**RAPID SUMMARY: ** Relatively quick overview videos introducing the core topics.

1) Modelling concepts and analogies: basics of 1st principles modelling and links between systems.

2) 1st order modelling: Examples of several 1st order models and analogies between them.

3) 1st order responses and related problem solving: how do 1st order models behave and why? Concepts of time constant and gain.

4) 2nd order modelling: Examples of several 1st order models and analogies between them.

5) 2nd order responses: how do 2nd order models behave and why? Concepts of damping/oscillation.

6) Generic behaviours: overview of characterisation of system behaviours. Stability, LHP and RHP.

** 1. Modelling principles: **
How do we do physical modelling? Are there common concepts we can exploit? What are the analogies between different disciplines? Derive models for example scenarios.

** 2. First order modelling:
**
Define a number of engineering scenarios which lead to first order models. Demonstrate the modelling from first principles and illustrate analogies.

** 3. First order model behaviours: **
How do first order systems behave? Are there efficient and insightful ways of defining and illustrating behaviour. How do we choose system parameters to achieve the desired behaviour?

** 4. Second order modelling:**
Define a number of engineering scenarios which lead to second order models. Demonstrate the modelling from first principles and illustrate analogies.

** 5. Second order model behaviours:**
How do second order systems behave? Are there efficient and insightful ways of defining and illustrating behaviour. How do we choose system parameters to achieve the desired behaviour?

** 6. Generic behaviours: **
Discussion of how to characterise behaviour in general, including for higher order systems.

** 7. Case studies: **
Examples of a variety of engineering scenarios and modelling from first principles leading to models with different orders and attributes.

** 8. Linearisation of non linear models: **
Most real models included nonlienar ocmponents and relationships, but can be approximated well enough locally by a linear model.