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Journal articles on the topic 'Computational physics'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 October 2024

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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 concerns the modelling of inviscid incompressible cavity flow using the potential flow. The numerical methods employed the finite differences approach. The time and space discretization is achieved using second-order schemes. The studies reveal that the finite differences approach is a computationally efficient approach and large computations can be performed on a single computer. The analysis of the flow physics reveals the presence of the recirculation region inside the cavity as well at the corners of the cavity

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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 (January 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 (June 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 (March 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 (July 1, 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 (October 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 (March 9, 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 problem, and in particular why computational externalism, are dualistic in the sense that they imply that nonphysical facts in the environment of a computing system single out the computation. We discuss certain attempts to dissolve Putnam’s unrestricted result by appealing to systems with certain kinds of input and output states as a special case of computational externalism, and show why this approach is not workable without collapsing to behaviorism. We conclude with some remarks about the nonphysical nature of mainstream approaches to both statistical mechanics and the quantum theory of measurement with respect to the singling out of partitions and observables.

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10

Nardelli, Marco Buongiorno. "Computation “is” Physics!: Computational Physics: Nicholas J. Giordano and Hisao Nakanishi." Physics Teacher 44, no. 7 (October 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 (January 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 for tools, languages, and environments. We conclude with some emerging topics for which only preliminary resources exist but represent important topics for future innovation.

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12

Singh, Gurdev. "Computational Physics: Role in interdisciplinary Research." International Journal of Science and Research (IJSR) 13, no. 6 (June 5, 2024): 1040–41. http://dx.doi.org/10.21275/sr24613103051.

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13

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

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14

Irvine, J. M. "Computational nuclear physics." Reports on Progress in Physics 51, no. 9 (September 1, 1988): 1181–204. http://dx.doi.org/10.1088/0034-4885/51/9/001.

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15

Gururajan, M. P. "Applied computational physics." Contemporary Physics 59, no. 4 (October 2, 2018): 419–20. http://dx.doi.org/10.1080/00107514.2018.1531936.

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16

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

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17

Myridis, Nikolaos E. "Physical perspectives on computation, computational perspectives on physics." Contemporary Physics 60, no. 1 (January 2, 2019): 100–101. http://dx.doi.org/10.1080/00107514.2019.1608310.

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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 (July 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 (December 23, 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 (October 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 (April 23, 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 (April 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 (December 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 (July 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 (January 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 (August 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 (August 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 (May 7, 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 research is students who take Computational Physics courses in January-June 2021. The data analysis technique used is the multivariate analysis based on factor loading testing with Confirmatory Factor Analysis (CFA) using Lisrel 8.80. The results showed that students' attitudes toward Computational Physics were good, with a percentage of student responses of 67.16%. Factors that influence learning difficulties are internal factors in the form of psychological factors in the aspect of interest (65%), motivational aspects (58%), and aspects of study habits (49%). Meanwhile, external factors do not affect students' difficulties in studying Computational Physics. Further research, it is necessary to carry out a similar analysis by taking into account other factors that are thought to influence the attitudes and difficulties of students in studying Computational Physics, both internal and external factors, so that they are better in determining the next steps to overcome student difficulties in studying Computational Physics.

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32

MACLENNAN, BRUCE J. "EMBODIED COMPUTATION: APPLYING THE PHYSICS OF COMPUTATION TO ARTIFICIAL MORPHOGENESIS." Parallel Processing Letters 22, no. 03 (July 8, 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, which act to create tissues with specific patterns, compositions, and forms. We outline a notation for describing morphogenetic programs and illustrate its use with two examples: simple diffusion and the assembly of a simple spine with attachment points for legs. While much research remains to be done — at the simulation level before we attempt physical implementations — our results to date show how we may implement the fundamental processes of morphogenesis as a practical application of embodied computation at the nano- and microscale.

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33

Seoane, Luís F. "Fate of Duplicated Neural Structures." Entropy 22, no. 9 (August 25, 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 would tell what kinds of brains to expect under set energetic, evolutionary, and computational conditions. With this big picture in mind, we focus on the fate of duplicated neural circuits. We look at examples from central nervous systems, with stress on computational thresholds that might prompt this redundancy. We also study a naive cost-benefit balance for duplicated circuits implementing complex phenotypes. From this, we derive phase diagrams and (phase-like) transitions between single and duplicated circuits, which constrain evolutionary paths to complex cognition. Back to the big picture, similar phase diagrams and transitions might constrain I/O and internal connectivity patterns of neural circuits at large. The formalism of statistical physics seems to be a natural framework for this worthy line of research.

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34

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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35

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

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36

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

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37

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

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38

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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39

SUZUKI, M. "MATHEMATICAL BASIS OF COMPUTATIONAL PHYSICS." International Journal of Modern Physics C 07, no. 03 (June 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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40

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

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41

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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42

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

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43

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

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44

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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45

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

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46

Dvorkin, Jack, Naum Derzhi, Elizabeth Diaz, and Qian Fang. "Relevance of computational rock physics." GEOPHYSICS 76, no. 5 (September 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 situation, instead of directly comparing data points generated by different methods of measurement, we compare trends formed by such data points. These trends include permeability versus porosity; electrical formation factor versus porosity; and elastic moduli (elastic-wave velocity) versus porosity. In the physical laboratory, these trends are generated by measuring a significant number of samples. In contrast, in the computational laboratory, these trends are often hidden inside a very small digital sample and can be derived by subsampling it. Hence, we base our validation paradigm on the assumption that if these computational trends match relevant physical trends and/or theoretical rock physics transforms, the computational results are correct. We present examples of such validation for clastic and carbonate samples, including drill cuttings.

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47

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

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48

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

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49

Stannett, Mike. "The computational status of physics." Natural Computing 8, no. 3 (February 28, 2009): 517–38. http://dx.doi.org/10.1007/s11047-009-9115-2.

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50

Nadrchal, J. "Software engineering and computational physics." Computer Physics Communications 41, no. 2-3 (August 1986): 197–203. http://dx.doi.org/10.1016/0010-4655(86)90064-0.

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