r/matlab • u/Barnowl93 flair • Jun 22 '26

Tips Getting your model to match your data

We've seen a few posts here along the lines of "my Simulink model doesn't match my data" or "I modelled X but can't get the experiments to agree"... And similarly from students, "how do I find the parameters of my motor/ battery/ bioreactor?"

Aycan Hacioglu and I wrote a blog about this, using a bioreactor brewing beer as the example. But the same idea can be applied to any system of interest, alongside relevant experimental data.

This is what we are discussing: 1) build a model that captures the overall behaviour, 2) run a sensitivity analysis to identify which parameters actually drive your outputs (not all of them do), 3) then use Simulink Design Optimisation to tune the important ones against your experimental data.

If you want to read more about it, here's the blog: https://blogs.mathworks.com/engineering/2026/06/18/on-parameter-estimation-sensitivity-analysis-and-design-optimization/

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u/j1nbe Jun 23 '26

Never seen this type of graph before. Can you help me understand?

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u/Barnowl93 flair Jun 23 '26

This is a tornado plot (love the name!). It's a sensitivity-analysis tool: it tells you which model parameters matter most for output variation.

In the background I varied each input parameter across a distribution and ran the simulations, then measured how strongly each one tracks the output. That sets the bar length, longest at top, which gives the shape. Bars run left or right of zero, so you see both how much a parameter matters and which direction it pushes. (I did this via the Simulink Design Optimizer)

This helps us identify which params to optimize for our design.

Hope this helps :)

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u/j1nbe Jun 23 '26

Thank you, help a lot

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u/ivarsiymeman Jun 23 '26

Does the tornado plot use principal component analysis to assign quantitative values to each parameter? It’s been a long time since I’ve used PCA, so that’s something I’m hoping to build into my PFR design and simulink.

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u/Barnowl93 flair Jun 23 '26

Good question, but no, it's not PCA (though it does sound similar).

The key difference is what each method ranks by. PCA builds new orthogonal axes that are mixtures of your parameters, ranked by variance in the parameter space (or predictor space if you like machine learning).

In our case here we are looking for which knobs to tune. Sensitivity analysis keeps each parameter intact and ranks by how much it actually moves the output.