Citation: | Yang W. Digintel mechanics—Governing the digintel era. Advances in Mechanics, 2024, 54(4): 1-10 doi: 10.6052/1000-0992-24-042 |
[1] |
牛顿. 2018. 自然哲学之数学原理 (王克迪译). 北京: 北京大学出版社 (原著出版于1687年).
原著出版于1687年
|
[2] |
托马斯·库恩. 2012. 科学革命的结构(伊安·哈金导读, 金吾伦和胡新和译). 北京: 北京大学出版社 (原著出版于1962年).
原著出版于1962年
|
[3] |
王鹏, 孙升, 张庆, 张统一. 2019. 力学信息学简介. 自然杂志, 40(5): 313-322. doi: 10.3969/j.issn.0253-9608.2019.05.001
|
[4] |
杨卫. 2024. 力学基本问题. 上海: 科学出版社.
|
[5] |
杨卫, 赵沛. 2024a. 范式融合导向的数智时代力学专业核心课程. 力学与实践, 待出版.
|
[6] |
杨卫, 赵沛. 2024b. 基础学科领域本科教育教学改革试点工作—力学“101计划”. 力学与实践, 待出版.
|
[7] |
Gao H, Ji B, Jager I L, et al. 2003. Materials become insensitive to flaws at nanoscale: Lessons from nature. Proceedings of the National Academy of Sciences, 100(10): 5597-5600. doi: 10.1073/pnas.0631609100
|
[8] |
He G, Jin G, Yang Y. 2017. Space-time correlations and dynamic coupling in turbulent flows. Annual Review of Fluid Mechanics, 49: 51-70. doi: 10.1146/annurev-fluid-010816-060309
|
[9] |
Huang M C, Liu C, Guo Y L, et al. 2024. A mechanics-based data-free problem independent machine learning (PIML) model for large-scale structural analysis and design optimization. Journal of the Mechanics and Physics of Solids, 193: 105893. doi: 10.1016/j.jmps.2024.105893
|
[10] |
Jin H B, Liu X W, Shao Y C, et al. 2022. High-speed quadrupedal locomotion by imitation-relaxation reinforcement learning. Nature Machine Intelligence, 4(12): 1198-1208. doi: 10.1038/s42256-022-00576-3
|
[11] |
Karapiperis K, Stainier L, Ortiz M, et al. 2021. Data-driven multiscale modeling in mechanics. Journal of the Mechanics and Physics of Solids, 147: 104239. doi: 10.1016/j.jmps.2020.104239
|
[12] |
Kepler J. 1622. Summary of copernican astronomy. Austria: Johann Planck.
|
[13] |
Kepler J. 1997. The harmony of the world (Aiton E J, Duncan A M, Field J V Trans). Pennsylvania: American Philosophical Society. (Original work published 1619).
|
[14] |
Meng Z Y, Zhong J R, Xu S B, et al. 2024. Simulating unsteady flows on a superconducting quantum processor. Communications Physics, 7(1): 349. doi: 10.1038/s42005-024-01845-w
|
[15] |
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. doi: 10.1016/j.jcp.2018.10.045
|
[16] |
Raissi M, Yazdani A, Karniadakis G E. 2020. Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations. Science, 367(6481): 1026-1030. doi: 10.1126/science.aaw4741
|
[17] |
Shen W, Yao J, Yang Y. 2024. Designing turbulence with entangled vortices. Proceedings of the National Academy of Sciences, 121 (35): e2405351121.
|
[18] |
Zhao Q, Zhu Q, Zhang Z, Yin B, Gao H, Zhou H. 2024. A machine learning–based framework for mapping hydrogen at the atomic scale. Proceedings of the National Academy of Sciences, 121 (39): e2410968121.
|