# Thread: mathematical modelling

1. what is mathematical modelling? i need some examples of it...

2.

3. Start here.
Examples?
Population growth (or decline).
Economics.
Wargaming. (Both hobby and "serious" professional).
Etc. Etc.
Far too many uses to list.

4. We use mathematical models of electronic circuits (aka simulations) to design everything from individual transistors to complete microprocessors.

5. Originally Posted by cmab
what is mathematical modelling? i need some examples of it...
As Dywyddyr says, hardly any activity hasn't been modeled mathematically. Wall Street uses math all the time, mainly to convert money into less money and more global warming. It's a subtle thermodynamic thing, but I'm sure it's well modeled mathematically.

6. Science uses mathematical modelling almost exclusively. The consistency and predictability of events allows for very accurate mathematical models of known systems. Accuracy and consistency allows for modeling.
There are some "uncertain" systems like weather which responds to so many variables it is hard to find accuracy (ceertainty) at a local scale.

7. Science uses mathematical modelling almost exclusively.
If you mean statistical modelling, that's not entirely true.

The only topic on which I have any knowledge at all is climate, and those models are not statistical, they're physics based. They feed in the various equations, Clausius-Clapeyron relation, radiative transfer equations, Milankovitch cycles and all the rest of them, set some initial conditions for a start date, push the start button and watch what happens.

I'd presume there are other disciplines where the same general rule applies. The physical or biological relationships set the framework, then you feed in some extra data about some aspect of the system and see what happens.

8. Just came across this piece on climate modelling. It's pretty dense and technical in places but this paragraph is relevant to the sort of thing that all scientists look at when modelling.

For example, climate models can be run in configurations that don’t match the real world at all: e.g. a waterworld with no landmasses, or a world in which interesting things are varied: the tilt of the pole, the composition of the atmosphere, etc. These models are useful, and the experiments performed with them may be perfectly valid, even though they differ deliberately from the observational system.What really matters is the relationship between the theoretical system and the observational system: in other words, how well does our current understanding (i.e. our theories) of climate explain the available observations (and of course the inverse: what additional observations might we make to help test our theories). When we ask questions about likely future climate changes, we’re not asking this question of the the calculational system, we’re asking it of the theoretical system; the models are just a convenient way of probing the theory to provide answers.
http://www.easterbrook.ca/steve/2010/11/validating-climate-models/

I presume geologists, metallurgists, and dozens of other scientific disciplines do similar things, though not all models are quite so complicated.

Science uses mathematical modelling almost exclusively.
If you mean statistical modelling, that's not entirely true.

The only topic on which I have any knowledge at all is climate, and those models are not statistical, they're physics based. They feed in the various equations, Clausius-Clapeyron relation, radiative transfer equations, Milankovitch cycles and all the rest of them, set some initial conditions for a start date, push the start button and watch what happens.

I'd presume there are other disciplines where the same general rule applies. The physical or biological relationships set the framework, then you feed in some extra data about some aspect of the system and see what happens.
I agree, and is why I mentioned weather as an exception from convenient predictability.

But most physics follows rigorous mathematical paths. It allows us to reliable predict the future for certain systems.

10. I agree, and is why I mentioned weather as an exception from convenient predictability.
That isn't the problem with predicting weather. Weather statistics emerge from weather events, but they're not the basis for meteorological prediction. Though they're often what we amateurs use to make our own crude predictions.

The biggest issue is that it's impossible to assemble the whole set of data for all the initial conditions over the thousands of kilometres that might affect the weather for the next few hours or days - and even if you could, the computers that could handle all that data and the equations in the short time allowed aren't available yet.

11. chaos theory is cool wish i knew more about it. Plus is it not difficult enough to graph the distance between a+b?

12. Originally Posted by fiveworlds
chaos theory is cool wish i knew more about it. Plus is it not difficult enough to graph the distance between a+b?
We know, in a 4 dimensional universe, graphing the distance from a - b takes (makes) time......

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