Author: admin

Bezek, L. B. et al. Effect of part size, displacement rate, and aging on compressive properties of elastomeric parts of different unit cell topologies formed by vat photopolymerization additive manufacturing. Polymers 16, 3166 (2024).

Google Scholar 

Yang, L. et al. Additive manufacturing of metal cellular structures: design and fabrication. Jom 67, 608–615 (2015).

Google Scholar 

Lin, H. et al. 3d printing of porous ceramics for enhanced thermal insulation properties. Adv. Sci. 12, 2412554 (2025).

Google Scholar 

Schaedler, T. A. et al. Designing metallic microlattices for energy absorber applications. Adv. Eng. Mater. 16, 276–283 (2014).

Google Scholar 

Schaedler, T. A. & Carter, W. B. Architected cellular materials. Annual Rev. Mater. Res. 46, 187–210 (2016).

Google Scholar 

Boursier Niutta, C., Ciardiello, R. & Tridello, A. Experimental and numerical investigation of a lattice structure for energy absorption: application to the design of an automotive crash absorber. Polymers 14, 1116 (2022).

Google Scholar 

Mohsenizadeh, M., Gasbarri, F., Munther, M., Beheshti, A. & Davami, K. Additively-manufactured lightweight metamaterials for energy absorption. Mater. Des. 139, 521–530 (2018).

Google Scholar 

Uribe-Lam, E., Treviño-Quintanilla, C. D., Cuan-Urquizo, E. & Olvera-Silva, O. Use of additive manufacturing for the fabrication of cellular and lattice materials: a review. Mater. Manuf. Process. 36, 257–280 (2021).

Google Scholar 

Mueller, J., Raney, J. R., Shea, K. & Lewis, J. A. Architected lattices with high stiffness and toughness via multicore-shell 3d printing. Adv.Mater. 30, 1705001 (2018).

Google Scholar 

Lei, H. et al. Evaluation of compressive properties of slm-fabricated multi-layer lattice structures by experimental test and \(\mu\)-ct-based finite element analysis. Materi. Des. 169, 107685 (2019).

Google Scholar 

Kumar, A., Collini, L., Daurel, A. & Jeng, J.-Y. Design and additive manufacturing of closed cells from supportless lattice structure. Additive Manuf. 33, 101168 (2020).

Google Scholar 

Nakarmi, S. et al. The role of unit cell topology in modulating the compaction response of additively manufactured cellular materials using simulations and validation experiments. Model. Simul. Mater. Sci. Eng. 32, 055029 (2024).

Google Scholar 

Nakarmi, S. et al. Mesoscale simulations and validation experiments of polymer foam compaction-volume density effects. Mater. Lett. 382, 137864 (2025).

Google Scholar 

Xia, L. & Breitkopf, P. Design of materials using topology optimization and energy-based homogenization approach in matlab. Struct. Multidisciplinary Optim. 52, 1229–1241 (2015).

Google Scholar 

Radman, A., Huang, X. & Xie, Y. Topology optimization of functionally graded cellular materials. J. Mater. Sci. 48, 1503–1510 (2013).

Google Scholar 

Bauer, J., Hengsbach, S., Tesari, I., Schwaiger, R. & Kraft, O. High-strength cellular ceramic composites with 3d microarchitecture. Procd. National Acad. Sci. 111, 2453–2458 (2014).

Google Scholar 

Nguyen, J., Park, S.-I. & Rosen, D. Heuristic optimization method for cellular structure design of light weight components. Int. J. Precision Eng. Manuf. 14, 1071–1078 (2013).

Google Scholar 

Meier, T. et al. Obtaining auxetic and isotropic metamaterials in counterintuitive design spaces: an automated optimization approach and experimental characterization. npj Comput. Mater. 10, 3 (2024).

Google Scholar 

Vangelatos, Z. et al. Strength through defects: A novel bayesian approach for the optimization of architected materials. Sci. Adv. 7, eabk2218 (2021).

Google Scholar 

Ramesh, A. et al. Zero-shot text-to-image generation. In International conference on machine learning, 8821–8831 (Pmlr, 2021).

Ramesh, A., Dhariwal, P., Nichol, A., Chu, C. & Chen, M. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.061251, 3 (2022).

Yao, Z. et al. Inverse design of nanoporous crystalline reticular materials with deep generative models. Nat. Mach. Intell. 3, 76–86 (2021).

Google Scholar 

Sanchez-Lengeling, B. & Aspuru-Guzik, A. Inverse molecular design using machine learning: Generative models for matter engineering. Science 361, 360–365 (2018).

Google Scholar 

Zhavoronkov, A. et al. Deep learning enables rapid identification of potent ddr1 kinase inhibitors. Nat. Biotechnol. 37, 1038–1040 (2019).

Google Scholar 

Liao, W., Lu, X., Fei, Y., Gu, Y. & Huang, Y. Generative ai design for building structures. Autom. Construct. 157, 105187 (2024).

Google Scholar 

Kingma, D. P., Welling, M. et al. Auto-encoding variational bayes (2013).

Goodfellow, I. J. et al. Generative adversarial nets. Adv. Neural Inf. Process. Syst. 27 (2014).

Ho, J., Jain, A. & Abbeel, P. Denoising diffusion probabilistic models. Adv. Neural Inf. Process. Syst. 33, 6840–6851 (2020).

Google Scholar 

Sohn, K., Lee, H. & Yan, X. Learning structured output representation using deep conditional generative models. Adv. Neural Inf. Process. Syst. 28 (2015).

Mirza, M. & Osindero, S. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014).

Dhariwal, P. & Nichol, A. Diffusion models beat gans on image synthesis. Adv. Neural Inf. Process. Syst. 34, 8780–8794 (2021).

Google Scholar 

Lee, D., Chen, W., Wang, L., Chan, Y.-C. & Chen, W. Data-driven design for metamaterials and multiscale systems: a review. Adv. Mater. 36, 2305254 (2024).

Google Scholar 

Zheng, X., Zhang, X., Chen, T.-T. & Watanabe, I. Deep learning in mechanical metamaterials: from prediction and generation to inverse design. Adv. Mater. 35, 2302530 (2023).

Google Scholar 

Wang, L. et al. Deep generative modeling for mechanistic-based learning and design of metamaterial systems. Comput. Methods App. Mech. Eng. 372, 113377 (2020).

Google Scholar 

Zheng, L., Karapiperis, K., Kumar, S. & Kochmann, D. M. Unifying the design space and optimizing linear and nonlinear truss metamaterials by generative modeling. Nat. Commun. 14, 7563 (2023).

Google Scholar 

Tian, J., Tang, K., Chen, X. & Wang, X. Machine learning-based prediction and inverse design of 2d metamaterial structures with tunable deformation-dependent poisson’s ratio. Nanoscale 14, 12677–12691 (2022).

Google Scholar 

Zheng, X., Chen, T.-T., Guo, X., Samitsu, S. & Watanabe, I. Controllable inverse design of auxetic metamaterials using deep learning. Mater. Des. 211, 110178 (2021).

Google Scholar 

Challapalli, A., Patel, D. & Li, G. Inverse machine learning framework for optimizing lightweight metamaterials. Materi. Des. 208, 109937 (2021).

Google Scholar 

Vlassis, N. N. & Sun, W. Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties. Comput. Methods Appl. Mech. Eng. 413, 116126 (2023).

Google Scholar 

Bastek, J.-H. & Kochmann, D. M. Inverse design of nonlinear mechanical metamaterials via video denoising diffusion models. Nat. Mach. Intell. 5, 1466–1475 (2023).

Google Scholar 

Meier, T. et al. Scalable phononic metamaterials: Tunable bandgap design and multi-scale experimental validation. Mater. Des. 252, 113778 (2025).

Google Scholar 

Kumar, S., Tan, S., Zheng, L. & Kochmann, D. M. Inverse-designed spinodoid metamaterials. npj Comput. Mater. 6, 73 (2020).

Google Scholar 

Nakarmi, S., Leiding, J. A., Lee, K.-S. & Daphalapurkar, N. P. Predicting non-linear stress-strain response of mesostructured cellular materials using supervised autoencoder. Comput. Methods Appl. Mech. Eng. 432, 117372 (2024).

Google Scholar 

McNeel, R. et al. Grasshopper-algorithmic modeling for rhino. http://www.grasshopper3d.com (2013).

Dassault Systèmes. Abaqus Analysis User’s Manual, Version 2020 (2020).

Mooney, M. A theory of large elastic deformation. J. Appl. Phys. 11, 582–592 (1940).

Google Scholar 

Rivlin, R. Large elastic deformations of isotropic materials. i. fundamental concepts. Philosophical Trans. Royal Soc. London. Series A, Math. Phys. Sci. 240, 459–490 (1948).

Google Scholar 

Abdi, H. & Williams, L. J. Principal component analysis. Wiley Interdisciplinary Rev. Comput. Statist. 2, 433–459 (2010).

Google Scholar 

Yang, C., Kim, Y., Ryu, S. & Gu, G. X. Prediction of composite microstructure stress-strain curves using convolutional neural networks. Mater. Des. 189, 108509 (2020).

Google Scholar 

Ioffe, S. & Szegedy, C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Int. Conference Mach. Learn., 448–456 (pmlr, 2015).

Li, X., Chen, S., Hu, X. & Yang, J. Understanding the disharmony between dropout and batch normalization by variance shift. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2682–2690 (2019).

Kullback, S. & Leibler, R. A. On information and sufficiency. Annals Math. Statist. 22, 79–86 (1951).

Google Scholar 

Higgins, I. et al. Early visual concept learning with unsupervised deep learning. arXiv preprint arXiv:1606.05579 (2016).

Fu, H. et al. Cyclical annealing schedule: A simple approach to mitigating kl vanishing. arXiv preprint arXiv:1903.10145 (2019).

Smith, S. L., Kindermans, P.-J., Ying, C. & Le, Q. V. Don’t decay the learning rate, increase the batch size. arXiv preprint arXiv:1711.00489 (2017).

Liu, Y., Neophytou, A., Sengupta, S. & Sommerlade, E. Relighting images in the wild with a self-supervised siamese auto-encoder. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 32–40 (2021).

Wang, Z., Bovik, A. C., Sheikh, H. R. & Simoncelli, E. P. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600–612 (2004).

Google Scholar 

Dice, L. R. Measures of the amount of ecologic association between species. Ecology 26, 297–302 (1945).

Google Scholar 

Zhao, F., Huang, Q. & Gao, W. Image matching by normalized cross-correlation. In 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, vol. 2, II–II (IEEE, 2006).