Page 32 - 《爆炸与冲击》2025年第5期

第 45 卷 柏劲松，等： 端到端机器学习代理模型构建及其在爆轰驱动问题中的应用 第 5 期

感谢中国工程物理研究院流体物理研究所实验物理数值模拟创新研究中心原职工张恒第在计
算程序实现上提供的帮助。

参考文献：

[1] 杨凯, 吕文泉, 闫胜斌. 智能化时代的作战方式变革 [J]. 军事文摘, 2022(1): 7–11.
YANG K, LYU W Q, YAN S B. Reform of combat methods in the era of intelligence [J]. Military Digest, 2022(1): 7–11.
[2] 中国国防科技信息中心. DARPA 成功完成“海上猎手”无人水面艇项目 [R/OL]. (2018-02-02)[2024-04-07]. https://
www.sohu.com/a/220477417_313834.
China National Defense Science and Technology Information Center. DARPA successfully completed the Sea Hunter
unmanned surface vehicle project [R/OL]. (2018-02-02)[2024-04-07]. https://www.sohu.com/a/220477417_313834.
[3] DATTELBAUM A M. Materials dynamics: LA-UR-22-25248 [R]. Los Alamos: Los Alamos National Laboratory, 2022.
[4] SHALEV-SHWARTZ S, SHAMMAH S, SHASHUA A. Safe, multi-agent, reinforcement learning for autonomous
driving [EB/OL]. arXiv: 1610.03295. (2016-11-11)[2024-04-10]. https://arxiv.org/abs/1610.03295. DOI: 10.48550/arXiv.1610.
03295.
[5] CHAR D S, SHAH N H, MAGNUS D. Implementing machine learning in health care—addressing ethical challenges [J]. The
New England Journal of Medicine, 2018, 378(11): 981–983. DOI: 10.1056/NEJMp1714229.
[6] LIN W Y, HU Y H, TSAI C F. Machine learning in financial crisis prediction: a survey [J]. IEEE Transactions on Systems,
Man, and Cybernetics, Part C (Applications and Reviews), 2012, 42(4): 421–436. DOI: 10.1109/TSMCC.2011.2170420.
[7] LIPSON H, POLLACK J B. Automatic design and manufacture of robotic lifeforms [J]. Nature, 2000, 406(6799): 974–978.
DOI: 10.1038/35023115.
[8] BERRAL J L, GOIRI Í, NOU R, et al. Towards energy-aware scheduling in data centers using machine learning [C]//
Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking. Passau: ACM, 2010: 215–
224. DOI: 10.1145/1791314.1791349.
[9] ENGEL A, VAN DEN BROECK C. Statistical mechanics of learning [M]. Cambridge: Cambridge University Press, 2001.
[10] CARLEO G, TROYER M. Solving the quantum many-body problem with artificial neural networks [J]. Science, 2017,
355(6325): 602–606. DOI: 10.1126/science.aag2302.
[11] SCHAFER N P, KIM B L, ZHENG W H, et al. Learning to fold proteins using energy landscape theory [J]. Israel Journal of
Chemistry, 2014, 54(8/9): 1311–1337. DOI: 10.1002/ijch.201300145.
[12] VANDERPLAS J, CONNOLLY A J, IVEZIĆ Ž, et al. Introduction to astroML: machine learning for astrophysics [C]//
Proceedings of 2012 Conference on Intelligent Data Understanding. Boulder: IEEE, 2012: 47–54. DOI: 10.1109/CIDU.
2012.6382200.
[13] BLASCHKE D N, NGUYEN T, NITOL M, et al. Machine learning based approach to predict ductile damage model
parameters for polycrystalline metals [J]. Computational Materials Science, 2023, 229: 112382. DOI: 10.1016/j.commatsci.
2023.112382.
[14] FERNÁNDEZ-GODINO M G, PANDA N, O’MALLEY D, et al. Accelerating high-strain continuum-scale brittle fracture
simulations with machine learning [J]. Computational Materials Science, 2021, 186: 109959. DOI: 10.1016/j.commatsci.
2020.109959.
[15] 杨寓翔, 李炜, 申建民, 等. 机器学习在相变中的应用 [J]. 中国科学: 物理学 力学 天文学, 2023, 53(9): 290011. DOI:
10.1360/SSPMA-2023-0130.
YANG Y X, LI W, SHEN J M, et al. Machine learning applications in phase transitions [J]. Scientia Sinica Physica,
Mechanica & Astronomica, 2023, 53(9): 290011. DOI: 10.1360/SSPMA-2023-0130.
[16] 刘泮宏. 基于机器学习的湍流建模应用研究 [D]. 哈尔滨: 哈尔滨工业大学, 2021. DOI: 10.27061/d.cnki.ghgdu.2021.
001683.
LIU P H. Application of turbulence modeling based on machine learning [D]. Harbin: Harbin Institute of Technology, 2021.
DOI: 10.27061/d.cnki.ghgdu.2021.001683.
[17] 刘永泽. 水下爆炸载荷下板架结构毁伤特性的机器学习方法及应用研究 [D]. 哈尔滨: 哈尔滨工程大学, 2022. DOI:

051101-11