Skip to main content

Numerical Computing Formalism

  • Chapter
  • First Online:
Introduction to Python for Engineers and Scientists
  • 11k Accesses


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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Author information

Authors and Affiliations


Rights and permissions

Reprints and permissions

Copyright information

© 2018 Sandeep Nagar

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Nagar, S. (2018). Numerical Computing Formalism. In: Introduction to Python for Engineers and Scientists. Apress, Berkeley, CA.

Download citation

Publish with us

Policies and ethics