Structural and Multidisciplinary Optimization最新文献

Topology-inclusive aerodynamic shape optimisation using a cellular automata parameterisation. 使用蜂窝自动机参数化技术进行拓扑包容性空气动力学形状优化。

IF 3.6 2区工程技术

Structural and Multidisciplinary Optimization Pub Date : 2025-01-01 Epub Date: 2025-01-30 DOI: 10.1007/s00158-024-03916-6

M J Wood, T C S Rendall, C B Allen, L J Kedward, N J Taylor, J Fincham, N E Leppard

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引用次数: 0

Topology optimization of multi-material active structures to reduce energy consumption and carbon footprint 拓扑优化多材料活性结构，降低能耗和碳足迹

IF 3.9 2区工程技术

Structural and Multidisciplinary Optimization Pub Date : 2024-01-01 DOI: 10.1007/s00158-023-03698-3

Yafeng Wang, Ole Sigmund

引用次数: 0

Active learning for adaptive surrogate model improvement in high-dimensional problems. 在高维问题中改进自适应代用模型的主动学习。

IF 3.6 2区工程技术

Structural and Multidisciplinary Optimization Pub Date : 2024-01-01 Epub Date: 2024-07-10 DOI: 10.1007/s00158-024-03816-9

Yulin Guo, Paromita Nath, Sankaran Mahadevan, Paul Witherell

{"title":"Active learning for adaptive surrogate model improvement in high-dimensional problems.","authors":"Yulin Guo, Paromita Nath, Sankaran Mahadevan, Paul Witherell","doi":"10.1007/s00158-024-03816-9","DOIUrl":"10.1007/s00158-024-03816-9","url":null,"abstract":"This paper investigates a novel approach to efficiently construct and improve surrogate models in problems with high-dimensional input and output. In this approach, the principal components and corresponding features of the high-dimensional output are first identified. For each feature, the active subspace technique is used to identify a corresponding low-dimensional subspace of the input domain; then a surrogate model is built for each feature in its corresponding active subspace. A low-dimensional adaptive learning strategy is proposed to identify training samples to improve the surrogate model. In contrast to existing adaptive learning methods that focus on a scalar output or a small number of outputs, this paper addresses adaptive learning with high-dimensional input and output, with a novel learning function that balances exploration and exploitation, i.e., considering unexplored regions and high-error regions, respectively. The adaptive learning is in terms of the active variables in the low-dimensional space, and the newly added training samples can be easily mapped back to the original space for running the expensive physics model. The proposed method is demonstrated for the numerical simulation of an additive manufacturing part, with a high-dimensional field output quantity of interest (residual stress) in the component that has spatial variability due to the stochastic nature of multiple input variables (including process variables and material properties). Various factors in the adaptive learning process are investigated, including the number of training samples, range and distribution of the adaptive training samples, contributions of various errors, and the importance of exploration versus exploitation in the learning function.","PeriodicalId":21994,"journal":{"name":"Structural and Multidisciplinary Optimization","volume":"67 7","pages":"122"},"PeriodicalIF":3.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11236939/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141617153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Concurrent optimization of actuator/sensor layout and control parameter on piezoelectric curved shells with active vibration control for minimizing transient noise 同时优化具有主动振动控制功能的压电曲面壳上的致动器/传感器布局和控制参数，以最大限度地降低瞬态噪声

IF 3.9 2区工程技术

Structural and Multidisciplinary Optimization Pub Date : 2023-12-21 DOI: 10.1007/s00158-023-03707-5

Hao Zheng, Guozhong Zhao, Wen-Xi Han, Yang Yu, Weizhen Chen

引用次数: 0

Aerodynamic shape optimization of gas turbines: a deep learning surrogate model approach 燃气轮机气动外形优化：深度学习代用模型方法

IF 3.9 2区工程技术

Structural and Multidisciplinary Optimization Pub Date : 2023-12-21 DOI: 10.1007/s00158-023-03703-9

Vahid Esfahanian, Mohammad Javad Izadi, Hosein Bashi, Mehran Ansari, Alireza Tavakoli, Mohammad Kordi

引用次数: 0

Stability constraints for geometrically nonlinear topology optimization 几何非线性拓扑优化的稳定性约束

IF 3.9 2区工程技术

Structural and Multidisciplinary Optimization Pub Date : 2023-12-01 DOI: 10.1007/s00158-023-03712-8

Peter D. Dunning

引用次数: 0

Multi-material and thickness optimization of laminated composite structures subject to high-cycle fatigue 承受高循环疲劳的层压复合材料结构的多材料和厚度优化

IF 3.9 2区工程技术

Structural and Multidisciplinary Optimization Pub Date : 2023-12-01 DOI: 10.1007/s00158-023-03708-4

S. Hermansen, Erik Lund

引用次数: 0

Two-stage optimization method for design of reinforced concrete frames using optimal pre-determined section database and non-revisiting genetic algorithm 利用最优预设截面数据库和非重访遗传算法设计钢筋混凝土框架的两阶段优化方法

IF 3.9 2区工程技术

Structural and Multidisciplinary Optimization Pub Date : 2023-12-01 DOI: 10.1007/s00158-023-03709-3

A. Tanhadoust, M. Madhkhan, M. Daei

引用次数: 0

An efficient 2D/3D NURBS-based topology optimization implementation using page-wise matrix operation in MATLAB 在 MATLAB 中使用分页矩阵运算实现基于 NURBS 的高效二维/三维拓扑优化

IF 3.9 2区工程技术

Structural and Multidisciplinary Optimization Pub Date : 2023-12-01 DOI: 10.1007/s00158-023-03701-x

Chungang Zhuang, Z. Xiong, Han Ding

引用次数: 0

An efficient uncertainty propagation analysis method of non-parameterized P-boxes based on dimension-reduction integral and maximum entropy estimation 基于降维积分和最大熵估计的非参数化 P-box 的高效不确定性传播分析方法

IF 3.9 2区工程技术

Structural and Multidisciplinary Optimization Pub Date : 2023-12-01 DOI: 10.1007/s00158-023-03705-7

Huichao Xie, Jinwen Li, Haibo Liu, Hao Hu, Daihui Liao

引用次数: 0