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Статті в журналах з теми "Computational Science, Engineering"

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Автор: Grafiati

Опубліковано: 20 червня 2021

Оновлено: 9 лютого 2022

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1

Cunningham, Steve, Sylvia Clark Pulliam, Charles D. Swanson, and Peter R. Turner. "Computational science and engineering." ACM SIGCSE Bulletin 34, no. 1 (March 2002): 135–36. http://dx.doi.org/10.1145/563517.563393.

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2

Sameh, A., G. Cybenko, M. Kalos, K. Neves, J. Rice, D. Sorensen, and F. Sullivan. "Computational science and engineering." ACM Computing Surveys 28, no. 4 (December 1996): 810–17. http://dx.doi.org/10.1145/242223.246865.

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3

Wilson, Greg, and Andrew Lumsdaine. "Software Engineering and Computational Science." Computing in Science & Engineering 11, no. 6 (November 2009): 12–13. http://dx.doi.org/10.1109/mcse.2009.206.

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4

Adler, Joan. "Computational Science and Engineering Education." Computing in Science & Engineering 22, no. 4 (July 1, 2020): 4–6. http://dx.doi.org/10.1109/mcse.2020.2998255.

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5

Liew, K. M., L. W. Zhang, J. N. Reddy, and Shaofan Li. "Computational Methods for Engineering Science." Mathematical Problems in Engineering 2015 (2015): 1. http://dx.doi.org/10.1155/2015/842103.

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6

Carver, Jeffrey C. "Software Engineering for Computational Science and Engineering." Computing in Science & Engineering 14, no. 2 (March 2012): 8–11. http://dx.doi.org/10.1109/mcse.2012.31.

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7

Frank, Michael, Dimitris Drikakis, and Vassilis Charissis. "Machine-Learning Methods for Computational Science and Engineering." Computation 8, no. 1 (March 3, 2020): 15. http://dx.doi.org/10.3390/computation8010015.

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The re-kindled fascination in machine learning (ML), observed over the last few decades, has also percolated into natural sciences and engineering. ML algorithms are now used in scientific computing, as well as in data-mining and processing. In this paper, we provide a review of the state-of-the-art in ML for computational science and engineering. We discuss ways of using ML to speed up or improve the quality of simulation techniques such as computational fluid dynamics, molecular dynamics, and structural analysis. We explore the ability of ML to produce computationally efficient surrogate models of physical applications that circumvent the need for the more expensive simulation techniques entirely. We also discuss how ML can be used to process large amounts of data, using as examples many different scientific fields, such as engineering, medicine, astronomy and computing. Finally, we review how ML has been used to create more realistic and responsive virtual reality applications.

8

Carley, Kathleen M. "Computational organizational science and organizational engineering." Simulation Modelling Practice and Theory 10, no. 5-7 (December 2002): 253–69. http://dx.doi.org/10.1016/s1569-190x(02)00119-3.

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9

Barba, Lorena A. "Computational Science and Engineering in 2020." Computing in Science & Engineering 22, no. 6 (November 1, 2020): 5–7. http://dx.doi.org/10.1109/mcse.2020.3027933.

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10

EDUCATION, SIAM WORKING GROUP ON CSE UNDERGRAD, Peter Turner and Linda Petzold, Co-Chairs, Angela Shiflet, Ignatios Vakalis, Kirk Jordan, and Samuel St John. "Undergraduate Computational Science and Engineering Education." SIAM Review 53, no. 3 (January 2011): 561–74. http://dx.doi.org/10.1137/07070406x.

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11

Carusi, Annamaria, Blanca Rodriguez, and Kevin Burrage. "Validation and models in computational biomedical sciences: Philosophy, science, engineering." Progress in Biophysics and Molecular Biology 129 (October 2017): 1–2. http://dx.doi.org/10.1016/j.pbiomolbio.2017.08.005.

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12

Fuoco, Alessio, Giorgio De Luca, Elena Tocci, and Johannes Carolus Jansen. "Computational Approaches in Membrane Science and Engineering." Computation 8, no. 2 (April 23, 2020): 36. http://dx.doi.org/10.3390/computation8020036.

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13

Rice, J. R. "Academic programs in computational science and engineering." IEEE Computational Science and Engineering 1, no. 1 (1994): 13–21. http://dx.doi.org/10.1109/99.295373.

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14

Cybenko, G. "Neural networks in computational science and engineering." IEEE Computational Science and Engineering 3, no. 1 (1996): 36–42. http://dx.doi.org/10.1109/99.486759.

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15

Bungartz, Hans-Joachim, Donald Estep, Ulrich Rüde, and Peter Turner. "Computational Science and Engineering Education: SIAM's Perspective." Computing in Science & Engineering 11, no. 6 (November 2009): 5–11. http://dx.doi.org/10.1109/mcse.2009.189.

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16

Post, Douglass. "The Promise of Science-Based Computational Engineering." Computing in Science & Engineering 11, no. 3 (May 2009): 3–4. http://dx.doi.org/10.1109/mcse.2009.60.

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17

Chen, Yen-Wei, Ikuko Nishikawa, Shinichi Tamura, Bao-Liang Lu, and Huiyan Jiang. "Computational Intelligence in Biomedical Science and Engineering." Computational Intelligence and Neuroscience 2012 (2012): 1–2. http://dx.doi.org/10.1155/2012/160356.

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18

Bouhali, O., I. G. Economou, and F. El-Mellouhi. "The International Computational Science and Engineering Conference." Journal of Computational Science 15 (July 2016): 1–2. http://dx.doi.org/10.1016/j.jocs.2016.02.004.

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19

Johnson, Chris, David Keyes, and Ulrich Ruede. "Special Issue on Computational Science and Engineering." SIAM Journal on Scientific Computing 30, no. 6 (January 2008): vii. http://dx.doi.org/10.1137/sjoce3000030000006000vii000001.

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20

Boman, E. G., D. Bozdag, U. V. Catalyurek, K. D. Devine, A. H. Gebremedhin, P. D. Hovland, and A. Pothen. "Combinatorial algorithms for computational science and engineering." Journal of Physics: Conference Series 125 (July 1, 2008): 012071. http://dx.doi.org/10.1088/1742-6596/125/1/012071.

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21

Education, SIAM Working Group on CSE. "Graduate Education in Computational Science and Engineering." SIAM Review 43, no. 1 (January 2001): 163–77. http://dx.doi.org/10.1137/s0036144500379745.

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22

Yasar, Osman, and Rubin H. Landau. "Elements of Computational Science and Engineering Education." SIAM Review 45, no. 4 (January 2003): 787–805. http://dx.doi.org/10.1137/s0036144502408075.

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23

Carver, Jeffrey C., and Tom Epperly. "Software Engineering for Computational Science and Engineering [Guest editors' introduction]." Computing in Science & Engineering 16, no. 3 (May 2014): 6–9. http://dx.doi.org/10.1109/mcse.2014.72.

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24

Mikhailov, I. F. "Computational approach to social knowledge." Philosophy of Science and Technology 26, no. 2 (2021): 23–37. http://dx.doi.org/10.21146/2413-9084-2021-26-1-23-37.

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Анотація:

Social and cognitive sciences have always faced the choice: either to meet the methodologi- cal standards given by successful natural sciences or to rely on their own. Talking about the conversion of knowledge into technology, the second way did not bring great success. The first way implies two alternative opportunities: reductionism or discovery of proprietary general laws. None of these chances have been realized with any satisfactory results, too. Methodological analysis shows that, to achieve significant progress in social sciences, what is missed there is not new facts or definitions but new conceptual schemes. The reason, as the author supposes, is the nomothetic approach being applied to systems with high degree of complexity and hierarchy. If we assume that social structures and processes are built upon cognitive psychological structures and processes, the former inherit the distributed computational architecture of the latter. The paper analyzes various conceptions of computations in order to determine their relevance to the task of building computational social science. The author offers a “generic” definition of computations as a process carried out by a computational system if the latter is understood as a mechanism of some representation. According to the author, the computationalization of social science implies “naturalization” of computations. This requires a theory that would explain the mechanism of growing complexity and hierarchy of natural (in particular, social) computational systems. As a method for constructing such a science, a kind of reverse engineering is proposed, which is recreation of a computational algorithmic scheme of social tissue by the determination and recombination of “social primitives” – elementary operations of social interaction.

25

Kawazoe, Yoshiyuki, and Masanori Kohyama. "Frontiers of Computational Materials Science and Engineering(1)." Journal of the Japan Institute of Metals 73, no. 8 (2009): 549. http://dx.doi.org/10.2320/jinstmet.73.549.

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26

Vigo-Aguiar, J., and J. A. Lopez-Ramos. "Applications of computational mathematics in science and engineering." International Journal of Computer Mathematics 88, no. 9 (June 2011): 1805–7. http://dx.doi.org/10.1080/00207160.2011.578828.

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27

Vigo-Aguiar, Jesús, and Pedro Alonso. "Computational and mathematical methods in science and engineering." International Journal of Computer Mathematics 89, no. 13-14 (September 2012): 1725–27. http://dx.doi.org/10.1080/00207160.2012.707924.

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28

Marchioro, T. L., and R. H. Landau. "Web-based education in computational science and engineering." IEEE Computational Science and Engineering 4, no. 2 (1997): 19–26. http://dx.doi.org/10.1109/99.609828.

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29

Thiruvathukal, George K. "The Next Generation of Computational Science and Engineering." Computing in Science & Engineering 17, no. 1 (January 2015): 4–5. http://dx.doi.org/10.1109/mcse.2015.26.

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30

Johanson, Arne, and Wilhelm Hasselbring. "Software Engineering for Computational Science: Past, Present, Future." Computing in Science & Engineering 20, no. 2 (March 2018): 90–109. http://dx.doi.org/10.1109/mcse.2018.021651343.

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31

Lu, Jian-Ming, Jyh-Ming Ting, Mo Li, and Eugene Kogan. "Macroscopic/Mesoscopic Computational Materials Science Modeling and Engineering." Mathematical Problems in Engineering 2015 (2015): 1. http://dx.doi.org/10.1155/2015/319626.

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32

Veiga, Luís, Didier El Baz, and João M. P. Cardoso. "Recent advances in computational science and engineering research." Journal of Computational Science 23 (November 2017): 118–19. http://dx.doi.org/10.1016/j.jocs.2017.09.012.

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33

Bouhali, O., I. G. Economou, and F. El-Mellouhi. "Some recent developments on computational science and engineering." Journal of Computational Science 28 (September 2018): 357–59. http://dx.doi.org/10.1016/j.jocs.2018.08.006.

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34

Yagawa, Genki. "Computational Engineering and Science in the Next Decade." Journal of the Society of Mechanical Engineers 98, no. 920 (1995): 540–41. http://dx.doi.org/10.1299/jsmemag.98.920_540.

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35

Turner, Peter R., Angela B. Shiflet, Steve Cunningham, Kris Stewart, Andrew T. Phillips, and Ignatios E. Vakalis. "Undergraduate computational science and engineering programs and courses." ACM SIGCSE Bulletin 34, no. 1 (March 2002): 96–97. http://dx.doi.org/10.1145/563517.563374.

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36

YAMAGUCHI, Takami. "Computational Science and Technology Applied to Biomedical Engineering." Journal of the Society of Mechanical Engineers 107, no. 1026 (2004): 349–52. http://dx.doi.org/10.1299/jsmemag.107.1026_349.

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37

Rüde, Ulrich, Karen Willcox, Lois Curfman McInnes, and Hans De Sterck. "Research and Education in Computational Science and Engineering." SIAM Review 60, no. 3 (January 2018): 707–54. http://dx.doi.org/10.1137/16m1096840.

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38

Bischof, C., K. Brühl, Th Eifert, T. Kuhlen, and D. an Mey. "Computational Engineering & Science an der RWTH Aachen." PIK - Praxis der Informationsverarbeitung und Kommunikation 25, no. 3 (September 2002): 119–27. http://dx.doi.org/10.1515/piko.2002.119.

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39

Yalavarthy, Phaneendra K. "Special Issue on Applied Computational Science and Engineering." Journal of the Indian Institute of Science 97, no. 3 (September 2017): 311. http://dx.doi.org/10.1007/s41745-017-0039-z.

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40

Delen, Dursun, Asil Oztekin, and Aziz Kolkiran. "Intelligent Computational Techniques in Science, Engineering and Business." Expert Systems with Applications 39, no. 1 (January 2012): 1–2. http://dx.doi.org/10.1016/j.eswa.2011.06.039.

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41

YAGAWA, Genki. "Some Remarks on Computational Science and Engineering and Computational Mechanics in Japan." TRENDS IN THE SCIENCES 19, no. 10 (2014): 10_8–10_11. http://dx.doi.org/10.5363/tits.19.10_8.

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42

Carver, Jeffrey C. "Third international workshop on software engineering for computational science and engineering." Procedia Computer Science 1, no. 1 (May 2010): 1485–86. http://dx.doi.org/10.1016/j.procs.2010.04.164.

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43

Carver, Jeffrey C. "First International Workshop on Software Engineering for Computational Science & Engineering." Computing in Science & Engineering 11, no. 2 (March 2009): 7–11. http://dx.doi.org/10.1109/mcse.2009.30.

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44

Liu, Zhen, Teng Yong Ng, and Zishun Liu. "Preface: Advances in computational aerospace materials science and engineering." International Journal of Computational Materials Science and Engineering 07, no. 01n02 (June 2018): 1802001. http://dx.doi.org/10.1142/s2047684118020013.

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Анотація:

In the last two decades, with the rapid development of Chinese Aerospace Engineering, many emerging new technologies and methodologies have been proposed and developed in the aerospace engineering discipline. This special topic issue will offer our valued readers insights into the current development of aerospace engineering related computational aerospace materials science and engineering research now being undertaken in China. These 11 research papers include the latest research into the vibration and strength of aerospace structures, aerodynamics of aerospace shuttles and satellite structures, and aeroacoustic noise of aerospace structures. We trust this series papers will provide an overview of aerospace engineering activities in China, focussing in the most advanced computational techniques and powerful numerical methodologies being developed and employed to advance this field.

45

Tattegrain-Veste, Hέlène, Thierry Bellet, Annie Pauziέ, and Andrέ Chapon. "Computational Driver Model in Transport Engineering: COSMODRIVE." Transportation Research Record: Journal of the Transportation Research Board 1550, no. 1 (January 1996): 1–7. http://dx.doi.org/10.1177/0361198196155000101.

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Анотація:

With regard to road safety issues, a deep understanding of the driver as a logic system is crucial to predict the most probable behavior according to the contextual elements. Knowledge and data about human functional abilities exist. But the problem is to organize and structure them. The development of a computational approach in driver modelization is addressed. In the first part, a brief historical overview is presented of available driver models in ergonomics and psychological areas, and the distinction between predictive and explicative models in an implementation perspective is the focus. In the second part, the computational aspect of the work is described, along with the software concepts, the cognitive modeling needs, and the implementation choices. Object-oriented techniques were chosen because they provide a modular overview of the general system and offer a convenient representation of cognitive processes. Object-oriented formalism, in particular object modeling technique diagrams, acts as a bridge between the two domains of computer science and the human sciences. The objective is to determine whether it is possible to implement reliably a driver model using the techniques from artificial intelligence and based on the theoretical knowledge from cognitive sciences research. This attempt to establish links between different scientific domains, requiring a common tool, is a challenge. A first step of a work that will have to be developed in a long-term time scale, taking into account its quite ambitious objective, is described.

46

Sullivan, F. "Computational science and pathological science." Computing in Science & Engineering 6, no. 3 (May 2004): 2–3. http://dx.doi.org/10.1109/mcise.2004.1289300.

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47

Aguiar, Jesús Vigo, and Raquel Garcia Rubio. "CMMSE: Computational and Mathematical Methods in Science and Engineering." International Journal of Computer Mathematics 97, no. 1-2 (January 4, 2020): 1. http://dx.doi.org/10.1080/00207160.2019.1699990.

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48

Foster, Ian, and Carl Kesselman. "Computational grids: On-demand computing in science and engineering." Computers in Physics 12, no. 2 (1998): 109. http://dx.doi.org/10.1063/1.168654.

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49

Vasile, Massimiliano, Edmondo Minisci, and Ke Tang. "Computational Intelligence in Aerospace Science and Engineering [Guest Editorial]." IEEE Computational Intelligence Magazine 12, no. 4 (November 2017): 12–13. http://dx.doi.org/10.1109/mci.2017.2742866.

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50

Cheah, WaiShiang, Shane Nissom, Jane Labadin, Nurfauza Jali, and HuiKeng Lau. "When Requirement Engineering Meets Computational Science Modelling and Simulation." Advanced Science Letters 23, no. 11 (November 1, 2017): 11288–92. http://dx.doi.org/10.1166/asl.2017.10268.

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