Uncertainty Propagation through Scientific Models

Uncertainty propagation is a very common task in scientific computing. It is the process of quantifying the uncertainty in the input of a model and propagating it through the model to quantify the uncertainty in the output. This is a very important task in many scientific fields, such as physics, chemistry, biology, and engineering.

I expect that you are familiar with the basics of probability theory and statistics. I also expect that you are familiar with Monte Carlo methods. In this chapter, we will discuss how to propagate uncertainty through scientific models more efficiently than using Monte Carlo methods.

Objectives

• Quantify input uncertainty in scientific models.

• Propagate uncertainties through a scientific models.