A Survey of Computational Physics

Landau, Rubin H.; Páez, Manuel J.; Bordeianu, Cristian C.

doi:10.2307/j.ctv7h0rx7

Cited by 21 publications

(17 citation statements)

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“…Dr. Landau was able to introduce them to numerous computational physics examples and numerical methods from the Python version of his book [5]. He was also able to give them practical advice on how to use Python in physics.…”

Section: Our Physics and Astronomy Departmentmentioning

confidence: 99%

Computing for STEM majors

Adams

Pruim

2012

Proceedings of the 43rd ACM Technical Symposium on Computer Science Education

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One of the challenges facing the U.S. technological workforce is that as fewer students take computing courses, fewer college graduates are being prepared for computing careers. Besides trying to attract more CS majors, another approach is to (i) design a computing curriculum that appeals to students and faculty from other (non-CS) disciplines, (ii) use special scholarships to attract students to that curriculum, and (iii) sponsor faculty development workshops for non-CS departments. In this paper, we detail this approach, using a new introductory course oriented to science majors, and scholarships funded by the National Science Foundation Scholarships for Science, Technology, Engineering, and Mathematics (NSF S-STEM) program to motivate non-CS majors to take CS courses. We also present several success stories that this approach has produced in its first two years.

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Section: Our Physics and Astronomy Departmentmentioning

confidence: 99%

Computing for STEM majors

Adams

Pruim

2012

Proceedings of the 43rd ACM Technical Symposium on Computer Science Education

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“…In figure 4, we show a 2D and a 3D representation of the results of evaluating numerically the wavelet transform (12) for the sample signal (1). The Java code used for the transform is from our textbook [4]. Recall that this signal contains one frequency during the first window of time, two frequencies during the second time window and three frequencies for the last window.…”

Section: The Continuous Wavelet Transformmentioning

confidence: 99%

“…Likewise, the ith member of the Daubechies class is obtained by applying the inverse transform to a Y vector with a 1 in the ith position and zeros in all the other positions. Programmes to perform DWTs and their inverse are available from our text [4] and on the Web 4 .…”

Section: Daubechies Wavelets Filteringmentioning

confidence: 99%

Wavelet analyses and applications

2009

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It is shown how a modern extension of Fourier analysis known as wavelet analysis is applied to signals containing multiscale information. First, a continuous wavelet transform is used to analyse the spectrum of a nonstationary signal (one whose form changes in time). The spectral analysis of such a signal gives the strength of the signal in each frequency as a function of time. Next, the theory is specialized to discrete values of time and frequency, and the resulting discrete wavelet transform is shown to be useful for data compression. This paper is addressed to a broad community, from undergraduate to graduate students to general physicists and to specialists in other fields than wavelets.

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“…Calculating expected values from these conditional distributions involve high‐dimensional integrals, which are typically computed using Laplace's method (Laplace, ) or via Monte Carlo sampling (Gelman et al , ; Landau et al , ; Quinn, ). The focus here is on the former, which rests on finding minima of the log‐likelihood of the conditional probability distribution.…”

Section: Introductionmentioning

confidence: 99%

Symplectic structure of statistical variational data assimilation

Kadakia

Rey

2017

Quart J Royal Meteoro Soc

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Data assimilation variational principles (4D-Var) exhibit a natural symplectic structure among the state variables x(t) andẋ(t). We explore the implications of this structure in both Lagrangian coordinates {x(t),ẋ(t)} and Hamiltonian canonical coordinates {x(t), p(t)} through a numerical examination of the chaotic Lorenz 1996 model in ten dimensions. We find that there are a number of subtleties associated with discretization, boundary conditions, and symplecticity, suggesting differing approaches when working in the the Lagrangian versus the Hamiltonian description. We investigate these differences in detail, and accordingly develop a protocol for searching for optimal trajectories in a Hamiltonian space. We find that casting the problem into canonical coordinates can, in some situations, considerably improve the quality of predictions.

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A Survey of Computational Physics

Cited by 21 publications

References 0 publications

Computing for STEM majors

Computing for STEM majors

Wavelet analyses and applications

Symplectic structure of statistical variational data assimilation

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