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第 46 卷       徐    蓉，等： 基于数据驱动混杂点阵结构的惯性效应和结构效应竞争机制分析                            第 10 期

                    shaped mesostructure [J]. Composite Structures, 2019, 226: 111219. DOI: 10.1016/j.compstruct.2019.111219.
               [20]   YAN C, FENG X M, WICK C, et al. Machine learning assisted discovery of new thermoset shape memory polymers based on
                    a small training dataset [J]. Polymer, 2021, 214: 123351. DOI: 10.1016/j.polymer.2020.123351.
               [21]   MURUGAN J, MUNIYANDY E, MIROSLAV M, et al. Accurate estimation of tensile strength of 3D printed parts using
                    machine learning algorithms [J]. Processes, 2022, 10(6): 1158. DOI: 10.3390/pr10061158.
               [22]   郑伟达, 张惠然, 胡红青, 等. 基于不同机器学习算法的钙钛矿材料性能预测 [J]. 中国有色金属学报, 2019, 29(4):
                    803–809. DOI: 10.19476/j.ysxb.1004.0609.2019.04.17.
                    ZHENG  W  D,  ZHANG  H  R,  HU  H  Q,  et  al.  Performance  prediction  of  perovskite  materials  based  on  different  machine
                    learning algorithms [J]. The Chinese Journal of Nonferrous Metals, 2019, 29(4): 803–809. DOI: 10.19476/j.ysxb.1004.0609.
                    2019.04.17.
               [23]   吴炜, 孙强. 应用机器学习加速新材料的研发 [J]. 中国科学: 物理学 力学 天文学, 2018, 48(10): 58–70. DOI:
                    10.1360/sspma2018-00073.
                    WU W, SUN Q. Applying machine learning to accelerate new materials development [J]. Scientia Sinica Physica, Mechanica
                    & Astronomica, 2018, 48(10): 58–70. DOI: 10.1360/sspma2018-00073.
               [24]   NATH  D,  ANKIT,  NEOG  D  R,  et  al.  Application  of  machine  learning  and  deep  learning  in  finite  element  analysis:  a
                    comprehensive  review  [J].  Archives  of  Computational  Methods  in  Engineering,  2024,  31(5):  2945–2984.  DOI:  10.1007/
                    s11831-024-10063-0.
               [25]   王扬卫, 姜炳岳, 程兴旺, 等. 基于机器学习的梯度点阵材料优化设计 [J]. 北京理工大学学报, 2023, 43(3): 311–319. DOI:
                    10.15918/j.tbit1001-0645.2022.062.
                    WANG Y W, JIANG B Y, CHENG X W, et al. Optimization design of graded lattice materials based on machine learning [J].
                    Transactions of Beijing Institute of Technology, 2023, 43(3): 311–319. DOI: 10.15918/j.tbit1001-0645.2022.062.
               [26]   WU  Y  R,  MAO  Z  F,  FENG  Y  Q.  Energy  absorption  prediction  for  lattice  structure  based  on  D2  shape  distribution  and
                    machine learning [J]. Composite Structures, 2023, 319: 117136. DOI: 10.1016/j.compstruct.2023.117136.
               [27]   ISANAKA  B  R,  MUKHOPADHYAY  T,  VARMA  R  K,  et  al.  On  exploiting  machine  learning  for  failure  pattern  driven
                    strength enhancement of honeycomb lattices [J]. Acta Materialia, 2022, 239: 118226. DOI: 10.1016/j.actamat.2022.118226.
               [28]   YÜKSEL N, EREN O, BÖRKLÜ H R, et al. Mechanical properties of additively manufactured lattice structures designed by
                    deep learning [J]. Thin-Walled Structures, 2024, 196: 111475. DOI: 10.1016/j.tws.2023.111475.
               [29]   陈思远. 轻质三维梯度夹芯结构的设计、制备及冲击吸能特性研究                 [D]. 哈尔滨: 哈尔滨工程大学, 2021. DOI: 10.27060/
                    d.cnki.ghbcu.2021.000517.
                    CHEN S Y. Research on design, fabrication and impact energy absorption characteristics of lightweight three-dimensional
                    gradient sandwich structure [D]. Harbin: Harbin Engineering University, 2021. DOI: 10.27060/d.cnki.ghbcu.2021.000517.
               [30]   DUAN X M, YU L, CAI S L, et al. Inertia effect of deformation in amorphous solids: a dynamic mesoscale model [J]. Journal
                    of the Mechanics and Physics of Solids, 2024, 193: 105917. DOI: 10.1016/j.jmps.2024.105917.
               [31]   CUI Z, SUN Z P, ZHAO J Y, et al. Response of hybrid plate-rod lattices to static and dynamic compression – an experimental
                    study [J]. International Journal of Impact Engineering, 2025, 202: 105321. DOI: 10.1016/j.ijimpeng.2025.105321.
               [32]   ZHAO J Y, CUI Z, WANG S L, et al. Flexural response of additively manufactured honeycomb sandwich structures with
                    continuous density-gradient variations [J]. Thin-Walled Structures, 2024, 197: 111642. DOI: 10.1016/j.tws.2024.111642.
               [33]   WANG  J,  ZHU  L,  DAI  T,  et  al.  Deep  memory  network  with  Bi-LSTM  for  personalized  context-aware  citation
                    recommendation [J]. Neurocomputing, 2020, 410: 103–113. DOI: 10.1016/j.neucom.2020.05.047.
               [34]   WU D W, XUE X H, ZHOU L. Improving the robustness of tree-based prediction model of oil/water relative permeability
                    through  hyperparameter  optimization  and  re-ensemble  algorithm  [J].  Fuel,  2025,  385:  134146.  DOI:  10.1016/j.fuel.2024.
                    134146.
               [35]   JAIN S, BHOWMIK A, LEE J. Machine learning approaches for predicting and validating mechanical properties of Mg rare
                    earth alloys for light weight applications [J]. Science and Technology of Advanced Materials, 2025, 26(1): 2449811. DOI:
                    10.1080/14686996.2025.2449811.
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