Simulation of Harmonic Oscillators on the Lattice

[EN] This work deals with the simulation of a two¿dimensional ideal lattice having simple tetragonal geometry. The harmonic character of the oscillators give rise to a system of second¿order linear differential equations, which can be recast into matrix form. The explicit solutions which govern the dynamics of this system can be expressed in terms of matrix trigonometric functions. For the derivation we employ the Lagrangian formalism to determine the correct solutions, which extremize the underlying action of the system. In the numerical evaluation we develop diverse state¿of¿the¿art algorithms which efficiently tackle equations with matrix sine and cosine functions. For this purpose, we introduce two special series related to trigonometric functions. They provide approximate solutions of the system through a suitable combination. For the final computation an algorithm based on Taylor expansion with forward and backward error analysis for computing those series had to be devised. We also implement several MATLAB programs which simulate and visualize the two¿dimensional lattice and check its energy conservation.This work has been supported by the Spanish Ministerio de Economia y Competitividad, the European Regional Development Fund (ERDF) under grant TIN2017-89314-P, and the Programa de Apoyo a la Investigacion y Desarrollo 2018 (PAID-06-18) of the Universitat Politecnica de Valencia under grant SP20180016.Tung, MM.; Ibáñez González, JJ.; Defez Candel, E.; Sastre, J. (2020). Simulation of Harmonic Oscillators on the Lattice. Mathematical Methods in the Applied Sciences. 43(14):8237-8252. https://doi.org/10.1002/mma.6510S823782524314Dehghan, M., & Hajarian, M. (2009). Determination of a matrix function using the divided difference method of Newton and the interpolation technique of Hermite. Journal of Computational and Applied Mathematics, 231(1), 67-81. doi:10.1016/j.cam.2009.01.021Dehghan, M., & Hajarian, M. (2010). Computing matrix functions using mixed interpolation methods. Mathematical and Computer Modelling, 52(5-6), 826-836. doi:10.1016/j.mcm.2010.05.013Kazem, S., & Dehghan, M. (2017). Application of finite difference method of lines on the heat equation. Numerical Methods for Partial Differential Equations, 34(2), 626-660. doi:10.1002/num.22218Kazem, S., & Dehghan, M. (2018). Semi-analytical solution for time-fractional diffusion equation based on finite difference method of lines (MOL). Engineering with Computers, 35(1), 229-241. doi:10.1007/s00366-018-0595-5Paterson, M. S., & Stockmeyer, L. J. (1973). On the Number of Nonscalar Multiplications Necessary to Evaluate Polynomials. SIAM Journal on Computing, 2(1), 60-66. doi:10.1137/0202007Sastre, J., Ibáñez, J., Defez, E., & Ruiz, P. (2011). Efficient orthogonal matrix polynomial based method for computing matrix exponential. Applied Mathematics and Computation, 217(14), 6451-6463. doi:10.1016/j.amc.2011.01.004Higham, N. J. (2008). Functions of Matrices. doi:10.1137/1.9780898717778Sastre, J., Ibáñez, J., Defez, E., & Ruiz, P. (2011). Accurate matrix exponential computation to solve coupled differential models in engineering. Mathematical and Computer Modelling, 54(7-8), 1835-1840. doi:10.1016/j.mcm.2010.12.049Serbin, S. M., & Blalock, S. A. (1980). An Algorithm for Computing the Matrix Cosine. SIAM Journal on Scientific and Statistical Computing, 1(2), 198-204. doi:10.1137/0901013Ruiz, P., Sastre, J., Ibáñez, J., & Defez, E. (2016). High performance computing of the matrix exponential. Journal of Computational and Applied Mathematics, 291, 370-379. doi:10.1016/j.cam.2015.04.001Higham, N. J. (1988). FORTRAN codes for estimating the one-norm of a real or complex matrix, with applications to condition estimation. ACM Transactions on Mathematical Software, 14(4), 381-396. doi:10.1145/50063.21438

Implicitization of curves and (hyper)surfaces using predicted support

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Spectral tensor-train decomposition

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