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Articoli di riviste sul tema "Multiscale optimization"

Autore: Grafiati

Pubblicato: 19 ottobre 2024

Ultima modifica: 31 luglio 2025

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1

Li, Zihao, Shiqiang Li, and Zhihua Wang. "Multiscale Concurrent Topology Optimization and Mechanical Property Analysis of Sandwich Structures." Materials 17, no. 24 (2024): 6086. https://doi.org/10.3390/ma17246086.

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Based on the basic theoretical framework of the Bi-directional Evolutionary Structural Optimization method (BESO) and the Solid Isotropic Material with Penalization method (SIMP), this paper presents a multiscale topology optimization method for concurrently optimizing the sandwich structure at the macro level and the core layer at the micro level. The types of optimizations are divided into macro and micro concurrent topology optimization (MM), macro and micro gradient concurrent topology optimization (MMG), and macro and micro layered gradient concurrent topology optimization (MMLG). In orde

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2

Xu, Fan, Peter Wai Tat TSE, Yan-Jun Fang, and Jia-Qi Liang. "A fault diagnosis method combined with compound multiscale permutation entropy and particle swarm optimization–support vector machine for roller bearings diagnosis." Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology 233, no. 4 (2018): 615–27. http://dx.doi.org/10.1177/1350650118788929.

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A method based on compound multiscale permutation entropy, support vector machine, and particle swarm optimization for roller bearings fault diagnosis was presented in this study. Firstly, the roller bearings vibration signals under different conditions were decomposed into permutation entropy values by the multiscale permutation entropy and compound multiscale permutation entropy methods. The compound multiscale permutation entropy model combined the different graining sequence information under each scale factor. The average value of each scale factor was regarded as the final entropy value

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3

Murphy, Ryan, Chikwesiri Imediegwu, Robert Hewson, and Matthew Santer. "Multiscale structural optimization with concurrent coupling between scales." Structural and Multidisciplinary Optimization 63, no. 4 (2021): 1721–41. http://dx.doi.org/10.1007/s00158-020-02773-3.

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AbstractA robust three-dimensional multiscale structural optimization framework with concurrent coupling between scales is presented. Concurrent coupling ensures that only the microscale data required to evaluate the macroscale model during each iteration of optimization is collected and results in considerable computational savings. This represents the principal novelty of this framework and permits a previously intractable number of design variables to be used in the parametrization of the microscale geometry, which in turn enables accessibility to a greater range of extremal point propertie

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4

Han, Zhenyu, Shouzheng Sun, Zhongxi Shao, and Hongya Fu. "Multiscale Collaborative Optimization of Processing Parameters for Carbon Fiber/Epoxy Laminates Fabricated by High-Speed Automated Fiber Placement." Advances in Materials Science and Engineering 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/5480352.

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Processing optimization is an important means to inhibit manufacturing defects efficiently. However, processing optimization used by experiments or macroscopic theories in high-speed automated fiber placement (AFP) suffers from some restrictions, because multiscale effect of laying tows and their manufacturing defects could not be considered. In this paper, processing parameters, including compaction force, laying speed, and preheating temperature, are optimized by multiscale collaborative optimization in AFP process. Firstly, rational model between cracks and strain energy is revealed in orde

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5

Mjolsness, E., C. D. Garrett, and W. L. Miranker. "Multiscale optimization in neural nets." IEEE Transactions on Neural Networks 2, no. 2 (1991): 263–74. http://dx.doi.org/10.1109/72.80337.

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6

Sivapuram, Raghavendra, Peter D. Dunning, and H. Alicia Kim. "Simultaneous material and structural optimization by multiscale topology optimization." Structural and Multidisciplinary Optimization 54, no. 5 (2016): 1267–81. http://dx.doi.org/10.1007/s00158-016-1519-x.

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7

Murphy, Ryan, Dilaksan Thillaithevan, Matthew Santer, and Rob Hewson. "Multiscale Optimization of Non-Linear Structures." International Conference on Computational & Experimental Engineering and Sciences 32, no. 1 (2024): 1. https://doi.org/10.32604/icces.2024.011402.

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8

Fritzen, Felix, Liang Xia, Matthias Leuschner, and Piotr Breitkopf. "Topology optimization of multiscale elastoviscoplastic structures." International Journal for Numerical Methods in Engineering 106, no. 6 (2015): 430–53. http://dx.doi.org/10.1002/nme.5122.

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9

Boucard, P. A., S. Buytet, and P. A. Guidault. "A multiscale strategy for structural optimization." International Journal for Numerical Methods in Engineering 78, no. 1 (2009): 101–26. http://dx.doi.org/10.1002/nme.2484.

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10

Zhao, Ang, Pei Li, Yehui Cui, Zhendong Hu, and Vincent Beng Chye Tan. "Multiscale topology optimization with Direct FE2." Computer Methods in Applied Mechanics and Engineering 419 (February 2024): 116662. http://dx.doi.org/10.1016/j.cma.2023.116662.

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11

Oliveira, D. F., and A. C. Reynolds. "Hierarchical Multiscale Methods for Life-Cycle-Production Optimization: A Field Case Study." SPE Journal 20, no. 05 (2015): 896–907. http://dx.doi.org/10.2118/173273-pa.

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Summary We apply hierarchical multiscale techniques previously developed by the authors to estimate the well controls that maximize the net present value of the long-term production from a real field offshore Brazil. This field has been in production for several years, and it represents a significant share of the overall oil production for the country. The production-optimization step is preceded by a 10-year historical period, where seismic and production data were history matched by use of ensemble-based approaches. The well controls on a sequence of control steps (time intervals) are optimi

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12

Sokomba, Abdullahi Zubairu, E. M. Dogo, D. Maliki, and I. M. Abdullahi. "Impact of Gaussian Noise on the Optimization of Medical Image Registration." Proceedings of the Faculty of Science Conferences 1 (April 7, 2025): 133–38. https://doi.org/10.62050/fscp2024.519.

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Gaussian noise often poses a significant challenge to medical image registration, impacting the accuracy and reliability of alignment across varying imaging modalities. The research investigates the effect of Gaussian noise on medical image registration by comparing four optimization techniques: a direct approach, an optimization using fmincon, a multiscale approach, and a combined optimization strategy that integrates fmincon and the multiscale approach. The comparative analysis assesses each method's robustness against Gaussian noise, evaluating registration accuracy through three key simila

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13

Zhou, Fuming, Xiaoqiang Yang, Jinxing Shen, and Wuqiang Liu. "Fault Diagnosis of Hydraulic Pumps Using PSO-VMD and Refined Composite Multiscale Fluctuation Dispersion Entropy." Shock and Vibration 2020 (August 24, 2020): 1–13. http://dx.doi.org/10.1155/2020/8840676.

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Multiscale fluctuation dispersion entropy (MFDE) has been proposed to measure the dynamic features of complex signals recently. Compared with multiscale sample entropy (MSE) and multiscale fuzzy entropy (MFE), MFDE has higher calculation efficiency and better performance to extract fault features. However, when conducting multiscale analysis, as the scale factor increases, MFDE will become unstable. To solve this problem, refined composite multiscale fluctuation dispersion entropy (RCMFDE) is proposed and used to improve the stability of MFDE. And a new fault diagnosis method for hydraulic pum

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14

Pal, Saloni, Richard Clare, Andrew Lambert, and Stephen Weddell. "Multiscale optimization of the geometric wavefront sensor." Applied Optics 60, no. 25 (2021): 7536. http://dx.doi.org/10.1364/ao.423536.

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15

Kato, Junji, Daishun Yachi, Shinsuke Takase, Kenjiro Terada, and Takashi Kyoya. "1903 Material Design using Multiscale Topology Optimization." Proceedings of The Computational Mechanics Conference 2013.26 (2013): _1903–1_—_1903–2_. http://dx.doi.org/10.1299/jsmecmd.2013.26._1903-1_.

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16

Yourdkhani, Mostafa, Damiano Pasini, and Francois Barthelat. "Multiscale mechanics and optimization of gastropod shells." Journal of Bionic Engineering 8, no. 4 (2011): 357–68. http://dx.doi.org/10.1016/s1672-6529(11)60041-3.

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17

Wang, Yingjun, Hang Xu, and Damiano Pasini. "Multiscale isogeometric topology optimization for lattice materials." Computer Methods in Applied Mechanics and Engineering 316 (April 2017): 568–85. http://dx.doi.org/10.1016/j.cma.2016.08.015.

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18

XU, Zhao, Weihong ZHANG, Ying ZHOU, and Jihong ZHU. "Multiscale topology optimization using feature-driven method." Chinese Journal of Aeronautics 33, no. 2 (2020): 621–33. http://dx.doi.org/10.1016/j.cja.2019.07.009.

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19

Hu, Nan, and Jacob Fish. "Enhanced ant colony optimization for multiscale problems." Computational Mechanics 57, no. 3 (2016): 447–63. http://dx.doi.org/10.1007/s00466-015-1245-z.

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20

Chandrasekhar, Aaditya, Saketh Sridhara, and Krishnan Suresh. "Graded multiscale topology optimization using neural networks." Advances in Engineering Software 175 (January 2023): 103359. http://dx.doi.org/10.1016/j.advengsoft.2022.103359.

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21

Deng, Jiadong, Claus B. W. Pedersen, and Wei Chen. "Connected morphable components-based multiscale topology optimization." Frontiers of Mechanical Engineering 14, no. 2 (2019): 129–40. http://dx.doi.org/10.1007/s11465-019-0532-3.

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22

Christofides, Panagiotis D., and Antonios Armaou. "Control and optimization of multiscale process systems." Computers & Chemical Engineering 30, no. 10-12 (2006): 1670–86. http://dx.doi.org/10.1016/j.compchemeng.2006.05.025.

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23

Yang, Jianghong, Hailiang Su, Xinqing Li, and Yingjun Wang. "Fail-safe topology optimization for multiscale structures." Computers & Structures 284 (August 2023): 107069. http://dx.doi.org/10.1016/j.compstruc.2023.107069.

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24

Xiao, Mi, Wei Sha, Yan Zhang, Xiliang Liu, Peigen Li, and Liang Gao. "CMTO: Configurable-design-element multiscale topology optimization." Additive Manufacturing 69 (May 2023): 103545. http://dx.doi.org/10.1016/j.addma.2023.103545.

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25

Krogstad, S., V. L. L. Hauge, and A. F. F. Gulbransen. "Adjoint Multiscale Mixed Finite Elements." SPE Journal 16, no. 01 (2010): 162–71. http://dx.doi.org/10.2118/119112-pa.

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Summary We develop an adjoint model for a simulator consisting of a multiscale pressure solver and a saturation solver that works on flow-adapted grids. The multiscale method solves the pressure on a coarse grid that is close to uniform in index space and incorporates fine-grid effects through numerically computed basis functions. The transport solver works on a coarse grid adapted by a fine-grid velocity field obtained by the multiscale solver. Both the multiscale solver for pressure and the flow-based coarsening approach for transport have shown earlier the ability to produce accurate result

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26

Wang, Peng, Kun Cheng, Yan Huang, Bo Li, Xinggui Ye, and Xiuhong Chen. "Multiscale Quantum Harmonic Oscillator Algorithm for Multimodal Optimization." Computational Intelligence and Neuroscience 2018 (2018): 1–12. http://dx.doi.org/10.1155/2018/8430175.

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This paper presents a variant of multiscale quantum harmonic oscillator algorithm for multimodal optimization named MQHOA-MMO. MQHOA-MMO has only two main iterative processes: quantum harmonic oscillator process and multiscale process. In the two iterations, MQHOA-MMO only does one thing: sampling according to the wave function at different scales. A set of benchmark test functions including some challenging functions are used to test the performance of MQHOA-MMO. Experimental results demonstrate good performance of MQHOA-MMO in solving multimodal function optimization problems. For the 12 tes

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27

Li, Keyu, Chao Yang, Xiaozhe Wang, Zhiqiang Wan, and Chang Li. "Multiscale Aeroelastic Optimization Method for Wing Structure and Material." Aerospace 10, no. 10 (2023): 866. http://dx.doi.org/10.3390/aerospace10100866.

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Microstructured materials, characterized by their lower weight and multifunctionality, have great application prospects in the aerospace field. Optimization methods play a pivotal role in enhancing the design efficiency of both macrostructural and microstructural topology (MMT) for aircraft. This paper proposes a multiscale aeroelastic optimization method for wing structure and material considering realistic aerodynamic loads for large aspect ratio wings with significant aeroelastic effects. The aerodynamic forces are calculated by potential flow theory and the aeroelastic equilibrium equation

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28

Liu, Xiaoming, Kun Gao, Peng Chen, Lijun Yin, and Jing Yang. "Multiscale Simulation of Nanowear-Resistant Coatings." Materials 18, no. 14 (2025): 3334. https://doi.org/10.3390/ma18143334.

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Nanowear-resistant coatings are critical for extending the service life of mechanical components, yet their performance optimization remains challenging due to the complex interplay between atomic-scale defects and macroscopic wear behavior. While experimental characterization struggles to resolve transient interfacial phenomena, multiscale simulations, integrating ab initio calculations, molecular dynamics, and continuum mechanics, have emerged as a powerful tool to decode structure–property relationships. This review systematically compares mainstream computational methods and analyzes their

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29

Wang, Yani, Jinfang Dong, and Bo Wang. "Feature Matching Optimization of Multimedia Remote Sensing Images Based on Multiscale Edge Extraction." Computational Intelligence and Neuroscience 2022 (June 2, 2022): 1–7. http://dx.doi.org/10.1155/2022/1764507.

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In order to solve the problem of low efficiency of image feature matching in traditional remote sensing image database, this paper proposes the feature matching optimization of multimedia remote sensing images based on multiscale edge extraction, expounds the basic theory of multiscale edge, and then registers multimedia remote sensing images based on the selection of optimal control points. In this paper, 100 remote sensing images with a size of 3619 ∗ 825 with a resolution of 30 m are selected as experimental data. The computer is configured with 2.9 ghz CPU, 16 g memory, and i7 processor. T

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30

Glanzer, Martin, and Georg Ch Pflug. "Multiscale stochastic optimization: modeling aspects and scenario generation." Computational Optimization and Applications 75, no. 1 (2019): 1–34. http://dx.doi.org/10.1007/s10589-019-00135-4.

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Abstract Real-world multistage stochastic optimization problems are often characterized by the fact that the decision maker may take actions only at specific points in time, even if relevant data can be observed much more frequently. In such a case there are not only multiple decision stages present but also several observation periods between consecutive decisions, where profits/costs occur contingent on the stochastic evolution of some uncertainty factors. We refer to such multistage decision problems with encapsulated multiperiod random costs, as multiscale stochastic optimization problems.

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31

Iyiola, Olaseni Oladehinde. "Multiscale Modeling and Performance Optimization of Smart Materials in Adaptive Building Structural Systems." International Journal of Research Publication and Reviews 6, no. 3 (2025): 7478–92. https://doi.org/10.55248/gengpi.6.0325.12141.

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32

Yu, Chen, Qifu Wang, Chao Mei, and Zhaohui Xia. "Multiscale Isogeometric Topology Optimization with Unified Structural Skeleton." Computer Modeling in Engineering & Sciences 122, no. 3 (2020): 779–803. http://dx.doi.org/10.32604/cmes.2020.09363.

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33

Acar, Pınar, Veera Sundararaghavan, and Nicholas Fasanella. "Multiscale Optimization of Nanocomposites with Probabilistic Feature Descriptors." AIAA Journal 56, no. 7 (2018): 2936–41. http://dx.doi.org/10.2514/1.j056791.

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34

Yan, Minghan, Jian Zhou, Cong Luo, Tingfa Xu, and Xiaoxue Xing. "Multiscale Joint Optimization Strategy for Retinal Vascular Segmentation." Sensors 22, no. 3 (2022): 1258. http://dx.doi.org/10.3390/s22031258.

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The accurate segmentation of retinal vascular is of great significance for the diagnosis of diseases such as diabetes, hypertension, microaneurysms and arteriosclerosis. In order to segment more deep and small blood vessels and provide more information to doctors, a multi-scale joint optimization strategy for retinal vascular segmentation is presented in this paper. Firstly, the Multi-Scale Retinex (MSR) algorithm is used to improve the uneven illumination of fundus images. Then, the multi-scale Gaussian matched filtering method is used to enhance the contrast of the retinal images. Optimized

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35

Nafati, N. M., S. Antonczak, J. Topin, and J. Golebiowski. "Multiscale Convergence Optimization in Constrained Molecular Dynamics Simulations." International Journal of Energy 16 (March 9, 2022): 45–51. http://dx.doi.org/10.46300/91010.2022.16.7.

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The energy analysis is essential for studying chemical or biochemical reactions but also for characterizing interactions between two protagonists. Molecular Dynamics Simulations are well suited to sampling interaction structures but under minimum energy. To sample unstable or high energy structures, it is necessary to apply a bias-constraint in the simulation, in order to maintain the system in a stable energy state. In MD constrained simulations of ""Umbrella Sampling"" type, the phenomenon of ligand-receptor dissociation is divided into a series of windows (space sampling) in which the simul

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36

Abdulle, Assyr, Orane Jecker, and Alexander Shapeev. "An Optimization Based Coupling Method for Multiscale Problems." Multiscale Modeling & Simulation 14, no. 4 (2016): 1377–416. http://dx.doi.org/10.1137/15m105389x.

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37

Gratton, Serge, Annick Sartenaer, and Philippe L. Toint. "Recursive Trust-Region Methods for Multiscale Nonlinear Optimization." SIAM Journal on Optimization 19, no. 1 (2008): 414–44. http://dx.doi.org/10.1137/050623012.

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38

Guo, Yanding, Shanshan Cheng, and Lijie Chen. "Multiscale concurrent topology optimization of transient thermoelastic structures." Computers & Structures 306 (January 2025): 107594. http://dx.doi.org/10.1016/j.compstruc.2024.107594.

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39

Yang, Jianghong, and Yingjun Wang. "A Fail-Safe Topology Optimization for Multiscale Structures." International Conference on Computational & Experimental Engineering and Sciences 29, no. 2 (2024): 1. http://dx.doi.org/10.32604/icces.2024.011249.

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40

Luo, Lingai, and Daniel Tondeur. "Multiscale optimization of flow distribution by constructal approach." China Particuology 3, no. 6 (2005): 329–36. http://dx.doi.org/10.1016/s1672-2515(07)60211-5.

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41

Wang, Guannan, and Marek-Jerzy Pindera. "Elasticity-based microstructural optimization: An integrated multiscale framework." Materials & Design 132 (October 2017): 337–48. http://dx.doi.org/10.1016/j.matdes.2017.07.003.

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42

White, Daniel A., William J. Arrighi, Jun Kudo, and Seth E. Watts. "Multiscale topology optimization using neural network surrogate models." Computer Methods in Applied Mechanics and Engineering 346 (April 2019): 1118–35. http://dx.doi.org/10.1016/j.cma.2018.09.007.

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43

Pun, Chi Seng, and Hoi Ying Wong. "Robust investment–reinsurance optimization with multiscale stochastic volatility." Insurance: Mathematics and Economics 62 (May 2015): 245–56. http://dx.doi.org/10.1016/j.insmatheco.2015.03.030.

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44

Andreasen, C. S., and O. Sigmund. "Multiscale modeling and topology optimization of poroelastic actuators." Smart Materials and Structures 21, no. 6 (2012): 065005. http://dx.doi.org/10.1088/0964-1726/21/6/065005.

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Lucia, Angelo, Peter A. DiMaggio, and Praveen Depa. "Funneling Algorithms for Multiscale Optimization on Rugged Terrains." Industrial & Engineering Chemistry Research 43, no. 14 (2004): 3770–81. http://dx.doi.org/10.1021/ie030636+.

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Podsiadlo, Pawel, and Gwidon W. Stachowiak. "Directional Multiscale Analysis and Optimization for Surface Textures." Tribology Letters 49, no. 1 (2012): 179–91. http://dx.doi.org/10.1007/s11249-012-0054-1.

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Imediegwu, Chikwesiri, Ryan Murphy, Robert Hewson, and Matthew Santer. "Multiscale structural optimization towards three-dimensional printable structures." Structural and Multidisciplinary Optimization 60, no. 2 (2019): 513–25. http://dx.doi.org/10.1007/s00158-019-02220-y.

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Zhao, Ang, Vincent Beng Chye Tan, Pei Li, Kui Liu, and Zhendong Hu. "A Reconstruction Approach for Concurrent Multiscale Topology Optimization Based on Direct FE2 Method." Mathematics 11, no. 12 (2023): 2779. http://dx.doi.org/10.3390/math11122779.

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Abstract (sommario):

The rapid development of material science is increasing the demand for the multiscale design of materials. The concurrent multiscale topology optimization based on the Direct FE2 method can greatly improve computational efficiency, but it may lead to the checkerboard problem. In order to solve the checkerboard problem and reconstruct the results of the Direct FE2 model, this paper proposes a filtering-based reconstruction method. This solution is of great significance for the practical application of multiscale topology optimization, as it not only solves the checkerboard problem but also prov

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Summers, R., T. Abdulla, and J.-M. Schleich. "Progress with Multiscale Systems." Measurement and Control 44, no. 6 (2011): 180–85. http://dx.doi.org/10.1177/002029401104400605.

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Zhao, Peiyao, Ziming Cai, Lingling Chen, et al. "Ultra-high energy storage performance in lead-free multilayer ceramic capacitors via a multiscale optimization strategy." Energy & Environmental Science 13, no. 12 (2020): 4882–90. http://dx.doi.org/10.1039/d0ee03094e.

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