Open Access
Mechanics & Industry
Volume 23, 2022
Article Number 26
Number of page(s) 6
Published online 10 October 2022
  1. D.-R. Taur, H.-T. Hsu, A composite guidance strategy for SAAMM with side jet controls. In AIAA Guidance, Navigation, and Control Conference and Exhibit (2001), p. 4427 [Google Scholar]
  2. Y. Gao, L. Han, J. Wang, Trajectory modeling and simulation of anti-missile interception of warship based missile, in Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems. Springer (2016), pp. 500–507 [CrossRef] [Google Scholar]
  3. K.A. Wise, D.J. Roy, Agile missile dynamics and control, J. Guidance Control Dyn. 21, 441–449 (1998) [CrossRef] [Google Scholar]
  4. S. Klus, F. Nüske, S. Peitz, J.H. Niemann, C. Clementi, C. Schütte, Data-driven approximation of the Koopman generator: model reduction, system identification, and control, Physica D 406, 132416 (2020) [CrossRef] [MathSciNet] [Google Scholar]
  5. Y. Lu, J. Duan, Discovering transition phenomena from data of stochastic dynamical systems with Lévy noise, Chaos 30, 093110 (2020) [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  6. M.O. Williams, I.G. Kevrekidis, C. Rowley, A datadriven approximation of the Koopman operator: extending dynamic mode decomposition, J. Nonlinear Sci. 25, 1307–1346 (2015) [CrossRef] [MathSciNet] [Google Scholar]
  7. S.L. Brunton, J. Proctor, J. Kutz, Discovering governing equations from data by sparse identification of nonlinear dynamical systems, Proc. Natl. Acad. Sci. USA 113, 201517384 (2016) [Google Scholar]
  8. K. Champion, B. Lusch, J.N. Kutz, S.L. Brunton, Datadriven discovery of coordinates and governing equations, Proc. Natl. Acad. Sci. USA 116, 1906995116 (2019) [Google Scholar]
  9. S. Lee, M. Kooshkbaghi, K. Spiliotis, C.I. Siettos, I.G. Kevrekidis, Coarse-scale PDEs from fine-scale observations via machine learning, Chaos 30, 013141 (2020) [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  10. S. Rudy, A. Alla, S.L. Brunton, J.N. Kutz, Data-driven identification of parametric partial differential equations, SIAM J. Appl. Dyn. Syst. 18, 643–660 (2019) [CrossRef] [MathSciNet] [Google Scholar]
  11. H. Schaeffer, R. Caflisch, C.D. Hauck, S. Osher, Sparse dynamics for partial differential equations, Proc. Natl. Acad. Sci. USA 110, 6634–6639 (2013) [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  12. L. Boninsegna, F. Nüske, C. Clementi, Sparse learning of stochastic dynamical equations, J. Chem. Phys. 148, 241723 (2018) [CrossRef] [PubMed] [Google Scholar]
  13. Y. Li, J. Duan, A data-driven approach for discovering stochastic dynamical systems with non-Gaussian Lévy noise, Physica D 417, 132830 (2021) [CrossRef] [Google Scholar]
  14. Y. Li, J. Duan, Extracting governing laws from sample path data of non-Gaussian stochastic dynamical systems, J. Stat. Phys. 186, 1–21 (2022) [CrossRef] [MathSciNet] [Google Scholar]
  15. R.T.Q. Chen, Y. Rubanova, J. Bettencourt, D. Duvenaud, Neural ordinary differential equations, Adv. Neural Inf. Process. Syst. 31, 6571–6583 (2018) [Google Scholar]
  16. X. Chen, L. Yang, J. Duan, G.E. Karniadakis, Solving inverse stochastic problems from discrete particle observations using the Fokker–Planck equation and physicsinformed neural networks, SIAM J. Sci. Comput. 43, B811–B830 (2021) [CrossRef] [Google Scholar]
  17. Y. Lu, Y. Li, J. Duan, Extracting stochastic governing laws by non-local Kramers–Moyal formulae, Philos. Trans. Royal Soc. A 380, 20210195 (2022) [CrossRef] [PubMed] [Google Scholar]
  18. M. Raissi, P. Perdikaris, G. Karniadakis, Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations, J. Comput. Phys. 378, 686–707 (2019) [CrossRef] [MathSciNet] [Google Scholar]

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