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Data-driven modeling of a four-dimensional stochastic projectile system |
| Yong Huang(黄勇)1 and Yang Li(李扬)2,† |
1 School of Energy and Power Engineering, Nanjing University of Science and Technology, Nanjing 210094, China;
2 School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China |
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Abstract The dynamical modeling of projectile systems with sufficient accuracy is of great difficulty due to high-dimensional space and various perturbations. With the rapid development of data science and scientific tools of measurement recently, there are numerous data-driven methods devoted to discovering governing laws from data. In this work, a data-driven method is employed to perform the modeling of the projectile based on the Kramers-Moyal formulas. More specifically, the four-dimensional projectile system is assumed as an Itô stochastic differential equation. Then the least square method and sparse learning are applied to identify the drift coefficient and diffusion matrix from sample path data, which agree well with the real system. The effectiveness of the data-driven method demonstrates that it will become a powerful tool in extracting governing equations and predicting complex dynamical behaviors of the projectile.
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Received: 28 November 2021
Revised: 16 January 2022
Accepted manuscript online: 24 January 2022
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PACS:
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05.10.-a
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(Computational methods in statistical physics and nonlinear dynamics)
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05.10.Gg
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(Stochastic analysis methods)
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05.40.-a
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(Fluctuation phenomena, random processes, noise, and Brownian motion)
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02.30.Zz
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(Inverse problems)
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| Fund: This research was supported by the Six Talent Peaks Project in Jiangsu Province, China (Grant No. JXQC-002). |
Corresponding Authors:
Yang Li
E-mail: liyangbx5433@163.com
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Cite this article:
Yong Huang(黄勇) and Yang Li(李扬) Data-driven modeling of a four-dimensional stochastic projectile system 2022 Chin. Phys. B 31 070501
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