Computing system for simulation intelligence

Authors

Guangming TAN, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, ChinaFollow
Weile JIA, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
Zhan WANG, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
Guojun YUAN, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
En SHAO, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
Ninghui SUN, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, ChinaFollow

Keywords

scientific simulation, simulation intelligence, artificial intelligence, computing system, Zetta-scale computing

Document Type

Vigorously Promote Scientific Research Paradigm Transform

Abstract

This study refers computer simulation in scientific research to as scientific simulation. Based on its narrow and broad definitions, this study divides scientific simulation into three stages: numerical computation, simulation intelligence, and science brain. Now, scientific simulation is entering the era of simulation intelligence, i. e., driven by scientific big data and artificial intelligence technology, scientific simulation is shifting from traditional numerical simulation to simulation integrated with artificial intelligence. In order to understand what the right computing system for simulation intelligence is, the design guidelines, basic methods, and key technical problems are discussed.

First page

17

Last Page

26

Language

Chinese

Publisher

Bulletin of Chinese Academy of Sciences

References

1 Weaver W. Science and complexity. American Scientist, 1948, 36(4): 536-544 .

2 Winsberg E. Computer simulations in science// Zalta E N, Nodelman U, eds. The Stanford Encyclopedia of Philosophy (Winter 2022 Edition). (2019-09-26)[2024-01-07]. https://plato.stanford.edu/archives/win2022/entries/simulations-science.

3 Lynch S M. Using Statistics in Social Research: A Concise Approach. New York: Springer, New York, 2013.

4 Schelling T C. Dynamic models of segregation. The Journal of Mathematical Sociology, 1971, 1(2): 143-186.

5 Kitano H. Nobel Turing Challenge: Creating the engine for scientific discovery. npj Systems Biology and Applications, 2021, 7: 29.

6 Klahr D, Kotovsky K. Complex Information Processing: The Impact of Herbert A. Simon. New Jersey: Lawrence Erlbaum Associates,1989: 375-398.

7 Petitet A, Whaley R C, Dongarra J, et al. HPL—A Portable Implementation of the High-Performance Linpack Benchmark for Distributed-Memory Computers. (2018-12-02)[2024-01-07]. https://netlib.org/benchmark/hpl/.

8 Dongarra J, Heroux M A. Toward a New Metric for Ranking High Performance Computing Systems. East Ave, Livermore: Sandia National Laboratories, 2013.

9 Dirac P A M. Quantum mechanics of many-electron systems. Proceedings of the Royal Society of London Series A, 1929, 123: 714-733.

10 Lavin A, Krakauer D, Zenil H, et al. Simulation intelligence: Towards a new generation of scientific methods. (2021-11-27)[2024-01-07]. https://arxiv.org/abs/2112.03235.

11 张林峰, 李鑫宇, 鄂维南, 等. 科学智能（AI4S）全球发展观察与展望. 北京: 北京科学人工智能研究院, 2023. Zhang L F, Li X Y, E W N, et al. Observation and Outlook on the Global Development of AI for Science (AI4S). Beijing: AI for Science Institute, Beijing, 2023. (in Chinese)

12 Zhang X, Wang L M, Helwig J, et al. Artificial intelligence for science in quantum, atomistic, and continuum systems. (2023-07-17)[2024-01-07]. http://arxiv.org/abs/2307.08423.

13 Voit E O. Perspective: Dimensions of the scientific method. PLoS Computational Biology, 2019, 15(9): e1007279.

14 Kuhn T S, Hawkins D. The structure of scientific revolutions. American Journal of Physics, 1962, 31(7): 554-555.

15 Wah B W, Li G J. A survey on the design of multiprocessing systems for artificial intelligence applications. IEEE Transactions on Systems, Man, and Cybernetics, 1989, 19(4): 667-692.

16 Colella P. Defining Software Requirements for Scientific Computing, 2004, (2012-05-19)[2024-01-07]. https://www.slideserve.com/kiral/software-requirements.

17 Asanović K, Bodík R, Catanzaro B, et al. The landscape of parallel computing research: A view from berkeley. Technical Report No. UCB/EECS-2006-183. Berkeley: EECS Department, University of California, 2006.

18 Zhang L F, Han J Q, Wang H, et al. Deep potential molecular dynamics: A scalable model with the accuracy of quantum mechanics. Physical Review Letters, 2018, 120(14): 143001.

19 Jia W L, Wang H, Chen M H, et al. Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning// SC20: International Conference for High Performance Computing, Networking, Storage and Analysis. Atlanta: IEEE, 2020: 1-14.

20 Shaw D E, Adams P J, Azaria A, et al. Anton 3: Twenty microseconds of molecular dynamics simulation before lunch// SC21: International Conference for High Performance Computing, Networking, Storage and Analysis. St. Louis: IEEE, 2021: 1-11.

21 Guo Z Q, Lu D H, Yan Y J, et al. Extending the limit of molecular dynamics with ab initio accuracy to 10 billion atoms// Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. Seoul: ACM, 2022: 205-218.

Recommended Citation

TAN, Guangming; JIA, Weile; WANG, Zhan; YUAN, Guojun; SHAO, En; and SUN, Ninghui (2024) "Computing system for simulation intelligence," Bulletin of Chinese Academy of Sciences (Chinese Version): Vol. 39 : Iss. 1 , Article 3.
DOI: https://doi.org/10.16418/j.issn.1000-3045.20231201001
Available at: https://bulletinofcas.researchcommons.org/journal/vol39/iss1/3