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Journal articles on the topic 'Computational methods'

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

Author: Grafiati

Published: 4 June 2021

Last updated: 25 May 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

Maeda, H. "Computational Methods." Journal of Offshore Mechanics and Arctic Engineering 115, no. 1 (February 1, 1993): 7–8. http://dx.doi.org/10.1115/1.2920095.

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3

Sprevak, Mark. "Not All Computational Methods Are Effective Methods." Philosophies 7, no. 5 (October 10, 2022): 113. http://dx.doi.org/10.3390/philosophies7050113.

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An effective method is a computational method that might, in principle, be executed by a human. In this paper, I argue that there are methods for computing that are not effective methods. The examples I consider are taken primarily from quantum computing, but these are only meant to be illustrative of a much wider class. Quantum inference and quantum parallelism involve steps that might be implemented in multiple physical systems, but cannot be implemented, or at least not at will, by an idealised human. Recognising that not all computational methods are effective methods is important for at least two reasons. First, it is needed to correctly state the results of Turing and other founders of computation theory. Turing is sometimes said to have offered a replacement for the informal notion of an effective method with the formal notion of a Turing machine. I argue that such a view only holds under limited circumstances. Second, not distinguishing between computational methods and effective methods can lead to mistakes when quantifying over the class of all possible computational methods. Such quantification is common in philosophy of mind in the context of thought experiments that explore the limits of computational functionalism. I argue that these ‘homuncular’ thought experiments should not be treated as valid.

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4

Dutra, Herica Silva, Edinêis de Brito Guirardello, Yin Li, and Jeannie P. Cimiotti. "Nurse Burnout Revisited: A Comparison of Computational Methods." Journal of Nursing Measurement 27, no. 1 (April 1, 2019): E17—E33. http://dx.doi.org/10.1891/1061-3749.27.1.e17.

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Background and PurposeTo examine computational measures of job-related burnout to determine the best computation to estimate job satisfaction and intent to leave in Brazilian nursing professionals.MethodsMaslach Burnout Inventory-Human Services Survey (MBI-HSS) was used assess burnout in 452 hospital-based nursing professionals. Adjusted logistic regression models were fit using different computations of burnout to estimate outcomes of interest.ResultsTotal mean score of burnout subscales was the best estimate of job satisfaction (Cox-Snell R2 = 0.312; Nagelkerke R2 = 0.450) and intent to leave (Cox-Snell R2 = 0.156; Nagelkerke R2 = 0.300), as was high emotional exhaustion (Cox-Snell R2 = 0.219; Nagelkerke R2 = 0.316).ConclusionWe have provided evidence that different computations of data from the Portuguese (Brazil) MBI-HSS can be used in to estimate the effect of job-related burnout on nurse outcomes.

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5

Carstensen, Carsten, Björn Engquist, and Daniel Peterseim. "Computational Multiscale Methods." Oberwolfach Reports 11, no. 2 (2014): 1625–81. http://dx.doi.org/10.4171/owr/2014/30.

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6

Engquist, Björn, and Daniel Peterseim. "Computational Multiscale Methods." Oberwolfach Reports 16, no. 3 (September 9, 2020): 2099–181. http://dx.doi.org/10.4171/owr/2019/35.

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7

&NA;. "ENGINEERING/COMPUTATIONAL METHODS." ASAIO Journal 42, no. 2 (April 1996): 56–57. http://dx.doi.org/10.1097/00002480-199642020-00011.

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8

Cross, M. "Computational Galerkin methods." Applied Mathematical Modelling 9, no. 3 (June 1985): 226. http://dx.doi.org/10.1016/0307-904x(85)90012-5.

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9

Nakazawa, Shohei. "Computational Galerkin methods." Computer Methods in Applied Mechanics and Engineering 50, no. 2 (August 1985): 199–200. http://dx.doi.org/10.1016/0045-7825(85)90091-x.

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10

Kourou, K., and DI Fotiadis. "Computational Modelling in Cancer: Methods and Applications." Biomedical Data Journal 01, no. 1 (January 2015): 15–25. http://dx.doi.org/10.11610/bmdj.01103.

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11

Liu, G. R. "Computational methods for certified solutions, adaptive analysis, real-time computation, and inverse analysis of mechanics problem." Proceedings of The Computational Mechanics Conference 2011.24 (2011): _—1_—_—5_. http://dx.doi.org/10.1299/jsmecmd.2011.24._-1_.

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12

Rubinacci, G., A. Tamburrino, S. Ventre, and F. Villone. "Fast computational methods for large-scale eddy-current computation." IEEE Transactions on Magnetics 38, no. 2 (March 2002): 529–32. http://dx.doi.org/10.1109/20.996139.

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13

Joseph, Prince. "Comparative Analysis of Computational Based Motif Refinement Methods." International Journal of Science and Research (IJSR) 12, no. 10 (October 5, 2023): 163–66. http://dx.doi.org/10.21275/sr231002163600.

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14

Rabczuk, Timon, Stéphane P. A. Bordas, and Goangseup Zi. "Computational Methods for Fracture." Mathematical Problems in Engineering 2014 (2014): 1–2. http://dx.doi.org/10.1155/2014/593041.

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15

Im, Chang-Hwan, Lei Ding, Yiwen Wang, and Sung-Phil Kim. "Computational Methods in Neuroengineering." Computational and Mathematical Methods in Medicine 2013 (2013): 1–2. http://dx.doi.org/10.1155/2013/617347.

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16

Goes, Saskia. "Computational methods for geodynamics." Geophysical Journal International 184, no. 2 (December 22, 2010): 974. http://dx.doi.org/10.1111/j.1365-246x.2010.04898.x.

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17

BANKES, STEVEN C., and DANIEL MARGOLIASH. "Methods in Computational Neurobiology." American Zoologist 33, no. 1 (February 1993): 8–15. http://dx.doi.org/10.1093/icb/33.1.8.

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18

Destexhe, Alain, and Vincenzo Crunelli. "Methods for computational neuroscience." Journal of Neuroscience Methods 169, no. 2 (April 2008): 269–70. http://dx.doi.org/10.1016/j.jneumeth.2008.01.025.

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19

Marin, Jean-Michel, Pierre Pudlo, Christian P. Robert, and Robin J. Ryder. "Approximate Bayesian computational methods." Statistics and Computing 22, no. 6 (October 21, 2011): 1167–80. http://dx.doi.org/10.1007/s11222-011-9288-2.

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20

Thiel, Walter, and Gerhard Hummer. "Methods for computational chemistry." Nature 504, no. 7478 (December 2013): 96–97. http://dx.doi.org/10.1038/504096a.

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21

Chillali, Abdelhakim, and Nacer E. El Kadri E. "Computational methods in aerodynamics." International Journal of Computer Aided Engineering and Technology 18, no. 1/2/3 (2023): 110. http://dx.doi.org/10.1504/ijcaet.2023.10052477.

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22

E., Nacer El Kadri, and Abdelhakim Chillali. "Computational methods in aerodynamics." International Journal of Computer Aided Engineering and Technology 18, no. 1/2/3 (2023): 110. http://dx.doi.org/10.1504/ijcaet.2023.127790.

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23

Song, Daegene. "Computational Methods in Correlated Network Manipulation and Quantum Realism." NeuroQuantology 20, no. 5 (May 10, 2022): 869–77. http://dx.doi.org/10.14704/nq.2022.20.5.nq22292.

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The realization of long-distance entanglement has been important in many quantum technologies, including quantum cryptography. In particular, a swapping protocol has been introduced for connecting multiple short networks to create a long-distance entangled pair. Building on previous work, this paper attempts to provide a numerical analysis of entanglement swapping of two 4-level states. While it is not always possible to have a weaker link between the two states (i.e., the optimal case), a substantial number of states satisfy the best scenario. Moreover, various numerical outcomes are shown to yield near-optimal results when a swapping protocol is applied. Recently, the subjective nature of physical reality has been discussed from the perspective of quantum theory. It is suggested that, similar to the energy and mass relation in relativity, an equivalence between mass and meaning in the mind may be derived. That is, the experience of mass may be different for observers with different mental knowledge.

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24

Kristensen, Leif Kahl. "Initial linking methods and their classification." Proceedings of the International Astronomical Union 2, S236 (August 2006): 301–8. http://dx.doi.org/10.1017/s1743921307003365.

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AbstractThe problem of initial linking of asteroids is of increasing interest for the next generation surveys. During the first week after discovery elliptical elements are very uncertain and other methods are used. A summary is given of 7 initial linking methods. There are two different types: In one, a search area is computed on a second night from the known and undoubtedly linked positions, typically on the first night. The other type assumes candidates which are then checked by the computation of O – C residuals of an orbit. Computations may be classified as belonging to the 3-dimensional space or the 2-dimensional sky-plane. A new basis, with a simpler computational algorithm, is given for the widely used Väisälä method. For a new N-Observation Orbit method a simple, efficient PC-programme is given.

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25

Ma, Xiaoyue. "Development of Computational Chemistry and Application of Computational Methods." Journal of Physics: Conference Series 2386, no. 1 (December 1, 2022): 012005. http://dx.doi.org/10.1088/1742-6596/2386/1/012005.

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Abstract This paper discussed the development of computational chemistry, some representative computational chemistry methods and some machine learning algorithms applied to computational chemistry. Computational chemistry has grown considerably since the 1920s and is rapidly becoming an important and emerging sub-discipline in the whole field of chemistry. In recent years, the introduction of machine learning algorithms such as support vector machines and deep learning has made computational chemistry processes more accurate and efficient. The application of computational chemistry in areas such as conformational prediction has brought about a revolutionary change in chemical research.

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26

Rubanov, I. V., and M. Y. Kovalyov. "Computational methods for airspace sectorisation." Informatics 17, no. 4 (January 3, 2021): 7–21. http://dx.doi.org/10.37661/1816-0301-2020-17-4-7-21.

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A problem of combining elementary sectors of an airspace region is considered, in which a minimum number of combined sectors must be obtained with restrictions on their load and feasibility of combinations such as the requirement of the space connectivity or the membership of a given set of permissible combinations. Computational methods are proposed and tested to be used for solution of general problems of airspace sectorization. In particular, two types of combinatorial algorithms are proposed for constructing partitions of a finite set with specified element weights and graph-theoretical relationships between the elements. Partitions are constructed by use of a branch and bound method to minimize the number of subsets in the final partition, while limiting the total weight of elements in the subset. In the first type algorithm, ready-made components of the final partition are formed in each node of the branch and bound tree. The remaining part of the original set is further divided at the lower nodes. In the second type algorithm, the entire current partition is formed in each node, the components of which are supplemented at the lower nodes. When comparing algorithms performance, the problems are divided into two groups, one of which contains a connectivity requirement, and the other does not. Several integer programming formulations are also presented. Computational complexity of two problem variants is established: a bin packing type problem with restrictions on feasible combinations, and covering type problem.

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27

Wulff, Wolfgang. "COMPUTATIONAL METHODS FOR MULTIPHASE FLOW." Multiphase Science and Technology 5, no. 1-4 (1990): 85–238. http://dx.doi.org/10.1615/multscientechn.v5.i1-4.30.

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28

Dinwoodie, Ian H. "Computational methods for asynchronous basins." Discrete and Continuous Dynamical Systems - Series B 21, no. 10 (November 2016): 3391–405. http://dx.doi.org/10.3934/dcdsb.2016103.

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29

Frenz, Christopher. "Computational Methods of Protein Engineering." Recent Patents on Biomedical Engineeringe 3, no. 1 (January 1, 2010): 1–5. http://dx.doi.org/10.2174/1874764711003010001.

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30

Waring, George O. "Computational Methods Advantageous for Ophthalmology." Journal of Refractive Surgery 8, no. 2 (March 1992): 126. http://dx.doi.org/10.3928/1081-597x-19920301-06.

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31

Leelananda, Sumudu P., and Steffen Lindert. "Computational methods in drug discovery." Beilstein Journal of Organic Chemistry 12 (December 12, 2016): 2694–718. http://dx.doi.org/10.3762/bjoc.12.267.

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The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein–ligand docking, pharmacophore modeling and QSAR techniques are reviewed.

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32

Zhou, Tao. "Representative methods of computational socioeconomics." Journal of Physics: Complexity 2, no. 3 (September 1, 2021): 031002. http://dx.doi.org/10.1088/2632-072x/ac2072.

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33

Rubanov, I. V., and M. Y. Kovalyov. "Computational methods for airspace sectorisation." Informatics 17, no. 4 (January 3, 2021): 7–21. http://dx.doi.org/10.37661/1816-0301-2020-17-4-7-21.

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A problem of combining elementary sectors of an airspace region is considered, in which a minimum number of combined sectors must be obtained with restrictions on their load and feasibility of combinations such as the requirement of the space connectivity or the membership of a given set of permissible combinations. Computational methods are proposed and tested to be used for solution of general problems of airspace sectorization. In particular, two types of combinatorial algorithms are proposed for constructing partitions of a finite set with specified element weights and graph-theoretical relationships between the elements. Partitions are constructed by use of a branch and bound method to minimize the number of subsets in the final partition, while limiting the total weight of elements in the subset. In the first type algorithm, ready-made components of the final partition are formed in each node of the branch and bound tree. The remaining part of the original set is further divided at the lower nodes. In the second type algorithm, the entire current partition is formed in each node, the components of which are supplemented at the lower nodes. When comparing algorithms performance, the problems are divided into two groups, one of which contains a connectivity requirement, and the other does not. Several integer programming formulations are also presented. Computational complexity of two problem variants is established: a bin packing type problem with restrictions on feasible combinations, and covering type problem.

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34

Rousu, Juho. "Computational Methods for Metabolic Networks." Electronic Proceedings in Theoretical Computer Science 116 (June 9, 2013): 11. http://dx.doi.org/10.4204/eptcs.116.2.

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35

Bardiès, M., and M. J. Myers. "Computational methods in radionuclide dosimetry." Physics in Medicine and Biology 41, no. 10 (October 1, 1996): 1941–55. http://dx.doi.org/10.1088/0031-9155/41/10/007.

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36

Kerckhoffs, R. C. P., S. N. Healy, T. P. Usyk, and A. D. McCulloch. "Computational Methods for Cardiac Electromechanics." Proceedings of the IEEE 94, no. 4 (April 2006): 769–83. http://dx.doi.org/10.1109/jproc.2006.871772.

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37

Miller, M., A. Banerjee, G. Christensen, S. Joshi, N. Khaneja, U. Grenander, and L. Matejic. "Statistical methods in computational anatomy." Statistical Methods in Medical Research 6, no. 3 (March 1, 1997): 267–99. http://dx.doi.org/10.1191/096228097673360480.

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38

Tam, Christopher K. W. "Computational aeroacoustics - Issues and methods." AIAA Journal 33, no. 10 (October 1995): 1788–96. http://dx.doi.org/10.2514/3.12728.

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39

Bian, W., A. Kendall, I. McCullough, and G. Stubbs. "Computational methods in fibre diffraction." Acta Crystallographica Section A Foundations of Crystallography 64, a1 (August 23, 2008): C564. http://dx.doi.org/10.1107/s0108767308081877.

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40

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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41

Miller, Michael, Ayananshu Banerjee, Gary Christensen, Sarang Joshi, Navin Khaneja, Ulf Grenander, and Larissa Matejic. "Statistical methods in computational anatomy." Statistical Methods in Medical Research 6, no. 3 (June 1997): 267–99. http://dx.doi.org/10.1177/096228029700600305.

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42

Katsikis, Vasilios N. "Computational methods for option replication." International Journal of Computer Mathematics 88, no. 13 (September 2011): 2752–69. http://dx.doi.org/10.1080/00207160.2011.555536.

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43

Ehrhardt, Matthias, Lucas Jódar Sánchez, and Rafael Company. "Novel methods in computational finance." International Journal of Computer Mathematics 93, no. 5 (April 7, 2016): 723–24. http://dx.doi.org/10.1080/00207160.2015.1071692.

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44

Nash, Maliha S. "Spatial Statistics and Computational Methods." Technometrics 46, no. 1 (February 2004): 115–16. http://dx.doi.org/10.1198/tech.2004.s747.

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45

Cortes, Corinna, Daryl Pregibon, and Chris Volinsky. "Computational Methods for Dynamic Graphs." Journal of Computational and Graphical Statistics 12, no. 4 (December 2003): 950–70. http://dx.doi.org/10.1198/1061860032742.

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46

Vorländer, Michael. "Computational methods in architectural acoustics." Journal of the Acoustical Society of America 129, no. 4 (April 2011): 2364. http://dx.doi.org/10.1121/1.3587647.

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47

Belytschko, Ted, Thomas J. R. Hughes, and P. Burgers. "Computational Methods for Transient Analysis." Journal of Applied Mechanics 52, no. 4 (December 1, 1985): 984. http://dx.doi.org/10.1115/1.3169187.

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48

Weintraub, Gabriel Y., C. Lanier Benkard, and Benjamin Van Roy. "Computational Methods for Oblivious Equilibrium." Operations Research 58, no. 4-part-2 (August 2010): 1247–65. http://dx.doi.org/10.1287/opre.1090.0790.

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49

Peyret, Roger, Thomas D. Taylor, and Stanley A. Berger. "Computational Methods for Fluid Flow." Physics Today 39, no. 7 (July 1986): 70–71. http://dx.doi.org/10.1063/1.2815085.

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50

Radha, Hayder. "Computational Photography: Methods and Applications." Journal of Electronic Imaging 20, no. 4 (October 1, 2011): 049901. http://dx.doi.org/10.1117/1.3663232.

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