> For the complete documentation index, see [llms.txt](https://monasheng.gitbook.io/scalable-labs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://monasheng.gitbook.io/scalable-labs/mass-spring-damper-sysid.md).

# Mass-Spring-Damper SysID

***

## Overview

System identification (SysID) is a fundamental skill in modelling and control, enabling us to select and tune mathematical models of real-world systems from experimental data.

In this lab, you will explore both:

1. A physics-informed approach — where you construct a model based on assumed system dynamics, and tune parameters based on real-world data, and,
2. A black-box approach, where you select a candidate transfer function based on the response and directly fit it's parameters to suit measured input–output data.

By engaging with both approaches, you will build an appreciation for the trade-offs between interpretability, accuracy, and effort in modelling real systems. You will also develop practical skills in experimental design, data acquisition, and parameter estimation.

## Learning Outcomes

Upon completing this lab, you will be able to:

* [x] Design and conduct experiments to obtain dynamic response data from a real physical systems,
* [x] Interpret free-response behaviour to estimate parameters of a physical system model,
* [x] Fit and evaluate black-box transfer function models using measured input–output data,
* [x] Compare physics-informed and black-box modelling approaches, including their assumptions and limitations,
* [x] Process and visualise experimental data to support model development and validation, and,
* [x] Critically assess model fidelity and sources of error in system identification.

## Generative AI Use

{% hint style="warning" %}
 ⚡️ **Warning:** This laboratory is *unassessed*, and exists only to help you develop fundamental skills in experiment design, data collection, processing, systems modelling, and parameter estimation.

Aspects of this laboratory can be trivially completed using generative AI, and doing so will only rob you of a valuable learning experience.

*You are strongly encouraged to complete this lab without the assistance of genAI*.
{% endhint %}


---

# Agent Instructions
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## Querying This Documentation
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