Epilogue
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-57863-2_10
Reference8 articles.
1. Brunton, S. L., and Kutz, J. N. (2022). Data-driven science and engineering: Machine learning, dynamical systems, and control. Cambridge University Press.
2. Di Matteo, A., Kougioumtzoglou, I. A., Pirrotta, A., Spanos, P. D., and Di Paola, M. (2014). Stochastic response determination of nonlinear oscillators with fractional derivatives elements via the wiener path integral. Probabilistic Engineering Mechanics, 38, 127–135.
3. Di Paola, M., and Alotta, G. (2020). Path integral methods for the probabilistic analysis of nonlinear systems under a white-noise process. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 6(4), 040801 (16 pages).
4. Katsidoniotaki, M. I., Psaros, A. F., and Kougioumtzoglou, I. A. (2022). Uncertainty quantification of nonlinear system stochastic response estimates based on the Wiener path integral technique: A Bayesian compressive sampling treatment. Probabilistic Engineering Mechanics, 67, 103193.
5. Mavromatis, I. G., Kougioumtzoglou, I. A., and Spanos, P. D. (2024). An extrapolation approach within the Wiener path integral technique for efficient stochastic response determination of nonlinear systems. International Journal of Non-Linear Mechanics, 160, 104646.
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