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International Journal of AI for
            Materials and Design                                                   AI applications in composite materials



            .                                                     infrared thermography. Compos Struct. 2022;290:115543.
            116. Wang W, Cheney W, Amirkhizi AV. Generative design      doi: 10.1016/j.compstruct.2022.115543
               of graded metamaterial arrays for dynamic response   127. Yang L, Zhang Z, Song Y,  et al. Diffusion models:
               modulation. Mater Design. 2024;237:112550.
                                                                  A comprehensive survey of methods and applications. ACM
               doi: 10.1016/j.matdes.2023.112550                  Comput Surv. 2023;56(4):1-39.
            117. Sun H, Wang X, Li J, Li Z, Guan Z. Efficient property-     doi: 10.1145/3626235
               oriented design of composite layups via controllable   128. Lyu X, Ren X. Microstructure reconstruction of 2D/3D
               latent features using generative VAE. Compos Sci Technol.   random materials via diffusion-based deep generative
               2025;259:110936.
                                                                  models. Sci Rep. 2024;14(1):5041.
               doi: 10.1016/j.compscitech.2024.110936
                                                                  doi: 10.1038/s41598-024-54861-9
            118. Zhang C, Liu X, Wei D, Bo L. Predicting damage and   129. Lee KH, Yun GJ. Microstructure reconstruction using
               quantifying uncertainty in composite plates with   diffusion-based generative models. Mech Adv Mater Struct.
               semi-supervised  VAE-BNN  model.  Measurement.     2024;31(18):4443-4461.
               2024;236:115069.
                                                                  doi: 10.1080/15376494.2023.2198528
               doi: 10.1016/j.measurement.2024.115069
                                                               130. Bastek JH, Kochmann DM. Inverse design of nonlinear
            119. Wang G, Zhang L, Xuan S, et al. An efficient surrogate model   mechanical metamaterials via video denoising diffusion
               for damage forecasting of composite laminates based on
               deep learning. Compos Struct. 2024;331:117863.     models. Nat Mach Intell. 2023;5(12):1466-1475.
                                                                  doi: 10.1038/s42256-023-00762-x
               doi: 10.1016/j.compstruct.2023.117863
                                                               131. Huang T, Gao Y, Li Z, Hu Y, Xuan F. A hybrid deep learning
            120. Jiang D, Qian H, Wang Y, Zheng J, Zhang D, Li Q. Data   framework based on diffusion model and deep residual
               driven  prediction  of  fatigue  residual  stiffness  of  braided   neural network for defect detection in composite plates.
               ceramic matrix composites based on Latent-ODE. Compos   Appl Sci. 2023;13(10):5843.
               Struct. 2023;323:117504.
                                                               132. Kobyzev I, Prince SJ, Brubaker MA. Normalizing flows: An
               doi: 10.1016/j.compstruct.2023.117504
                                                                  introduction and review of current methods.  IEEE Trans
            121. Goodfellow IJ, Pouget-Abadie J, Mirza M, et al. Generative   Pattern Anal Mach Intell. 2020;43(11):3964-3979.
               Adversarial  Nets.  In:  Advances in Neural Information
               Processing Systems. Vol. 27; 2014 [arXiv Preprint].     doi: 10.1109/TPAMI.2020.2992934
                                                               133. Mirzaee H, Kamrava S. Inverse design of microstructures
            122. Yang Z, Yu CH, Buehler MJ. Deep learning model to predict   using  conditional  continuous  normalizing  flows.  Acta
               complex stress and strain fields in hierarchical composites.   Mater. 2025;285:120704.
               Sci Adv. 2021;7(15):eabd7416.
                                                                  doi: 10.1016/j.actamat.2024.120704
               doi: 10.1126/sciadv.abd7416
                                                               134. Zhang C, Lu J, Zhao Y. Generative pre-trained transformers
            123. Guo R, Alves M, Mehdikhani M, Breite C, Swolfs Y.
               Synthesising realistic 2D microstructures of unidirectional   (GPT)-based automated data mining for building energy
               fibre-reinforced composites with a generative adversarial   management: Advantages, limitations and the future. Energy
               network. Compos Sci Technol. 2024;250:110539.      Built Environ. 2024;5(1):143-169.
                                                                  doi: 10.1016/j.enbenv.2023.06.005
               doi: 10.1016/j.compscitech.2024.110539
                                                               135. Shah B, Sinha A, Saxena P. Image GPT with Super Resolution.
            124. Wang Y, Sun J, Wang X, et al. Multi-objective optimization   Berlin: Springer; 2022. p. 99-107.
               of  engineered cementitious composite  based  on  machine
               learning and generative adversarial network.  J  Build Eng.   136. Hatamizadeh A, Song J, Liu G, Kautz J, Vahdat A.  Diffit:
               2024;96:110471.                                    Diffusion Vision Transformers for Image Generation. Berlin:
                                                                  Springer; 2024. p. 37-55.
               doi: 10.1016/j.jobe.2024.110471
                                                               137. Generale AP, Robertson AE, Kelly C, Kalidindi SR.
            125. Li M, Jia G, Cheng Z, Shi Z. Generative adversarial network
               guided topology optimization of periodic structures via   Inverse stochastic microstructure design.  Acta Mater.
               Subset Simulation. Compos Struct. 2021;260:113254.  2024;271:119877.
                                                                  doi: 10.2139/ssrn.4590691
               doi: 10.1016/j.compstruct.2020.113254
                                                               138. Murphy RR. Introduction to AI Robotics. Cambridge: MIT
            126. Cheng L, Tong Z, Xie S, Kersemans M. IRT-GAN:
               A generative adversarial network with a multi-headed fusion   Press; 2019.
               strategy for automated defect detection in composites using   139. Yurtsever E, Lambert J, Carballo A, Takeda K. A  survey


            Volume 2 Issue 3 (2025)                         29                        doi: 10.36922/IJAMD025210016
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