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From Surrogate Modelling to Aerospace Engineering: a NASA Case Study

Piero Paialunga
TDS Archive
Published in
10 min readAug 15, 2024

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Image made by author using DALL·E

Imagine that you are going to the doctor because you have abdominal pain. Imagine that, in order to tell you the reason for your pain, the doctor needs to run a numerical simulation. This numerical simulation solves the differential equation of your body and the approximation error is so low that it results in basically 100% accuracy: you will know exactly why you have that abdominal pain, with no room for error at all (I know this sounds crazy, but bear with me here). Would you use the numerical simulation? Of course you would, why not?

Now imagine that, because this numerical simulation solves very complicated differential equations, to get the response from the computer you need to wait 35 years of GPU runtime. This would immediately drop the attractiveness of the method. What is the use of this method if it takes so long to get the response out of it? Sure, it might be 100% accurate, but the computational cost to pay is too large.

Well, engineers thought of a solution. This solution is called surrogate modelling. As the name says, the approach is to use a surrogate of the original simulation. It is also a model, meaning that it aims to replicate the result of the original system but in a fraction of the GPU resources and computational time. Of course, surrogate models are not perfect and they result in a loss of accuracy with respect to the original method, but the computational time is incredibly smaller and it turns non feasible studies into feasible ones.

In Aerospace Engineering, surrogate model is intensively used. This is because the surrogate models help to understand the parameters of a problem and allow the designers to select the variables that are actually interesting and the ones that can be discarded for a specific problem.

In this blogpost, I will give a general idea of surrogate models, from theory to code. Then, I will describe a specific NASA application of surrogate models coming from their own paper. Let’s get it started!

1. About Surrogate Models

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TDS Archive
TDS Archive

Published in TDS Archive

An archive of data science, data analytics, data engineering, machine learning, and artificial intelligence writing from the former Towards Data Science Medium publication.

Piero Paialunga
Piero Paialunga

Written by Piero Paialunga

PhD in Aerospace Engineering at the University of Cincinnati. Machine Learning Engineer @ Gen Nine, Martial Artist, Coffee Drinker, from Italy.