Generative AI-Enhanced Surrogate Modeling for Fast Dynamic Analysis of Smart Laminated Structures with Viscoelastic Damping
Keywords:
surrogate modeling; generative adversarial networks; smart laminated structures; viscoelastic damping; dynamic analysis; physics-informed machine learning; system-level governanceAbstract
The increasing complexity of modern engineered systems demands rapid, high-fidelity structural analysis, particularly for smart laminated composites that incorporate viscoelastic damping materials and distributed sensing-actuation networks. Conventional full-order dynamic simulations, though accurate, are computationally prohibitive for real-time health monitoring, iterative design, and adaptive control. This paper presents a system-level investigation into generative artificial intelligence-enhanced surrogate modeling as a transformative approach to accelerate dynamic analysis of such multi-physics structures. Moving beyond purely computational mechanics, the work examines architectural trade-offs between data-driven generative models—generative adversarial networks and variational autoencoders—and physics-informed training strategies, emphasizing the joint role of accuracy, latency, and generalization. A comprehensive discussion is provided on integrating these surrogates within larger socio-technical infrastructures, including cloud-based digital twins, edge computing platforms, and automated decision pipelines. Governance challenges are examined through the lenses of data provenance, model drift, robustness against adversarial perturbations, and the ethical imperative of equitable access to advanced structural intelligence. Additionally, the paper addresses sustainability outcomes, regulatory policy considerations, and the certification hurdles that arise when AI-generated predictions inform safety-critical decisions. By synthesizing insights from smart materials, machine learning, systems engineering, and science and technology policy, this contribution delineates a pathway toward resilient, scalable, and responsible deployment of generative surrogate models for next-generation laminated structures.
References
1. Simpson, T. W., Poplinski, J. D., Koch, P. N., & Allen, J. K. (2001). Metamodels for computer-based engineering design: survey and recommendations. Engineering with Computers, 17(2), 129–150.
2. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, 27.
3. Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019). Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378, 686–707.
4. Trindade, M. A., Benjeddou, A., & Ohayon, R. (2000). Modeling of frequency-dependent viscoelastic materials for active-passive vibration damping. Journal of Vibration and Acoustics, 122(2), 169–174.
5. Lam, K. Y., Peng, X. Q., Liu, G. R., & Reddy, J. N. (1997). A finite-element model for piezoelectric composite laminates. Smart Materials and Structures, 6(5), 583–591.
6. Mead, D. J., & Markus, S. (1969). The forced vibration of a three-layer, damped sandwich beam with arbitrary boundary conditions. Journal of Sound and Vibration, 10(2), 163–175.
7. Balmès, E. (1996). Parametric families of reduced finite element models: theory and applications. Mechanical Systems and Signal Processing, 10(4), 381–394.
8. Tripathy, R. K., & Bilionis, I. (2018). Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification. Journal of Computational Physics, 375, 565–588.
9. Lu, J., Zhan, Z., Liu, X., & Wang, P. (2018). Numerical modeling and model updating for smart laminated structures with viscoelastic damping. Smart Materials and Structures, 27(7), 075038.
10. Ghorbanian, P., & Bagheri, S. (2022). Generative adversarial networks for surrogate modeling of complex physics: Applications to turbulence. Journal of Computational Physics, 448, 110740.
11. Baz, A., & Ro, J. (1995). Optimum design and control of active constrained layer damping. Journal of Vibration and Acoustics, 117(1), 25–32.
12. Berthelot, J. M., & Sefrani, Y. (2004). Damping analysis of composite materials and structures. Composite Structures, 65(3–4), 421–432.
13. Mares, C., Mottershead, J. E., & Friswell, M. I. (2006). Stochastic model updating: Part 1—Theory and simulated example. Mechanical Systems and Signal Processing, 20(7), 1674–1695.
14. Viana, F. A. C., Haftka, R. T., & Steffen, V. (2009). Multiple surrogates: how cross-validation errors can help us to obtain the best predictor. Structural and Multidisciplinary Optimization, 39(4), 439–457.
15. Oh, S., Jung, Y., Kim, S., Lee, I., & Kang, N. (2019). Deep generative design: Integration of topology optimization and generative models. Journal of Mechanical Design, 141(11), 111405.
16. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), Article 115, 1–35.
17. Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019). Digital twin in industry: state-of-the-art. IEEE Transactions on Industrial Informatics, 15(4), 2405–2415.
18. Guo, L., Wang, Y., Yang, Z., Liu, Q., & Gao, J. (2021). A cloud-based intelligent simulation platform for multi-disciplinary collaborative design. Advanced Engineering Informatics, 50, 101399.
19. Marrel, A., Iooss, B., Laurent, B., & Roustant, O. (2009). Calculations of Sobol indices for the Gaussian process metamodel. Reliability Engineering & System Safety, 94(3), 742–751.
20. Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1).
21. Giurgiutiu, V. (2005). Tuned lamb wave excitation and detection with piezoelectric wafer active sensors for structural health monitoring. Journal of Intelligent Material Systems and Structures, 16(4), 291–305.
22. Kingma, D. P., & Welling, M. (2013). Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Advanced Artificial Intelligence Research
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
