DataCentric Engineering最新文献_第6页

Chaotic systems learning with hybrid echo state network/proper orthogonal decomposition based model 基于混合回波状态网络/适当正交分解模型的混沌系统学习

DataCentric Engineering Pub Date : 2021-10-13 DOI: 10.1017/dce.2021.17

Mathias Lesjak, N. Doan

{"title":"Chaotic systems learning with hybrid echo state network/proper orthogonal decomposition based model","authors":"Mathias Lesjak, N. Doan","doi":"10.1017/dce.2021.17","DOIUrl":"https://doi.org/10.1017/dce.2021.17","url":null,"abstract":"Abstract We explore the possibility of combining a knowledge-based reduced order model (ROM) with a reservoir computing approach to learn and predict the dynamics of chaotic systems. The ROM is based on proper orthogonal decomposition (POD) with Galerkin projection to capture the essential dynamics of the chaotic system while the reservoir computing approach used is based on echo state networks (ESNs). Two different hybrid approaches are explored: one where the ESN corrects the modal coefficients of the ROM (hybrid-ESN-A) and one where the ESN uses and corrects the ROM prediction in full state space (hybrid-ESN-B). These approaches are applied on two chaotic systems: the Charney–DeVore system and the Kuramoto–Sivashinsky equation and are compared to the ROM obtained using POD/Galerkin projection and to the data-only approach based uniquely on the ESN. The hybrid-ESN-B approach is seen to provide the best prediction accuracy, outperforming the other hybrid approach, the POD/Galerkin projection ROM, and the data-only ESN, especially when using ESNs with a small number of neurons. In addition, the influence of the accuracy of the ROM on the overall prediction accuracy of the hybrid-ESN-B is assessed rigorously by considering ROMs composed of different numbers of POD modes. Further analysis on how hybrid-ESN-B blends the prediction from the ROM and the ESN to predict the evolution of the system is also provided.","PeriodicalId":34169,"journal":{"name":"DataCentric Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44690113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Multi-resolution dynamic mode decomposition for damage detection in wind turbine gearboxes 风电齿轮箱损伤检测的多分辨率动态模态分解

DataCentric Engineering Pub Date : 2021-10-08 DOI: 10.1017/dce.2022.34

Paolo Climaco, J. Garcke, Rodrigo Iza-Teran

引用次数: 2

Performance and accuracy assessments of an incompressible fluid solver coupled with a deep convolutional neural network 与深度卷积神经网络耦合的不可压缩流体求解器的性能和精度评估

DataCentric Engineering Pub Date : 2021-09-20 DOI: 10.1017/dce.2022.2

Ekhi Ajuria Illarramendi, M. Bauerheim, B. Cuenot

{"title":"Performance and accuracy assessments of an incompressible fluid solver coupled with a deep convolutional neural network","authors":"Ekhi Ajuria Illarramendi, M. Bauerheim, B. Cuenot","doi":"10.1017/dce.2022.2","DOIUrl":"https://doi.org/10.1017/dce.2022.2","url":null,"abstract":"Abstract The resolution of the Poisson equation is usually one of the most computationally intensive steps for incompressible fluid solvers. Lately, DeepLearning, and especially convolutional neural networks (CNNs), has been introduced to solve this equation, leading to significant inference time reduction at the cost of a lack of guarantee on the accuracy of the solution.This drawback might lead to inaccuracies, potentially unstable simulations and prevent performing fair assessments of the CNN speedup for different network architectures. To circumvent this issue, a hybrid strategy is developed, which couples a CNN with a traditional iterative solver to ensure a user-defined accuracy level. The CNN hybrid method is tested on two flow cases: (a) the flow around a 2D cylinder and (b) the variable-density plumes with and without obstacles (both 2D and 3D), demonstrating remarkable generalization capabilities, ensuring both the accuracy and stability of the simulations. The error distribution of the predictions using several network architectures is further investigated in the plume test case. The introduced hybrid strategy allows a systematic evaluation of the CNN performance at the same accuracy level for various network architectures. In particular, the importance of incorporating multiple scales in the network architecture is demonstrated, since improving both the accuracy and the inference performance compared with feedforward CNN architectures. Thus, in addition to the pure networks’ performance evaluation, this study has also led to numerous guidelines and results on how to build neural networks and computational strategies to predict unsteady flows with both accuracy and stability requirements.","PeriodicalId":34169,"journal":{"name":"DataCentric Engineering","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43104613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Universal Digital Twin - A Dynamic Knowledge Graph 通用数字孪生——动态知识图谱

DataCentric Engineering Pub Date : 2021-09-06 DOI: 10.1017/dce.2021.10

J. Akroyd, S. Mosbach, A. Bhave, M. Kraft

{"title":"Universal Digital Twin - A Dynamic Knowledge Graph","authors":"J. Akroyd, S. Mosbach, A. Bhave, M. Kraft","doi":"10.1017/dce.2021.10","DOIUrl":"https://doi.org/10.1017/dce.2021.10","url":null,"abstract":"Abstract This paper introduces a dynamic knowledge-graph approach for digital twins and illustrates how this approach is by design naturally suited to realizing the vision of a Universal Digital Twin. The dynamic knowledge graph is implemented using technologies from the Semantic Web. It is composed of concepts and instances that are defined using ontologies, and of computational agents that operate on both the concepts and instances to update the dynamic knowledge graph. By construction, it is distributed, supports cross-domain interoperability, and ensures that data are connected, portable, discoverable, and queryable via a uniform interface. The knowledge graph includes the notions of a “base world” that describes the real world and that is maintained by agents that incorporate real-time data, and of “parallel worlds” that support the intelligent exploration of alternative designs without affecting the base world. Use cases are presented that demonstrate the ability of the dynamic knowledge graph to host geospatial and chemical data, control chemistry experiments, perform cross-domain simulations, and perform scenario analysis. The questions of how to make intelligent suggestions for alternative scenarios and how to ensure alignment between the scenarios considered by the knowledge graph and the goals of society are considered. Work to extend the dynamic knowledge graph to develop a digital twin of the UK to support the decarbonization of the energy system is discussed. Important directions for future research are highlighted.","PeriodicalId":34169,"journal":{"name":"DataCentric Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43575559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 37

Emulating computer experiments of transport infrastructure slope stability using Gaussian processes and Bayesian inference 利用高斯过程和贝叶斯推理模拟交通基础设施边坡稳定性的计算机实验

DataCentric Engineering Pub Date : 2021-09-06 DOI: 10.1017/dce.2021.14

A. Svalova, P. Helm, D. Prangle, M. Rouainia, S. Glendinning, D. Wilkinson

{"title":"Emulating computer experiments of transport infrastructure slope stability using Gaussian processes and Bayesian inference","authors":"A. Svalova, P. Helm, D. Prangle, M. Rouainia, S. Glendinning, D. Wilkinson","doi":"10.1017/dce.2021.14","DOIUrl":"https://doi.org/10.1017/dce.2021.14","url":null,"abstract":"Abstract We propose using fully Bayesian Gaussian process emulation (GPE) as a surrogate for expensive computer experiments of transport infrastructure cut slopes in high-plasticity clay soils that are associated with an increased risk of failure. Our deterioration experiments simulate the dissipation of excess pore water pressure and seasonal pore water pressure cycles to determine slope failure time. It is impractical to perform the number of computer simulations that would be sufficient to make slope stability predictions over a meaningful range of geometries and strength parameters. Therefore, a GPE is used as an interpolator over a set of optimally spaced simulator runs modeling the time to slope failure as a function of geometry, strength, and permeability. Bayesian inference and Markov chain Monte Carlo simulation are used to obtain posterior estimates of the GPE parameters. For the experiments that do not reach failure within model time of 184 years, the time to failure is stochastically imputed by the Bayesian model. The trained GPE has the potential to inform infrastructure slope design, management, and maintenance. The reduction in computational cost compared with the original simulator makes it a highly attractive tool which can be applied to the different spatio-temporal scales of transport networks.","PeriodicalId":34169,"journal":{"name":"DataCentric Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43230324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

On generative models as the basis for digital twins 论作为数字孪生基础的生成模型

DataCentric Engineering Pub Date : 2021-08-31 DOI: 10.1017/dce.2021.13

G. Tsialiamanis, D. Wagg, N. Dervilis, K. Worden

{"title":"On generative models as the basis for digital twins","authors":"G. Tsialiamanis, D. Wagg, N. Dervilis, K. Worden","doi":"10.1017/dce.2021.13","DOIUrl":"https://doi.org/10.1017/dce.2021.13","url":null,"abstract":"Abstract A framework is proposed for generative models as a basis for digital twins or mirrors of structures. The proposal is based on the premise that deterministic models cannot account for the uncertainty present in most structural modeling applications. Two different types of generative models are considered here. The first is a physics-based model based on the stochastic finite element (SFE) method, which is widely used when modeling structures that have material and loading uncertainties imposed. Such models can be calibrated according to data from the structure and would be expected to outperform any other model if the modeling accurately captures the true underlying physics of the structure. The potential use of SFE models as digital mirrors is illustrated via application to a linear structure with stochastic material properties. For situations where the physical formulation of such models does not suffice, a data-driven framework is proposed, using machine learning and conditional generative adversarial networks (cGANs). The latter algorithm is used to learn the distribution of the quantity of interest in a structure with material nonlinearities and uncertainties. For the examples considered in this work, the data-driven cGANs model outperforms the physics-based approach. Finally, an example is shown where the two methods are coupled such that a hybrid model approach is demonstrated.","PeriodicalId":34169,"journal":{"name":"DataCentric Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48633324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

A greedy data collection scheme for linear dynamical systems 线性动力系统的贪心数据采集方案

DataCentric Engineering Pub Date : 2021-07-28 DOI: 10.1017/dce.2022.16

Karim Cherifi, P. Goyal, P. Benner

引用次数: 5

Bayesian model uncertainty quantification for hyperelastic soft tissue models 超弹性软组织模型贝叶斯模型不确定性量化

DataCentric Engineering Pub Date : 2021-07-13 DOI: 10.1017/dce.2021.9

Milad Zeraatpisheh, S. Bordas, L. Beex