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Journal articles on the topic 'Computational science'

To see the other types of publications on this topic, follow the link: Computational science.

Author: Grafiati

Published: 4 June 2021

Last updated: 10 March 2023

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1

Mikhailov, Igor F. "Computational Knowledge Representation in Cognitive Science." Epistemology & Philosophy of Science 56, no. 3 (2019): 138–52. http://dx.doi.org/10.5840/eps201956355.

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Cognitive research can contribute to the formal epistemological study of knowledge representation inasmuch as, firstly, it may be regarded as a descriptive science of the very same subject as that, of which formal epistemology is a normative one. And, secondly, the notion of representation plays a constitutive role in both disciplines, though differing therein in shades of its meaning. Representation, in my view, makes sense only being paired with computation. A process may be viewed as computational if it adheres to some algorithm and is substrate-independent. Traditionally, psychology is not directly determined by neuroscience, sticking to functional or dynamical analyses in the what-level and skipping mechanistic explanations in the how-level. Therefore, any version of computational approach in psychology is a very promising move in connecting the two scientific realms. On the other hand, the digital and linear computational approach of the classical cognitive science is of little help in this way, as it is not biologically realistic. Thus, what is needed there on the methodological level, is a shift from classical Turing-style computationalism to a generic computational theory that would comprehend the complicated architecture of neuronal computations. To this end, the cutting-edge cognitive neuroscience is in need of а satisfactory mathematical theory applicable to natural, particularly neuronal, computations. Computational systems may be construed as natural or artificial devices that use some physical processes on their lower levels as atomic operations for algorithmic processes on their higher levels. A cognitive system is a multi-level mechanism, in which linguistic, visual and other processors are built on numerous levels of more elementary operations, which ultimately boil down to atomic neural spikes. The hypothesis defended in this paper is that knowledge derives not only from an individual computational device, such as a brain, but also from the social communication system that, in its turn, may be presented as a kind of supercomputer of the parallel network architecture. Therefore, a plausible account of knowledge production and exchange must base on some mathematical theory of social computations, along with that of natural, particularly neuronal, ones.

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Thabet, Senan, and Thabit H. Thabit. "Computational Fluid Dynamics: Science of the Future." International Journal of Research and Engineering 5, no. 6 (2018): 430–33. http://dx.doi.org/10.21276/ijre.2018.5.6.2.

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3

Tan, C. J. Kenneth. "Computational science." Future Generation Computer Systems 18, no. 5 (April 2002): 659. http://dx.doi.org/10.1016/s0167-739x(02)00030-4.

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4

Mikhailov, I. F. "COMPUTATIONAL IMAGE OF SCIENCE." Humanities And Social Studies In The Far East 17, no. 3 (2020): 81–88. http://dx.doi.org/10.31079/1992-2868-2020-17-3-81-88.

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Within a round table at the Institute of Philosophy of the Russian Academy of Sciences in the framework of the seminar ‘Interdisciplinary problems of cognitive sciences’ on December 19, 2019, a discussion was held on the possibility of identifying computational processes in nature, which identification, if successful, could constitute the ontological and methodological basis of cognitive (neuro) sciences. Some participants in the discussion expressed doubts about relevance of such an approach, putting forward the following arguments: computational processes deal with symbols by perforce and form part of human purposeful activity. In this article, I analyze these arguments and show that both symbolism and purposefulness, as the alleged attributes of computations, are inherent in human interpretations of the corresponding processes rather than in the course of their implementation. Here I also consider the ontological and methodological features of the computational approach to scientific knowledge and show how, owing to these features, this approach can help overcome the limitations of the traditional nomological model of science in the study of complex self-organizing systems.

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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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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.

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6

Duke, Dennis W. "Computational Science At the Supercomputer Computations Research Institute." International Journal of Supercomputing Applications 5, no. 3 (September 1991): 4–12. http://dx.doi.org/10.1177/109434209100500302.

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7

Stevenson, D. E. "Science, computational science, and computer science." Communications of the ACM 37, no. 12 (December 1994): 85–96. http://dx.doi.org/10.1145/198366.198386.

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8

Stančić, Hrvoje. "Computational Archival Science." Moderna arhivistika 1, no. 2 (June 1, 2018): 323–30. http://dx.doi.org/10.54356/ma/2018/iyln2017.

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The digitisation of archival materials and ingest of digitally born materials in digital archives has led to the possibilities of application of the big data analytical principles in the digital archives. The author explains the 5V characteristics of big data. He proceeds to define the concept of Computational Archival Science (CAS). Two CAS examples are given in order to illustrate the type of research that can be conducted in that area. Further, the author explains the prerequisites for engaging with CAS. Finally, suggestions on how archival institutions might get involved in CAS activities are given.

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9

Bland-Hawthorn, J. "Recognizing Computational Science." Science 313, no. 5787 (August 4, 2006): 614b—615b. http://dx.doi.org/10.1126/science.313.5787.614b.

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10

Kahl, Gerhard, and Georg Kresse. "Computational materials science." Journal of Physics: Condensed Matter 23, no. 40 (September 19, 2011): 400201. http://dx.doi.org/10.1088/0953-8984/23/40/400201.

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11

Harmon, Bruce. "Computational Materials Science." Journal of Phase Equilibria & Diffusion 25, no. 2 (April 1, 2004): 110. http://dx.doi.org/10.1361/15477020419091.

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12

Wolynes, P. G. "Computational biomolecular science." Proceedings of the National Academy of Sciences 95, no. 11 (May 26, 1998): 5848. http://dx.doi.org/10.1073/pnas.95.11.5848.

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13

Rundle, John, and Geoffrey Fox. "Computational Earthquake Science." Computing in Science & Engineering 14, no. 5 (September 2012): 7–9. http://dx.doi.org/10.1109/mcse.2012.94.

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14

Maxville, Valerie. "Introducing: Computational Science." Procedia Computer Science 18 (2013): 1456–65. http://dx.doi.org/10.1016/j.procs.2013.05.313.

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15

DUAN, W. "Computational materials science." Current Opinion in Solid State and Materials Science 10, no. 1 (February 2006): 1. http://dx.doi.org/10.1016/j.cossms.2006.07.001.

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16

Merali, Zeeya. "Computational science: ...Error." Nature 467, no. 7317 (October 2010): 775–77. http://dx.doi.org/10.1038/467775a.

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17

Cioffi-Revilla, Claudio. "Computational social science." Wiley Interdisciplinary Reviews: Computational Statistics 2, no. 3 (May 2010): 259–71. http://dx.doi.org/10.1002/wics.95.

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18

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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19

Lazer, D., A. Pentland, L. Adamic, S. Aral, A. L. Barabasi, D. Brewer, N. Christakis, et al. "SOCIAL SCIENCE: Computational Social Science." Science 323, no. 5915 (February 6, 2009): 721–23. http://dx.doi.org/10.1126/science.1167742.

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20

Farris, Amy Voss, and Pratim Sengupta. "Democratizing Children's Computation: Learning Computational Science as Aesthetic Experience." Educational Theory 66, no. 1-2 (April 2016): 279–96. http://dx.doi.org/10.1111/edth.12168.

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21

Petrovskii, Sergei, and Natalia Petrovskaya. "Computational ecology as an emerging science." Interface Focus 2, no. 2 (January 5, 2012): 241–54. http://dx.doi.org/10.1098/rsfs.2011.0083.

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It has long been recognized that numerical modelling and computer simulations can be used as a powerful research tool to understand, and sometimes to predict, the tendencies and peculiarities in the dynamics of populations and ecosystems. It has been, however, much less appreciated that the context of modelling and simulations in ecology is essentially different from those that normally exist in other natural sciences. In our paper, we review the computational challenges arising in modern ecology in the spirit of computational mathematics, i.e. with our main focus on the choice and use of adequate numerical methods. Somewhat paradoxically, the complexity of ecological problems does not always require the use of complex computational methods. This paradox, however, can be easily resolved if we recall that application of sophisticated computational methods usually requires clear and unambiguous mathematical problem statement as well as clearly defined benchmark information for model validation. At the same time, many ecological problems still do not have mathematically accurate and unambiguous description, and available field data are often very noisy, and hence it can be hard to understand how the results of computations should be interpreted from the ecological viewpoint. In this scientific context, computational ecology has to deal with a new paradigm: conventional issues of numerical modelling such as convergence and stability become less important than the qualitative analysis that can be provided with the help of computational techniques. We discuss this paradigm by considering computational challenges arising in several specific ecological applications.

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22

Fangohr, Hans, Thomas Kluyver, and Massimo DiPierro. "Jupyter in Computational Science." Computing in Science & Engineering 23, no. 2 (March 1, 2021): 5–6. http://dx.doi.org/10.1109/mcse.2021.3059494.

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23

Ohira, Hideki, Yu Bai, Reiji Suzuki, and Toru Ohira. "Computational science and psychology." Proceedings of the Annual Convention of the Japanese Psychological Association 79 (September 22, 2015): IS—008—IS—008. http://dx.doi.org/10.4992/pacjpa.79.0_is-008.

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24

YUMURA, Takashi. "Computational Surface Science（1）." Journal of the Japan Society of Colour Material 86, no. 11 (2013): 409–17. http://dx.doi.org/10.4011/shikizai.86.409.

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YUMURA, Takashi. "Computational Surface Science（2）." Journal of the Japan Society of Colour Material 86, no. 11 (2013): 418–27. http://dx.doi.org/10.4011/shikizai.86.418.

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26

HONMA, Yoshio. "Communicating Computational Science Online." Journal of Computer Chemistry, Japan 20, no. 2 (2021): A16—A18. http://dx.doi.org/10.2477/jccj.2021-0023.

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27

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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28

Kalos, Melvin. "Challenges in computational science." ACM Computing Surveys 28, no. 4es (December 1996): 22. http://dx.doi.org/10.1145/242224.242251.

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29

Thiruvathukal, G. K. "Xml and computational science." Computing in Science & Engineering 6, no. 1 (January 2004): 74–80. http://dx.doi.org/10.1109/mcise.2004.1255825.

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30

Rundle, J. B. "Computational earth system science." Computing in Science and Engineering 2, no. 3 (May 2000): 20–21. http://dx.doi.org/10.1109/mcise.2000.841792.

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31

Dean, D. J. "Computational Science and Innovation." Journal of Physics: Conference Series 312, no. 6 (September 23, 2011): 062001. http://dx.doi.org/10.1088/1742-6596/312/6/062001.

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32

Thagard, Paul, and Peter Skiff. "Computational Philosophy of Science." Computers in Physics 3, no. 6 (1989): 96. http://dx.doi.org/10.1063/1.4822883.

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33

Foucault Welles, Brooke, and Isabel Meirelles. "Visualizing Computational Social Science." Science Communication 37, no. 1 (November 12, 2014): 34–58. http://dx.doi.org/10.1177/1075547014556540.

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34

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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35

Turk, Matthew. "Fostering Collaborative Computational Science." Computing in Science & Engineering 16, no. 2 (March 2014): 68–71. http://dx.doi.org/10.1109/mcse.2014.37.

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36

Armstrong, Marc P. "Geography and Computational Science." Annals of the Association of American Geographers 90, no. 1 (March 2000): 146–56. http://dx.doi.org/10.1111/0004-5608.00190.

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37

Denning, Peter. "Computational Thinking in Science." American Scientist 105, no. 1 (2017): 13. http://dx.doi.org/10.1511/2017.124.13.

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38

Belleman, Robert G. "Visualization in Computational Science." Procedia Computer Science 1, no. 1 (May 2010): 1689–90. http://dx.doi.org/10.1016/j.procs.2010.04.189.

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39

Winter, Stephan, Xin Chen, and Bo Xu. "Current computational transportation science." GeoInformatica 20, no. 2 (March 15, 2016): 151–57. http://dx.doi.org/10.1007/s10707-016-0249-y.

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40

Babuška, I., F. Nobile, and R. Tempone. "Reliability of computational science." Numerical Methods for Partial Differential Equations 23, no. 4 (2007): 753–84. http://dx.doi.org/10.1002/num.20263.

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41

Parker, Adam. "Computational Algebraic Geometry as a Computational Science Elective." Journal of Computational Science Education 1, no. 1 (December 2010): 2–7. http://dx.doi.org/10.22369/issn.2153-4136/1/1/1.

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42

Boguta, Kovas. "Complexity and the paradigm of Wolfram'sA new kind of science: From the computational sciences to the science of computation." Complexity 10, no. 4 (2005): 15–21. http://dx.doi.org/10.1002/cplx.20066.

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43

Conte, Rosaria, and Francesca Giardini. "Towards Computational and Behavioral Social Science." European Psychologist 21, no. 2 (April 2016): 131–40. http://dx.doi.org/10.1027/1016-9040/a000257.

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Abstract. In the last few years, the study of social phenomena has hosted a renewal of interest in Computational Social Science (CSS). While this field is not new – Axelrod’s first computational work on the evolution of cooperation goes back to 1981 – CSS has recently resurged under the pressure of quantitative social science and the application of Big Data analytics to social datasets. However, Big Data is no panacea and the data deluge that it provides raises more questions than it answers. The aim of this paper is to present an overview in which CSS will be introduced and the costs of CSS will be balanced against its benefits, in an attempt to propose an integrative view of the new and the old practice of CSS. In particular, two routes to integration will be drawn. First, it will be advocated that social data mining and computational modeling need to be integrated. Second, we will introduce the generative approach, aimed to understand how social phenomena can be generated starting from the micro-components, including psychological mechanisms, and we will discuss the necessity of combining it with the anticipatory, data-driven objective. By these means, Computational Social Science will develop into a more comprehensive field of Computational Social and Behavioral Science in which data science, ICT, as well as the behavioral and social sciences will be fruitfully integrated.

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44

Cook, Gregory B., and Saul A. Teukolsky. "Numerical relativity: challenges for computational science." Acta Numerica 8 (January 1999): 1–45. http://dx.doi.org/10.1017/s0962492900002889.

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We describe the burgeoning field of numerical relativity, which aims to solve Einstein's equations of general relativity numerically. The field presents many questions that may interest numerical analysts, especially problems related to nonlinear partial differential equations: elliptic systems, hyperbolic systems, and mixed systems. There are many novel features, such as dealing with boundaries when black holes are excised from the computational domain, or how even to pose the problem computationally when the coordinates must be determined during the evolution from initial data. The most important unsolved problem is that there is no known general 3-dimensional algorithm that can evolve Einstein's equations with black holes that is stable. This review is meant to be an introduction that will enable numerical analysts and other computational scientists to enter the field. No previous knowledge of special or general relativity is assumed.

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45

von Arnim, Albrecht G., and Anamika Missra. "Graduate Training at the Interface of Computational and Experimental Biology: An Outcome Report from a Partnership of Volunteers between a University and a National Laboratory." CBE—Life Sciences Education 16, no. 4 (December 2017): ar61. http://dx.doi.org/10.1187/cbe.17-02-0038.

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Leading voices in the biological sciences have called for a transformation in graduate education leading to the PhD degree. One area commonly singled out for growth and innovation is cross-training in computational science. In 1998, the University of Tennessee (UT) founded an intercollegiate graduate program called the UT-ORNL Graduate School of Genome Science and Technology in partnership with the nearby Oak Ridge National Laboratory. Here, we report outcome data that attest to the program’s effectiveness in graduating computationally enabled biologists for diverse careers. Among 77 PhD graduates since 2003, the majority came with traditional degrees in the biological sciences, yet two-thirds moved into computational or hybrid (computational–experimental) positions. We describe the curriculum of the program and how it has changed. We also summarize how the program seeks to establish cohesion between computational and experimental biologists. This type of program can respond flexibly and dynamically to unmet training needs. In conclusion, this study from a flagship, state-supported university may serve as a reference point for creating a stable, degree-granting, interdepartmental graduate program in computational biology and allied areas.

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46

Hariprasad, M. "A GCD problem and a Hessenberg determinant." Notes on Number Theory and Discrete Mathematics 24, no. 2 (June 2018): 28–31. http://dx.doi.org/10.7546/nntdm.2018.24.2.28-31.

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47

Sejnowski, T., C. Koch, and P. Churchland. "Computational neuroscience." Science 241, no. 4871 (September 9, 1988): 1299–306. http://dx.doi.org/10.1126/science.3045969.

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48

Eberhart, M. "Computational Metallurgy." Science 265, no. 5170 (July 15, 1994): 332–33. http://dx.doi.org/10.1126/science.265.5170.332.

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49

Cramer, C. J. "COMPUTATIONAL ANALYSIS:Y2K." Science 286, no. 5448 (December 17, 1999): 2281. http://dx.doi.org/10.1126/science.286.5448.2281.

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50

Wallach, Hanna. "Computational social science ≠ computer science + social data." Communications of the ACM 61, no. 3 (February 21, 2018): 42–44. http://dx.doi.org/10.1145/3132698.

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