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Trajectory design via unsupervised probabilistic learning on optimal manifolds – Corrigendum 轨迹设计通过无监督概率学习在最优流形-勘误表
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-09-14 DOI: 10.1017/dce.2022.30
C. Safta, R. Ghanem, M. J. Grant, Michael J. Sparapany, H. Najm
“Real-time optimization of planetary reentry trajectories is a difficult task that requires simultaneous accounting for constraints related to flight dynamics, vehicle limitations during flight, variable initial and terminal conditions, and a high-dimensional parameter set for the models employed for these systems.” (p.1) Secondly, the phrase “hypersonic problems” in the following sentence on p.2 is corrected with the phrase “planetary reentry problems”:
“行星再入轨迹的实时优化是一项艰巨的任务,需要同时考虑与飞行动力学、飞行过程中的飞行器限制、可变的初始和最终条件以及这些系统所用模型的高维参数集有关的约束。”(p.1)其次,第2页下句中的“高超音速问题”改为“行星再入问题”:
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引用次数: 0
On improved fail-safe sensor distributions for a structural health monitoring system 结构健康监测系统故障安全传感器分布的改进
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-09-07 DOI: 10.1017/dce.2022.27
Tingna Wang, R. Barthorpe, D. Wagg, K. Worden
Abstract Sensor placement optimization (SPO) is usually applied during the structural health monitoring sensor system design process to collect effective data. However, the failure of a sensor may significantly affect the expected performance of the entire system. Therefore, it is necessary to study the optimal sensor placement considering the possibility of sensor failure. In this article, the research focusses on an SPO giving a fail-safe sensor distribution, whose sub-distributions still have good performance. The performance of the fail-safe sensor distribution with multiple sensors placed in the same position will also be studied. The adopted data sets include the mode shapes and corresponding labels of structural states from a series of tests on a glider wing. A genetic algorithm is used to search for sensor deployments, and the partial results are validated by an exhaustive search. Two types of optimization objectives are investigated, one for modal identification and the other for damage identification. The results show that the proposed fail-safe sensor optimization method is beneficial for balancing the system performance before and after sensor failure.
摘要传感器布局优化(SPO)通常应用于结构健康监测传感器系统的设计过程中,以收集有效的数据。然而,传感器的故障可能会显著影响整个系统的预期性能。因此,有必要研究考虑传感器故障可能性的最佳传感器布置。在本文中,研究的重点是给出一个故障安全传感器分布的SPO,其子分布仍然具有良好的性能。还将研究在同一位置放置多个传感器的故障安全传感器分布的性能。所采用的数据集包括滑翔机机翼一系列测试的模态形状和相应的结构状态标签。使用遗传算法搜索传感器部署,并通过穷举搜索验证部分结果。研究了两种类型的优化目标,一种用于模态识别,另一种用于损伤识别。结果表明,所提出的故障安全传感器优化方法有利于平衡传感器故障前后的系统性能。
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引用次数: 0
Trajectory design via unsupervised probabilistic learning on optimal manifolds 基于无监督概率学习的最优流形轨迹设计
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-08-23 DOI: 10.1017/dce.2022.26
C. Safta, R. Ghanem, M. J. Grant, Michael J. Sparapany, H. Najm
Abstract This article illustrates the use of unsupervised probabilistic learning techniques for the analysis of planetary reentry trajectories. A three-degree-of-freedom model was employed to generate optimal trajectories that comprise the training datasets. The algorithm first extracts the intrinsic structure in the data via a diffusion map approach. We find that data resides on manifolds of much lower dimensionality compared to the high-dimensional state space that describes each trajectory. Using the diffusion coordinates on the graph of training samples, the probabilistic framework subsequently augments the original data with samples that are statistically consistent with the original set. The augmented samples are then used to construct conditional statistics that are ultimately assembled in a path planning algorithm. In this framework, the controls are determined stage by stage during the flight to adapt to changing mission objectives in real-time.
摘要本文阐述了无监督概率学习技术在行星再入轨迹分析中的应用。采用三自由度模型来生成包括训练数据集的最优轨迹。该算法首先通过扩散图方法提取数据中的内在结构。我们发现,与描述每条轨迹的高维状态空间相比,数据驻留在维度低得多的流形上。使用训练样本图上的扩散坐标,概率框架随后用与原始集统计一致的样本来扩充原始数据。扩增的样本随后被用于构建条件统计,这些条件统计最终被组装在路径规划算法中。在这个框架中,控制是在飞行过程中逐步确定的,以实时适应不断变化的任务目标。
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引用次数: 1
A physics-based domain adaptation framework for modeling and forecasting building energy systems 建筑能源系统建模和预测的基于物理的领域自适应框架
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-08-19 DOI: 10.1017/dce.2023.8
Zack Xuereb Conti, R. Choudhary, L. Magri
Abstract State-of-the-art machine-learning-based models are a popular choice for modeling and forecasting energy behavior in buildings because given enough data, they are good at finding spatiotemporal patterns and structures even in scenarios where the complexity prohibits analytical descriptions. However, their architecture typically does not hold physical correspondence to mechanistic structures linked with governing physical phenomena. As a result, their ability to successfully generalize for unobserved timesteps depends on the representativeness of the dynamics underlying the observed system in the data, which is difficult to guarantee in real-world engineering problems such as control and energy management in digital twins. In response, we present a framework that combines lumped-parameter models in the form of linear time-invariant (LTI) state-space models (SSMs) with unsupervised reduced-order modeling in a subspace-based domain adaptation (SDA) approach, which is a type of transfer-learning (TL) technique. Traditionally, SDA is adopted for exploiting labeled data from one domain to predict in a different but related target domain for which labeled data is limited. We introduced a novel SDA approach where instead of labeled data, we leverage the geometric structure of the LTI SSM governed by well-known heat transfer ordinary differential equations to forecast for unobserved timesteps beyond available measurement data by geometrically aligning the physics-derived and data-derived embedded subspaces closer together. In this initial exploration, we evaluate the physics-based SDA framework on a demonstrative heat conduction scenario by varying the thermophysical properties of the source and target systems to demonstrate the transferability of mechanistic models from physics to observed measurement data.
最先进的基于机器学习的模型是建模和预测建筑物能源行为的流行选择,因为给定足够的数据,它们善于发现时空模式和结构,即使在复杂性禁止分析描述的情况下。然而,它们的结构通常与控制物理现象的机械结构不具有物理对应关系。因此,它们成功推广未观察到的时间步长的能力取决于数据中观察系统的动态代表性,这在现实世界的工程问题中很难保证,例如数字孪生中的控制和能量管理。作为回应,我们提出了一个框架,该框架将线性时不变(LTI)状态空间模型(ssm)形式的集总参数模型与基于子空间的域适应(SDA)方法中的无监督降阶建模相结合,这是一种迁移学习(TL)技术。传统上,SDA是利用一个领域的标记数据来预测一个不同但相关的目标领域,而目标领域的标记数据是有限的。我们引入了一种新的SDA方法,在这种方法中,我们利用LTI SSM的几何结构(由众所周知的传热常微分方程控制)来预测超出可用测量数据的未观察到的时间步,方法是将物理衍生和数据衍生的嵌入子空间更紧密地排列在一起。在这一初步探索中,我们通过改变源系统和目标系统的热物理性质来评估基于物理的SDA框架,以验证从物理到观测测量数据的机制模型的可转移性。
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引用次数: 0
A mapping method for anomaly detection in a localized population of structures 一种在局部结构群中进行异常检测的映射方法
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-08-09 DOI: 10.1017/dce.2022.25
Weijiang Lin, K. Worden, A. E. Maguire, E. Cross
Abstract Population-based structural health monitoring (PBSHM) provides a means of accounting for inter-turbine correlations when solving the problem of wind farm anomaly detection. Across a wind farm, where a group of structures (turbines) is placed in close vicinity to each other, the environmental conditions and, thus, structural behavior vary in a spatiotemporal manner. Spatiotemporal trends are often overlooked in the existing data-based wind farm anomaly detection methods, because most current methods are designed for individual structures, that is, detecting anomalous behavior of a turbine based on the past behavior of the same turbine. In contrast, the idea of PBSHM involves sharing data across a population of structures and capturing the interactions between structures. This paper proposes a population-based anomaly detection method, specifically for a localized population of structures, which accounts for the spatiotemporal correlations in structural behavior. A case study from an offshore wind farm is given to demonstrate the potential of the proposed method as a wind farm performance indicator. It is concluded that the method has the potential to indicate operational anomalies caused by a range of factors across a wind farm. The method may also be useful for other tasks such as wind power and turbine load modeling.
摘要基于群体的结构健康监测(PBSHM)在解决风电场异常检测问题时,提供了一种计算风机间相关性的方法。在风电场中,一组结构(涡轮机)相互靠近,环境条件和结构行为以时空方式变化。在现有的基于数据的风电场异常检测方法中,时空趋势往往被忽视,因为目前的大多数方法都是针对单个结构设计的,即基于同一涡轮机的过去行为来检测涡轮机的异常行为。相比之下,PBSHM的理念涉及在结构群体中共享数据,并捕捉结构之间的相互作用。本文提出了一种基于群体的异常检测方法,特别是针对结构的局部群体,该方法考虑了结构行为中的时空相关性。通过一个海上风电场的案例研究,证明了所提出的方法作为风电场性能指标的潜力。得出的结论是,该方法有可能指示整个风电场由一系列因素引起的运行异常。该方法也可用于其他任务,例如风力发电和涡轮机负载建模。
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引用次数: 0
Neural modal ordinary differential equations: Integrating physics-based modeling with neural ordinary differential equations for modeling high-dimensional monitored structures 神经模态常微分方程:将基于物理的建模与神经常微分方程集成在高维监测结构的建模中
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-07-16 DOI: 10.1017/dce.2022.35
Zhilu Lai, Wei Liu, Xudong Jian, Kiran Bacsa, Limin Sun, E. Chatzi
Abstract The dimension of models derived on the basis of data is commonly restricted by the number of observations, or in the context of monitored systems, sensing nodes. This is particularly true for structural systems, which are typically high-dimensional in nature. In the scope of physics-informed machine learning, this article proposes a framework—termed neural modal ordinary differential equations (Neural Modal ODEs)—to integrate physics-based modeling with deep learning for modeling the dynamics of monitored and high-dimensional engineered systems. In this initiating exploration, we restrict ourselves to linear or mildly nonlinear systems. We propose an architecture that couples a dynamic version of variational autoencoders with physics-informed neural ODEs (Pi-Neural ODEs). An encoder, as a part of the autoencoder, learns the mappings from the first few items of observational data to the initial values of the latent variables, which drive the learning of embedded dynamics via Pi-Neural ODEs, imposing a modal model structure on that latent space. The decoder of the proposed model adopts the eigenmodes derived from an eigenanalysis applied to the linearized portion of a physics-based model: a process implicitly carrying the spatial relationship between degrees-of-freedom (DOFs). The framework is validated on a numerical example, and an experimental dataset of a scaled cable-stayed bridge, where the learned hybrid model is shown to out perform a purely physics-based approach to modeling. We further show the functionality of the proposed scheme within the context of virtual sensing, that is, the recovery of generalized response quantities in unmeasured DOFs from spatially sparse data.
基于数据导出的模型的维度通常受到观测数量的限制,或者在被监测系统的背景下,感知节点。对于结构系统来说尤其如此,因为结构系统本质上是典型的高维结构。在物理信息机器学习的范围内,本文提出了一个框架-称为神经模态常微分方程(neural modal ode) -将基于物理的建模与深度学习相结合,用于对监测和高维工程系统的动态建模。在这个初步的探索中,我们将自己限制在线性或轻度非线性系统中。我们提出了一种将变分自编码器的动态版本与物理信息神经ode (Pi-Neural ode)耦合的架构。编码器作为自动编码器的一部分,学习从观测数据的前几项到潜在变量的初始值的映射,这通过Pi-Neural ode驱动嵌入式动态的学习,在潜在空间上施加模态模型结构。所提出的模型的解码器采用从应用于基于物理的模型的线性化部分的特征分析中导出的特征模式:一个隐含地携带自由度(dof)之间的空间关系的过程。该框架在一个数值示例和一个缩放斜拉桥的实验数据集上进行了验证,其中学习混合模型显示出优于纯粹基于物理的建模方法。我们进一步展示了所提出方案在虚拟传感环境中的功能,即从空间稀疏数据中恢复未测量dof中的广义响应量。
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引用次数: 3
Universal Digital Twin: Integration of national-scale energy systems and climate data 通用数字孪生:整合国家规模的能源系统和气候数据
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-06-13 DOI: 10.1017/dce.2022.22
Thomas R. Savage, J. Akroyd, S. Mosbach, Nenad B. Krdzavac, M. Hillman, M. Kraft
Abstract This article applies a knowledge graph-based approach to unify multiple heterogeneous domains inherent in climate and energy supply research. Existing approaches that rely on bespoke models with spreadsheet-type inputs are noninterpretable, static and make it difficult to combine existing domain specific models. The difficulties inherent to this approach become increasingly prevalent as energy supply models gain complexity while society pursues a net-zero future. In this work, we develop new ontologies to extend the World Avatar knowledge graph to represent gas grids, gas consumption statistics, and climate data. Using a combination of the new and existing ontologies we construct a Universal Digital Twin that integrates data describing the systems of interest and specifies respective links between domains. We represent the UK gas transmission system, and HadUK-Grid climate data set as linked data for the first time, formally associating the data with the statistical output areas used to report governmental administrative data throughout the UK. We demonstrate how computational agents contained within the World Avatar can operate on the knowledge graph, incorporating live feeds of data such as instantaneous gas flow rates, as well as parsing information into interpretable forms such as interactive visualizations. Through this approach, we enable a dynamic, interpretable, modular, and cross-domain representation of the UK that enables domain specific experts to contribute toward a national-scale digital twin.
摘要本文应用基于知识图的方法来统一气候和能源供应研究中固有的多个异质领域。依赖于具有电子表格类型输入的定制模型的现有方法是不可解释的、静态的,并且很难组合现有的特定领域模型。随着社会追求净零未来,能源供应模型变得越来越复杂,这种方法固有的困难变得越来越普遍。在这项工作中,我们开发了新的本体论来扩展世界化身知识图,以表示天然气网格、天然气消耗统计数据和气候数据。使用新本体和现有本体的组合,我们构建了一个通用数字孪生,它集成了描述感兴趣系统的数据,并指定了域之间的相应链接。我们首次将英国天然气输送系统和HadUK电网的气候数据集作为链接数据,将数据与用于报告英国各地政府行政数据的统计输出区域正式关联。我们展示了世界化身中包含的计算代理如何在知识图上运行,将即时气体流速等实时数据馈送以及将信息解析为交互式可视化等可解释形式。通过这种方法,我们实现了英国的动态、可解释、模块化和跨领域表示,使特定领域的专家能够为国家规模的数字孪生做出贡献。
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引用次数: 3
Structural health monitoring of 52-meter wind turbine blade: Detection of damage propagation during fatigue testing 52米风力涡轮机叶片的结构健康监测:疲劳试验期间损伤扩展的检测
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-06-07 DOI: 10.1017/dce.2022.20
M. A. Fremmelev, P. Ladpli, E. Orlowitz, L. Bernhammer, M. McGugan, K. Branner
Abstract This work is concerned with damage detection in a commercial 52-meter wind turbine blade during fatigue testing. Different artificial damages are introduced in the blade in the form of laminate cracks. The lengths of the damages are increased manually, and they all eventually propagate and develop into delaminations during fatigue loading. Strain gauges, acoustic emission sensors, distributed accelerometers, and an active vibration monitoring system are used to track different physical responses in healthy and damaged states of the blade. Based on the recorded data, opportunities and limitations of the different sensing systems for blade structural health monitoring are investigated.
本文研究了商用52米风力机叶片疲劳试验过程中的损伤检测问题。以层合裂纹的形式对叶片进行了不同的人为损伤。损伤的长度是人为增加的,在疲劳加载过程中,损伤最终都会扩展并发展成分层。应变计、声发射传感器、分布式加速度计和主动振动监测系统用于跟踪叶片在健康和受损状态下的不同物理响应。基于实测数据,分析了不同传感系统在叶片结构健康监测中的优势和局限性。
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引用次数: 5
Guidance for Materials 4.0 to interact with a digital twin 材料4.0与数字孪生交互指南
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-06-02 DOI: 10.1017/dce.2022.23
D. Cogswell, Chaitanya Paramatmuni, Lucia Scotti, James Moffat
Abstract The rapid development of new infrastructure programmes requires an accelerated deployment of new materials in new environments. Materials 4.0 is crucial to achieve these goals. The application of digital to the field of materials has been at the forefront of research for many years, but there does not exist a unified means to describe a framework for this area creating pockets of development. This is confounded by the broader expectations of a digital twin (DT) as the possible answer to all these problems. The issue being that there is no accepted definition of a component DT, and what information it should contain and how it can be implemented across the product lifecycle exist. Within this position paper, a clear distinction is made between the “manufacturing DT” and the “component DT”; the former being the starting boundary conditions of the latter. In order to achieve this, we also discuss the introduction of a digital thread as a key concept in passing data through manufacturing and into service. The stages of how to define a framework around the development of DTs from a materials perspective is given, which acknowledges the difference between creating new understanding within academia and the application of this knowledge on a per-component basis in industry. A number of challenges are identified to the broad application of a component DT; all lead to uncertainty in properties and locations, resolving these requires judgments to be made in the provision of safety-dependent materials property data.
摘要新基础设施计划的快速发展要求在新环境中加快部署新材料。材料4.0对于实现这些目标至关重要。多年来,数字在材料领域的应用一直处于研究的前沿,但目前还没有一种统一的方法来描述这一领域的框架,从而创造出一些发展空间。这与数字孪生(DT)作为所有这些问题的可能答案的更广泛期望相混淆。问题是,组件DT没有公认的定义,它应该包含什么信息,以及如何在整个产品生命周期中实现。在本立场文件中,明确区分了“制造DT”和“部件DT”;前者是后者的起始边界条件。为了实现这一点,我们还讨论了引入数字线程,将其作为通过制造和服务传递数据的关键概念。给出了如何从材料的角度定义DTs发展的框架的各个阶段,这承认了在学术界创造新的理解和在工业中基于每个组件应用这些知识之间的区别。DT组件的广泛应用面临许多挑战;所有这些都会导致特性和位置的不确定性,解决这些问题需要在提供安全相关材料特性数据时做出判断。
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引用次数: 5
Scalable algorithms for physics-informed neural and graph networks 基于物理的神经网络和图网络的可扩展算法
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-05-16 DOI: 10.1017/dce.2022.24
K. Shukla, Mengjia Xu, N. Trask, G. Karniadakis
Abstract Physics-informed machine learning (PIML) has emerged as a promising new approach for simulating complex physical and biological systems that are governed by complex multiscale processes for which some data are also available. In some instances, the objective is to discover part of the hidden physics from the available data, and PIML has been shown to be particularly effective for such problems for which conventional methods may fail. Unlike commercial machine learning where training of deep neural networks requires big data, in PIML big data are not available. Instead, we can train such networks from additional information obtained by employing the physical laws and evaluating them at random points in the space–time domain. Such PIML integrates multimodality and multifidelity data with mathematical models, and implements them using neural networks or graph networks. Here, we review some of the prevailing trends in embedding physics into machine learning, using physics-informed neural networks (PINNs) based primarily on feed-forward neural networks and automatic differentiation. For more complex systems or systems of systems and unstructured data, graph neural networks (GNNs) present some distinct advantages, and here we review how physics-informed learning can be accomplished with GNNs based on graph exterior calculus to construct differential operators; we refer to these architectures as physics-informed graph networks (PIGNs). We present representative examples for both forward and inverse problems and discuss what advances are needed to scale up PINNs, PIGNs and more broadly GNNs for large-scale engineering problems.
摘要物理知情机器学习(PIML)已成为一种很有前途的新方法,用于模拟由复杂多尺度过程控制的复杂物理和生物系统,其中也有一些数据可用。在某些情况下,目标是从可用数据中发现部分隐藏的物理现象,PIML已被证明对传统方法可能失败的此类问题特别有效。与商业机器学习不同,在商业机器学习中,深度神经网络的训练需要大数据,而在PIML中,大数据是不可用的。相反,我们可以利用物理定律获得的额外信息来训练这种网络,并在时空域中的随机点对其进行评估。这样的PIML将多模态和多理想数据与数学模型集成,并使用神经网络或图网络来实现它们。在这里,我们回顾了将物理嵌入机器学习的一些流行趋势,使用主要基于前馈神经网络和自动微分的物理知情神经网络(PINN)。对于更复杂的系统或系统中的系统和非结构化数据,图神经网络(GNN)具有一些明显的优势,在这里我们回顾了如何使用基于图外部演算的GNN来构造微分算子来实现物理知情学习;我们将这些架构称为物理知情图网络(PIGNs)。我们给出了正问题和反问题的代表性例子,并讨论了在大规模工程问题中扩大PINN、PIGNN和更广泛的GNN需要哪些进展。
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引用次数: 19
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DataCentric Engineering
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