloy was fabricated, and the properties were experimentally investigated. The testing confirms that this alloy offers advantages for additive repair applications over standard IN718.
摘要使用神经网络框架设计了一种新型镍基高温合金,该合金的性能优于IN718,可用于激光吹制粉末定向能沉积修复应用。该框架利用包括不同合金成分的物理和热力学性质的大型数据库来学习成分与性质以及性质与性质的关系。合金成分空间以IN718为基础,但W被额外包括在内,并且与标准IN718相比,限制Al和Co含量被允许增加,从而使合金接近ATI 718Plus®(718Plus)的成分。确定了具有最高概率满足目标性能的成分,包括相稳定性、凝固应变和拉伸强度。制备了该合金,并对其性能进行了实验研究。测试证实,与标准IN718相比,该合金在添加剂修复应用方面具有优势。
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引用次数: 1
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