Abstract
Contemporary applications in computational science and engineering, as well as in finance, require powerful mathematical methods to analyze big data sets. Due to the rapidly growing complexity of relevant application data at limited computational (hardware) capacities, efficient numerical algorithms are required for the simulation of complex systems with only a few parameters. Both the parameter identification and the data assimilation are based on high-performance computational methods to approximately represent mathematical functions.
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Iske, A. (2018). Introduction. In: Approximation Theory and Algorithms for Data Analysis. Texts in Applied Mathematics, vol 68. Springer, Cham. https://doi.org/10.1007/978-3-030-05228-7_1
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DOI: https://doi.org/10.1007/978-3-030-05228-7_1
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Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05227-0
Online ISBN: 978-3-030-05228-7
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