1000 — Machine learning¶
This chapter introduces machine learning as an engineering tool for propulsion problems: not as “AI magic”, but as function approximation under constraints.
In propulsion and aircraft system studies, we often need fast surrogates for:
- cycle calculations,
- component maps,
- mission-level performance evaluation,
- design space exploration.
A feed-forward neural network (FFNN) is one of the simplest ways to learn a mapping:
\[
\mathbf{x} \;\mapsto\; \mathbf{y}
\]
where:
- \(\mathbf{x}\) are operating/design inputs,
- \(\mathbf{y}\) are outputs predicted by a high-fidelity or expensive model.
The goal is to understand:
- what an FFNN can and cannot learn,
- what “good performance” means beyond a low loss value,
- how to evaluate generalization and failure modes.
Exercises in this chapter¶
1000.1 — FFNN regression on engine data (PyTorch)¶
Train a simple neural network to predict engine outputs from input parameters using a dataset stored in .dat files.
Learning outcomes¶
After completing this chapter, you should be able to:
- Train and evaluate a basic FFNN regression model
- Explain the difference between training error and generalization error
- Diagnose underfitting vs overfitting
- Recognize the role of scaling/normalization in regression
- Produce meaningful plots (parity plots, residuals) to validate a surrogate
- Discuss how this kind of model could be used (and misused) in system studies