Relevant bibliographies by topics / Multiscale optimization

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Multiscale optimization'

Author: Grafiati
Published: 19 October 2024
Last updated: 31 July 2025

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Journal articles on the topic "Multiscale optimization"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Multiscale optimization"

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Lalanne, Jean-Benoît. "Multiscale dissection of bacterial proteome optimization." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/130217.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, May, 2020<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 315-348).<br>The quantitative composition of proteomes results from biophysical and biochemical selective pressures acting under system-level resource allocation constraints. The nature and strength of these evolutionary driving forces remain obscure. Through the development of analytical tools and precision measurement platforms spanning biological scales, we found evidence of optimization in bacterial
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Fitriani. "Multiscale Dynamic Time and Space Warping." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45279.

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Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2008.<br>Includes bibliographical references (p. 149-151).<br>Dynamic Time and Space Warping (DTSW) is a technique used in video matching applications to find the optimal alignment between two videos. Because DTSW requires O(N4) time and space complexity, it is only suitable for short and coarse resolution videos. In this thesis, we introduce Multiscale DTSW: a modification of DTSW that has linear time and space complexity (O(N)) with good accuracy. The first step in Multiscale DTSW is to app
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Yourdkhani, Mostafa. "Multiscale modeling and optimization of seashell structure and material." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=66991.

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The vast majority of mollusks grow a hard shell for protection. Typical seashells are composed of two distinct layers, with an outer layer made of calcite (a hard but brittle material) and an inner layer made of a tough and ductile material called nacre. Nacre is a biocomposite material that consists of more than 95% of tablet-shape aragonite, CaCO3, and a soft organic material as matrix. Although the brittle ceramic aragonite composes a high volume fraction of nacre, its mechanical prop-erties are found to be surprisingly higher than those of its constituents. It has been su
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Umoh, Utibe Godwin. "Multiscale analysis of cohesive fluidization." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/28988.

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Fluidization of a granular assembly of solid particles is a process where particles are suspended in a fluid by the upward flow of fluid through the bed. This process is important in industry as it has a wide range of applications due to the high mixing and mass transfer rates present as a result of the rapid movement of particles which occurs in the bed. The dynamics of fluidization is heavily dependent on the particle scale physics and the forces acting at a particle level. For particles with sizes and densities less than 100μm and 103 kg/m3, the importance of interparticle forces such as co
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Sorrentino, Luigi. "Simulation and optimization of crowd dynamics using a multiscale model." Doctoral thesis, Universita degli studi di Salerno, 2012. http://hdl.handle.net/10556/318.

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2010 - 2011<br>In the last decades, the modeling of crowd motion and pedestrian .ow has attracted the attention of applied mathematicians, because of an increasing num- ber of applications, in engineering and social sciences, dealing with this or similar complex systems, for design and optimization purposes. The crowd has caused many disasters, in the stadiums during some major sporting events as the "Hillsborough disaster" occurred on 15 April 1989 at Hills- borough, a football stadium, in She¢ eld, England, resulting in the deaths of 96 people, and 766 being injured that remains the d
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Chen, Xing. "Flexoelectric Effect of Heterogeneous Structure and Its Multiscale Topology Optimization." Electronic Thesis or Diss., Université Gustave Eiffel, 2024. http://www.theses.fr/2024UEFL2023.

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Au cours des dernières décennies, la flexoélectricité a suscité un intérêt croissant pour convertir l'énergie vibratoire ambiante en énergie électrique. La flexoélectricité est un phénomène électromécanique qui associe des champs électriques à des gradients de déformation et induit inversement une déformation mécanique pour un gradient de champ électrique. Ce phénomène est inhérent à tous les matériaux diélectriques, et peut être significatif aux très petites échelles. L'universalité et le remarquable effet d'échelle de l'effet flexoélectrique sont prometteurs pour la conception de systèmes él
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Parno, Matthew David. "A multiscale framework for Bayesian inference in elliptic problems." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/65322.

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Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2011.<br>Page 118 blank. Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 112-117).<br>The Bayesian approach to inference problems provides a systematic way of updating prior knowledge with data. A likelihood function involving a forward model of the problem is used to incorporate data into a posterior distribution. The standard method of sampling this distribution is Markov chain Monte Carlo which can become inefficient in high dimensions, wasting many evaluat
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MEJIAS, TUNI JESUS ALBERTO. "Multiscale approach applied to fires in tunnels, Model optimization and development." Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2960751.

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Chen, Minghan. "Stochastic Modeling and Simulation of Multiscale Biochemical Systems." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/90898.

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Numerous challenges arise in modeling and simulation as biochemical networks are discovered with increasing complexities and unknown mechanisms. With the improvement in experimental techniques, biologists are able to quantify genes and proteins and their dynamics in a single cell, which calls for quantitative stochastic models for gene and protein networks at cellular levels that match well with the data and account for cellular noise. This dissertation studies a stochastic spatiotemporal model of the Caulobacter crescentus cell cycle. A two-dimensional model based on a Turing mechanism is in
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Ahamad, Intan Salwani. "Multiscale line search in interior point methods for nonlinear optimization and applications." Thesis, University of Cambridge, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.612762.

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Books on the topic "Multiscale optimization"

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Hager, William W., Shu-Jen Huang, Panos M. Pardalos, and Oleg A. Prokopyev, eds. Multiscale Optimization Methods and Applications. Springer US, 2006. http://dx.doi.org/10.1007/0-387-29550-x.

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Günther, Michael, ed. Coupled Multiscale Simulation and Optimization in Nanoelectronics. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46672-8.

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Christofides, Panagiotis D., Antonios Armaou, Yiming Lou, and Amit Varshney. Control and Optimization of Multiscale Process Systems. Birkhäuser Boston, 2009. http://dx.doi.org/10.1007/978-0-8176-4793-3.

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Lou. Control and Optimization of Multiscale Process Systems. Birkhäuser Boston, 2009.

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Multiscale Structural Topology Optimization. Elsevier, 2016. http://dx.doi.org/10.1016/c2015-0-01254-0.

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Xia, Liang. Multiscale Structural Topology Optimization. Elsevier, 2016.

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Xia, Liang. Multiscale Structural Topology Optimization. Elsevier, 2016.

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Cheng, Gengdong, and Jun Yan. Multiscale Optimization and Material Design. World Scientific Publishing Co Pte Ltd, 2020.

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Pardalos, P. M. Multiscale Optimization Methods and Applications. Springer, 2014.

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Pardalos, P. M. Multiscale Optimization Methods and Applications. Springer, 2006.

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Book chapters on the topic "Multiscale optimization"

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Lewis, Robert Michael, and Stephen G. Nash. "Practical Aspects of Multiscale Optimization Methods for VLSICAD." In Combinatorial Optimization. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4757-3748-6_7.

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Christofides, Panagiotis D., Antonios Amaou, Yiming Lou, and Amit Varsheny. "Optimization of Multiscale Process Systmes." In Control and Optimization of Multiscale Process Systems. Birkhäuser Boston, 2008. http://dx.doi.org/10.1007/978-0-8176-4793-3_6.

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Kuś, Wacław, and Tadeusz Burczyński. "Bioinspired Algorithms in Multiscale Optimization." In Advanced Structured Materials. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-05241-5_10.

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Wang, Yanfei, and Qinghua Ma. "Iterated Adaptive Regularization for the Operator Equations of the First Kind." In Multiscale Optimization Methods and Applications. Springer US, 2006. http://dx.doi.org/10.1007/0-387-29550-x_19.

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Christofides, Panagiotis D., Antonios Amaou, Yiming Lou, and Amit Varsheny. "Multiscale Process Modeling and Simulation." In Control and Optimization of Multiscale Process Systems. Birkhäuser Boston, 2008. http://dx.doi.org/10.1007/978-0-8176-4793-3_2.

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Binder, Thomas, Luise Blank, Wolfgang Dahmen, and Wolfgang Marquardt. "Multiscale Concepts for Moving Horizon Optimization." In Online Optimization of Large Scale Systems. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04331-8_19.

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Binder, Thomas, Luise Blank, Wolfgang Dahmen, and Wolfgang Marquardt. "Iterative Multiscale Methods for Process Monitoring." In Fast Solution of Discretized Optimization Problems. Birkhäuser Basel, 2001. http://dx.doi.org/10.1007/978-3-0348-8233-0_2.

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Ramm, Ekkehard, Andrea Erhart, Thomas Hettich, Ingrid Bruss, Frédéric Hilchenbach, and Junji Kato. "Damage Propagation in Composites – Multiscale Modeling and Optimization." In Multiscale Methods in Computational Mechanics. Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-9809-2_15.

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de Wit, Albert, and Fred van Keulen. "Framework for Multi-Level Optimization of Complex Systems." In Multiscale Methods in Computational Mechanics. Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-9809-2_18.

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Yuan, Xiaohui, Jing Peng, and Yasumasa Nishiura. "Particle Swarm Optimization with Multiscale Searching Method." In Computational Intelligence and Security. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11596448_99.

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Conference papers on the topic "Multiscale optimization"

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Würthwein, Thomas, Anke Bonse, Felix Neumann, et al. "Intraoperative stimulated Raman imaging of human brain tissue for therapy optimization." In Multiscale Imaging and Spectroscopy VI, edited by Alex J. Walsh, Darren M. Roblyer, and Paul J. Campagnola. SPIE, 2025. https://doi.org/10.1117/12.3042014.

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Dong, Junjie, Ran Liu, Xiaolin Zhang, and Jing Meng. "Cellular Image Segmentation Model Based on Multiscale and Attention Mechanisms." In 2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS). IEEE, 2024. http://dx.doi.org/10.1109/docs63458.2024.10704422.

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Zhou, Jingjing. "Multiscale grid-model-based optimization algorithm for tobacco logistics distribution routes." In International Conference on Smart Transportation and City Engineering (STCE 2024), edited by Zhengang Feng and Miroslava Mikusova. SPIE, 2025. https://doi.org/10.1117/12.3060821.

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Sun, Yongxin, Xiaojuan Chen, Xinghua Zhang, Liang Chang, Zhen Yue, and Xiaohui Cai. "Multiscale Parkinson's speech classification study based on crown porcupine optimization algorithm." In Fifth International Conference on Digital Signal and Computer Communications (DSCC 2025), edited by Gordana Jovanovic-Dolecek and Ke-Lin Du. SPIE, 2025. https://doi.org/10.1117/12.3071481.

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Liu, Bo feng, Haiyang Yu, Xiaojuan Hu, and Yanfeng Li. "Multiscale network combined with multiloss optimization for low-light image super-resolution reconstruction." In 5th International Conference on Computer Vision and Data Mining (ICCVDM 2024), edited by Xin Zhang and Minghao Yin. SPIE, 2024. http://dx.doi.org/10.1117/12.3048271.

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Kakodkar, Rahul, Betsie Montano Flores, Marco De Sousa, Yilun Lin, and Efstratios N. Pistikopoulos. "Towards Energy and Material Transition Integration � A Systematic Multi-scale Modeling and Optimization Framework." In Foundations of Computer-Aided Process Design. PSE Press, 2024. http://dx.doi.org/10.69997/sct.171988.

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The energy transition is driven both by the motivation to decarbonize as well as the decrease in cost of low carbon technology. Net-carbon neutrality over the lifetime of technology use can neither be quantitatively assessed nor realized without accounting for the flows of carbon comprehensively from cradle to grave. Sources of emission are disparate with contributions from resource procurement, process establishment and function, and material refining. The synergies between the constituent value chains are especially apparent in the mobility transition which involves (i) power generation, sto
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Jia, Bingshu, Xianliang Zhang, Guizhou Wu, et al. "The optimization of principal component analysis algorithm through multiscale local binary pattern feature extraction." In Second International Conference on Electronics, Electrical and Control System (EECS 2025), edited by Ljiljana Trajkovic and Zhe Chen. SPIE, 2025. https://doi.org/10.1117/12.3071547.

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Fang, Huimin, Han Qin, and Chenming Zhao. "Multiscale optimization scheduling method for comprehensive energy based on dual carbon targets and GSA algorithm." In International Conference on Energy Technology and Electrical Power (ETEP24), edited by Jiashen Teh and Gen Li. SPIE, 2025. https://doi.org/10.1117/12.3065106.

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Li, Yunjie, Pengfei Liu, Haitao Zhao, Haifeng Tang, Minxian Shen, and Lingyao Wang. "LW-MSTCNN: An Optimization Study on Integrating Multiscale Attention Mechanism with Deep Separable Convolutional Networks." In 2025 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2025. https://doi.org/10.1109/cec65147.2025.11043014.

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Dowling, Alexander W. "Artificial Intelligence and Machine Learning for Sustainable Molecular-to-Systems Engineering." In Foundations of Computer-Aided Process Design. PSE Press, 2024. http://dx.doi.org/10.69997/sct.114705.

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Sustainability encompasses many wicked problems involving complex interdependencies across social, natural, and engineered systems. We argue holistic multiscale modeling and decision-support frameworks are needed to address multifaceted interdisciplinary aspects of these wicked problems. This review highlights three emerging research areas for artificial intelligence (AI) and machine learning (ML) in molecular-to-systems engineering for sustainability: (1) molecular discovery and materials design, (2) automation and self-driving laboratories, (3) process and systems-of-systems optimization. Re
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Reports on the topic "Multiscale optimization"

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Bhattacharya, Kaushik. Multiscale Modeling and Process Optimization for Engineered Microstructural Complexity. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada490968.

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Haile, S. M., M. Ortiz, G. Ravichandran, et al. Multiscale Modeling and Process Optimization for Engineered Microstructural Complexity. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada572383.

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Becker, R., M. McElfresh, C. Lee, R. Balhorn, and D. White. Multiscale Modeling of Nano-scale Phenomena: Towards a Multiphysics Simulation Capability for Design and Optimization of Sensor Systems. Office of Scientific and Technical Information (OSTI), 2003. http://dx.doi.org/10.2172/15013766.

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Al-Jassim, Mowafak, and Steve Harvey. Addressing Critical Problems in Materials Science Through Multiscale and Multimode Characterization (Project 1); Characterization and Optimization of Novel Triple-Conducting Oxide Materials for Energy Applications (Project 2): Cooperative Research and Development Final Report, CRADA Number CRD-17-00711. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2318703.

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