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Numerical Computing Formalism

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Introduction to Python for Engineers and Scientists
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Abstract

Numerical computation enables us to compute solutions for numerical problems, provided we can frame them into a proper format. This requires certain considerations. For example, if we digitize continuous functions, we are going to introduce certain errors due to the sampling at a finite frequency. Hence, a very accurate result would require a very fast sampling rate. In cases when a large dataset needs to be computed, it becomes a computationally intensive and time-consuming task. Also, users must understand that the numerical solutions are an approximation, at best, when compared to analytical solutions. The onus of finding their physical meaning and significance lies on us. The art of discarding solutions that do not have a meaning for a real-world scenario is something that a scientist/engineer develops over the years. Furthermore, a computational device is just as intelligent as its operator. The law of GIGO (garbage in, garbage out) is followed very strictly in this domain.

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© 2018 Sandeep Nagar

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Nagar, S. (2018). Numerical Computing Formalism. In: Introduction to Python for Engineers and Scientists. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-3204-0_10

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