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Journal articles on the topic 'Computational physics'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

ILIE, Marcel, Augustin Semenescu, Gabriela Liliana STROE, and Sorin BERBENTE. "NUMERICAL COMPUTATIONS OF THE CAVITY FLOWS USING THE POTENTIAL FLOW THEORY." ANNALS OF THE ACADEMY OF ROMANIAN SCIENTISTS Series on ENGINEERING SCIENCES 13, no. 2 (2021): 78–86. http://dx.doi.org/10.56082/annalsarscieng.2021.2.78.

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Computational fluid dynamics of turbulent flows requires large computational resources or are not suitable for the computations of transient flows. Therefore methods such as Reynolds-averaged Navier-Stokes equations are not suitable for the computation of transient flows. The direct numerical simulation provides the most accurate solution, but it is not suitable for high-Reynolds number flows. Large-eddy simulation (LES) approach is computationally less demanding than the DNS but still computationally expensive. Therefore, alternative computational methods must be sought. This research concern

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2

Giordano, Nicholas J., Marvin L. De Jong, Susan R. McKay, and Wolfgang Christian. "Computational Physics." Computers in Physics 11, no. 4 (1997): 351. http://dx.doi.org/10.1063/1.4822569.

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3

Gustafson, Karl. "Computational Physics." Computers in Physics 5, no. 5 (1991): 457. http://dx.doi.org/10.1063/1.4823010.

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4

Landau, Rubin H., Manuel Páez, Harvey Gould, and Jan Tobochnik. "Computational Physics." American Journal of Physics 67, no. 1 (1999): 94–95. http://dx.doi.org/10.1119/1.19197.

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5

Koonin, Steven E., and Peter B. Kramer. "Computational Physics." Physics Today 39, no. 6 (1986): 88–90. http://dx.doi.org/10.1063/1.2815046.

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6

Thijssen, J. M., and Alan F. Wright. "Computational Physics." Physics Today 53, no. 3 (2000): 76–77. http://dx.doi.org/10.1063/1.883008.

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7

Borcherds, P. H. "Computational physics." Physics Education 21, no. 4 (1986): 238–43. http://dx.doi.org/10.1088/0031-9120/21/4/008.

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8

Giordano, Nicholas J., Tao Pang, and John M. Blondin. "Computational Physics and an Introduction to Computational Physics." Physics Today 51, no. 10 (1998): 84–86. http://dx.doi.org/10.1063/1.882417.

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9

Hemmo, Meir, and Orly Shenker. "The Multiple-Computations Theorem and the Physics of Singling Out a Computation." Monist 105, no. 2 (2022): 175–93. http://dx.doi.org/10.1093/monist/onab030.

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Abstract The problem of multiple-computations discovered by Hilary Putnam presents a deep difficulty for functionalism (of all sorts, computational and causal). We describe in outline why Putnam’s result, and likewise the more restricted result we call the Multiple-Computations Theorem, are in fact theorems of statistical mechanics. We show why the mere interaction of a computing system with its environment cannot single out a computation as the preferred one amongst the many computations implemented by the system. We explain why nonreductive approaches to solving the multiple-computations pro

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10

Nardelli, Marco Buongiorno. "Computation “is” Physics!: Computational Physics: Nicholas J. Giordano and Hisao Nakanishi." Physics Teacher 44, no. 7 (2006): 480. http://dx.doi.org/10.1119/1.2353604.

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11

Atherton, Timothy J. "Resource Letter CP-3: Computational physics." American Journal of Physics 91, no. 1 (2023): 7–27. http://dx.doi.org/10.1119/5.0106476.

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This Resource Letter provides information and guidance for those looking to incorporate computation into their courses or to refine their own computational practice. We begin with general resources, including policy documents and supportive organizations. We then survey efforts to integrate computation across the curriculum as well as provide information for instructors looking to teach a computational physics course specifically. An overview of education research into computation in physics, including materials from beyond Physics Education Research, is then provided, followed by suggestions

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12

Rieger, Heiko. "Computational Statistical Physics." Europhysics News 53, no. 3 (2022): 32. http://dx.doi.org/10.1051/epn/2022306.

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13

Irvine, J. M. "Computational nuclear physics." Reports on Progress in Physics 51, no. 9 (1988): 1181–204. http://dx.doi.org/10.1088/0034-4885/51/9/001.

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14

Gururajan, M. P. "Applied computational physics." Contemporary Physics 59, no. 4 (2018): 419–20. http://dx.doi.org/10.1080/00107514.2018.1531936.

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15

Koren, Barry, Ute Ebert, Tamas Gombosi, Hervé Guillard, Rony Keppens, and Dana Knoll. "Computational plasma physics." Journal of Computational Physics 231, no. 3 (2012): 717. http://dx.doi.org/10.1016/j.jcp.2011.11.012.

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16

Myridis, Nikolaos E. "Physical perspectives on computation, computational perspectives on physics." Contemporary Physics 60, no. 1 (2019): 100–101. http://dx.doi.org/10.1080/00107514.2019.1608310.

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17

Singh, Gurdev. "Computational Physics: Role in interdisciplinary Research." International Journal of Science and Research (IJSR) 13, no. 6 (2024): 1040–41. http://dx.doi.org/10.21275/sr24613103051.

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18

Dean, Edward J., Steven E. Koonin, and Dawn C. Meredith. "Computational Physics--Fortran Version." Mathematics of Computation 59, no. 199 (1992): 305. http://dx.doi.org/10.2307/2153006.

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19

Sadiku, Matthew N. O., Adebowale E. Shadare, and Sarhan M. Musa. "Computational Physics: An Introduction." International Journal of Engineering Research 6, no. 9 (2017): 427. http://dx.doi.org/10.5958/2319-6890.2017.00054.x.

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20

Vesely, Franz J., Forest Davenport, Susan McKay, and Wolfgang Christian. "Computational Physics: An Introduction." Computers in Physics 10, no. 1 (1996): 47. http://dx.doi.org/10.1063/1.4822354.

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21

Bridson, R., and C. Batty. "Computational Physics in Film." Science 330, no. 6012 (2010): 1756–57. http://dx.doi.org/10.1126/science.1198769.

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22

Koonin, Steven E., Dawn C. Meredith, and William H. Press. "Computational Physics: Fortran Version." Physics Today 44, no. 10 (1991): 112–13. http://dx.doi.org/10.1063/1.2810288.

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23

Stoneham, A. M. "Computational physics: a perspective." Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences 360, no. 1795 (2002): 1107–21. http://dx.doi.org/10.1098/rsta.2002.0985.

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24

Hoover, Wm G., and Carol G. Hoover. "Computational physics with particles." American Journal of Physics 76, no. 4 (2008): 481–92. http://dx.doi.org/10.1119/1.2830538.

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25

Tobochnik, Jan, and Harvey Gould. "New Computational Physics Section." American Journal of Physics 80, no. 12 (2012): 1041. http://dx.doi.org/10.1119/1.4754019.

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26

Smit, B. "Computational Physics in Industry." Europhysics News 27, no. 5 (1996): 189–91. http://dx.doi.org/10.1051/epn/19962705189.

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27

Ayars, Eric. "Computational Physics (second edition)." American Journal of Physics 74, no. 7 (2006): 652–53. http://dx.doi.org/10.1119/1.2203648.

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28

Borcherds, Peter. "Computational Techniques in Physics." Physics Bulletin 39, no. 1 (1988): 29. http://dx.doi.org/10.1088/0031-9112/39/1/029.

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29

Godwal, B. K. "Computational condensed matter physics." Bulletin of Materials Science 22, no. 5 (1999): 877–84. http://dx.doi.org/10.1007/bf02745548.

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30

Lister, G. G. "Computational techniques in physics." Computer Physics Communications 50, no. 3 (1988): 414. http://dx.doi.org/10.1016/0010-4655(88)90195-6.

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31

Indriani, Revi, Akmam Akmam, Fatni Mufit, Rahmat Hidayat, and Silvi Yulia Sari. "Analysis of Students' Attitudes and Difficulties in Studying Computational Physics." Berkala Ilmiah Pendidikan Fisika 10, no. 1 (2022): 34. http://dx.doi.org/10.20527/bipf.v10i1.12408.

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Ideally, students who study Computational Physics are required to think computationally. However, student learning outcomes tend to be low. Low learning outcomes are suspected by students having difficulties. One of the causes of learning difficulties is students' attitude in responding to learning. This study aims to determine student attitudes in studying Computational Physics and the factors influencing student learning difficulties. This research is descriptive research with a quantitative approach. The population in this study were students of Physics FMIPA UNP. The sample in this researc

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32

MACLENNAN, BRUCE J. "EMBODIED COMPUTATION: APPLYING THE PHYSICS OF COMPUTATION TO ARTIFICIAL MORPHOGENESIS." Parallel Processing Letters 22, no. 03 (2012): 1240013. http://dx.doi.org/10.1142/s0129626412400130.

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We discuss the problem of assembling complex physical systems that are structured from the nanoscale up through the macroscale, and argue that embryological morphogenesis provides a good model of how this can be accomplished. Morphogenesis (whether natural or artificial) is an example of embodied computation, which exploits physical processes for computational ends, or performs computations for their physical effects. Examples of embodied computation in natural morphogenesis can be found at many levels, from allosteric proteins, which perform simple embodied computations, up through cells, whi

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33

Shearin, Rhonda. "Physics Computing '94 Explores Innovations in Computational Physics." Computers in Physics 8, no. 3 (1994): 241. http://dx.doi.org/10.1063/1.4823292.

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34

Landau, R. "Computational Physics: A Better Model for Physics Education?" Computing in Science & Engineering 8, no. 5 (2006): 22–30. http://dx.doi.org/10.1109/mcse.2006.85.

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35

Kouh, Minjoon. "Free Software Resources for Teaching AC RC Circuits." Physics Teacher 60, no. 4 (2022): 266–69. http://dx.doi.org/10.1119/5.0038131.

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The importance of introducing computational approaches early and actively in science education is widely acknowledged among educators and scientists. Many great ideas have been put forward and implemented by advocates and organizations such as Partnership for Integration of Computation into Undergraduate Physics ( https://www.compadre.org/PICUP ) and the Science Education Resource Center at Carleton College ( https://serc.carleton.edu/teaching_computation/index.html ). According to “Phys21: Preparing Physics Students for 21st-Century Careers,” a recent report by the Joint Task Force on Undergr

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36

Seoane, Luís F. "Fate of Duplicated Neural Structures." Entropy 22, no. 9 (2020): 928. http://dx.doi.org/10.3390/e22090928.

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Statistical physics determines the abundance of different arrangements of matter depending on cost-benefit balances. Its formalism and phenomenology percolate throughout biological processes and set limits to effective computation. Under specific conditions, self-replicating and computationally complex patterns become favored, yielding life, cognition, and Darwinian evolution. Neurons and neural circuits sit at a crossroads between statistical physics, computation, and (through their role in cognition) natural selection. Can we establish a statistical physics of neural circuits? Such theory wo

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37

Takabe, Hideaki, and Luca Baiotti. "Conference on Computational Physics 2012." Asia Pacific Physics Newsletter 02, no. 01 (2013): 12–13. http://dx.doi.org/10.1142/s2251158x13000040.

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38

Orban, C. M., and R. M. Teeling-Smith. "Computational Thinking in Introductory Physics." Physics Teacher 58, no. 4 (2020): 247–51. http://dx.doi.org/10.1119/1.5145470.

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39

Raj, K. M. Kiran, Srivatsa Maddodi, and U. Yogish Pai. "Computational Physics Methods and Algorithms." Journal of Physics: Conference Series 1712 (December 2020): 012028. http://dx.doi.org/10.1088/1742-6596/1712/1/012028.

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40

SUZUKI, M. "MATHEMATICAL BASIS OF COMPUTATIONAL PHYSICS." International Journal of Modern Physics C 07, no. 03 (1996): 355–59. http://dx.doi.org/10.1142/s0129183196000296.

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The present paper explains some general basic formulas concerning quantum Monte Carlo simulations, symplectic integration and other numerical calculations. A generalization of the BCH formula is given with an application to the decomposition of exponential operators in the presence of small parameters.

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41

Pang, Tao, Harvey Gould, and Jan Tobochnik. "An Introduction to Computational Physics." American Journal of Physics 67, no. 1 (1999): 94–95. http://dx.doi.org/10.1119/1.19198.

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42

Smit, Berend. "Computational physics in petrochemical industry." Physica Scripta T66 (January 1, 1996): 80–84. http://dx.doi.org/10.1088/0031-8949/1996/t66/010.

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43

Kadanoff, Leo P. "Computational Physics: Pluses and Minuses." Physics Today 39, no. 7 (1986): 7–9. http://dx.doi.org/10.1063/1.2815070.

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44

Hedstrom, Gerald. "Computational Physics Talks: Tell Techniques." Physics Today 46, no. 12 (1993): 68. http://dx.doi.org/10.1063/1.2809142.

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45

Backer, Arnd. "Computational Physics Education with Python." Computing in Science & Engineering 9, no. 3 (2007): 30–33. http://dx.doi.org/10.1109/mcse.2007.48.

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46

DeTar, DeLosF. "Methods in computational molecular physics." Computers & Chemistry 9, no. 1 (1985): 78. http://dx.doi.org/10.1016/0097-8485(85)80023-1.

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47

Dvorkin, Jack, Naum Derzhi, Elizabeth Diaz, and Qian Fang. "Relevance of computational rock physics." GEOPHYSICS 76, no. 5 (2011): E141—E153. http://dx.doi.org/10.1190/geo2010-0352.1.

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To validate the transport (fluid and electrical) and elastic properties computed on CT scan pore-scale volumes of natural rock, we first contrast these values to physical laboratory measurements. We find that computational and physical data obtained on the same rock material source often differ from each other. This mismatch, however, does not preclude the validity of either of the data type — it only implies that expecting a direct match between the effective properties of two volumes of very different sizes taken from the same heterogeneous material is generally incorrect. To address this si

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48

Attig, Norbert. "Computational physics with PetaFlops computers." Computer Physics Communications 180, no. 4 (2009): 555–58. http://dx.doi.org/10.1016/j.cpc.2008.12.032.

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49

Duch, Włodzisław. "Computational physics of the mind." Computer Physics Communications 97, no. 1-2 (1996): 136–53. http://dx.doi.org/10.1016/0010-4655(96)00027-6.

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50

Stannett, Mike. "The computational status of physics." Natural Computing 8, no. 3 (2009): 517–38. http://dx.doi.org/10.1007/s11047-009-9115-2.

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