Most important is an understanding of the non-trivial problem. e.g. If your roof is leaking, just going and hitting it with a hammer won't help. You need to understand how a roof works, and how slate works etc. When you have all that knowledge processed, you can use your hammer as a tool for fixing your roof - you still don't need to understand the physics of the hammer.
Most important is an understanding of the non-trivial problem. e.g. If your roof is leaking, just going and hitting it with a hammer won't help. You need to understand how a roof works, and how slate works etc. When you have all that knowledge processed, you can use your hammer as a tool for fixing your roof - you still don't need to understand the physics of the hammer.
people, neural nets are good at recognising patterns. who ever suggested they were good at computing results to non-linear functions?
a good neural net learns whether to return 0 or 1 when asked "is there a letter A in this bitmap data?". how is that related to "what is the resultant force vector when the following objects come together under the following conditions?"
a good neural net learns whether to return 0 or 1 when asked "is there a letter A in this bitmap data?". how is that related to "what is the resultant force vector when the following objects come together under the following conditions?"
I'm done.
This question was addressed in a previous post. Check this out for more information.
col, the hammer metaphor is not valid. I replaced your invalid metaphor with a similarly invalid but more appropriate metaphor. I should have been more clear about what I was trying to do there.
You didn't address the crux of my argument.
Does arguing with people online make you feel like a Bad Mother****er?
It makes me feel like a pretentious douche, so I'm going to stop here. Cheers.
Hmm, the crux of your argument seems to be calling me names. I was trying to ignore that and continue the tyre physics discussion. It's a shame you're intent on pursuing the ad hominem angle.
Yes it is!
I clicked your 'let me google that for you' link and the second result includes various comments the back up my stance here. Both that ANNs are not good for modelling complex systems due to efficiency issues, and that you don't need to understand them to use them:
I've emboldened the pertinent sections.
i don't think you understand what neural nets are for.
just because google gives you search results with "neural net" and "nonlinear" in the same article doesn't mean what you think it means.
I beg to differ - I didn't mean that the state should contain a copy of the history, but that the history will change the state in such a manner that you don't need lug it around, much less feed it into the net.
You're telling me that the ball's position over the last x frames must be used to calcute the next position, I'm telling you to just store the current speed in the state and do away with all that calculation.
I notice you didn't embolden "neural nets have been successfully used to solve many complex and diverse tasks".
NNs are highly suited to reducing complex interactions between numerous inputs into relatively few outputs.
yes. my argument is that the outputs generated by neural nets are not the floating-point vector results of complex physical interactions.
Well, you can do floats in ANNs if you want to...
Or vectors!
You could rig up an ANN to give you vectors as well, you could theoretically rig up an ANN to solve most problems.
The issue here is that they are only _good_ at solving a limited set of problems.
There's no point in using Neural Nets in areas where they would be hopelessly inefficient or otherwise impractical. You would choose a more appropriate tool for the job.
http://www.youtube.com/watch?v=n_hiDuaZ_0Y
note from 1:30 to 2:00 how points appear and disappear.
the simulation undertaken in this video is of another order of computation cost than the one LFS can afford for tire rubber calculations, but the concept of maintaining a set of the most influential points could be useful.
Is tire physics a finished problem? I majored in math at the university but that's a long ways from really understanding physics. Well, I tried looking around since I can sort of read the math and I remember seeing one really old paper from around the 70s (nothing newer seemed to show up on Google) about how, this was unsolved.
What I mean is, areas of physics that are well understood show up on Wikipedia as well understood, tires don't seem to be done. Of course, it's entirely possible that the Pirelli engineers consider this a finished problem but I'm sort of doubtful considering the way the tires were graining in Austria.
What I mean is, areas of physics that are well understood show up on Wikipedia as well understood, tires don't seem to be done. Of course, it's entirely possible that the Pirelli engineers consider this a finished problem but I'm sort of doubtful considering the way the tires were graining in Austria.
No it's not. Even with a lot of HPC power and plenty of time.
The fundamentals of the dynamic behavior of the materials involved remains kinda mysterious atm. Math/HPC alone can't do much.
The fundamentals of the dynamic behavior of the materials involved remains kinda mysterious atm. Math/HPC alone can't do much.
from the Milliken book i'm reading (race car vehicle dynamics) it seems like the state of the art technology is to take a tire and scrub it against a moving belt at various angles and speeds and come up with tables of static data points. plotting these points can give you a bunch of curves for different variables, e.g. lateral force vs slip angle.
i believe LFS uses a set of fitted polynomials made to match these curves.
as Keling implies, this totally misses any subtle nonlinear effects. in reality the traction obtained by the tire is based on a lot of dynamic effects and what we're missing now is how the traction changes as the tire moves from one angle to another, or goes from over the limit back onto the limit.
the static data point interpolation gives a useful estimate but to get closer we need to simulate the dynamics. one example of something that needs simulating is the ridge of pressure that exists in a small part of the contact patch and that makes up for a lot of the lateral force that is generated. this ridge moves around as you switch from accelerating to braking and otherwise go from one extreme to the other.
This is how you get your reference data, yes.
That's generally how empirical models work, for example Pacejka's Magic Formula (though that's a mixture of trigonometric curves with linear or polynomial modifiers). LFS as released uses Scawen's custom implementation of an empirical model.
State of the art these are not though, and this is where more physically based modelling comes in (e.g. brush, Dugoff, finite element, etc).
It might (probably not) be good idea to use neural nets to calculate some smaller more specific parts of tire physics. But creating a huge net, which inputs stuff like velocity, position, temperature, etc... and outputs stuff like... contact frictions?, sounds quite silly to me. Probably it is possible, sure, why not, but slower than the current test bench algo.
Also lookup tables can be small part of the solution.. but not the whole thing, this is not NFS where you fetch the friction coefficient from LUT by angle of attack. It doesn't mean that anything can't be acquired from LUT. Maybe some minor stuff, which adds some spice to the soup, but would be too heavy to calculate realtime.
Also lookup tables can be small part of the solution.. but not the whole thing, this is not NFS where you fetch the friction coefficient from LUT by angle of attack. It doesn't mean that anything can't be acquired from LUT. Maybe some minor stuff, which adds some spice to the soup, but would be too heavy to calculate realtime.
RCVD is quite old, but the method is still standard. Whatever your type model is, this kind of data is still used as reference.
But with more physics based (or should I just say more detailed?) models you can't utilize the measurement directly. You have to inverse a set of model parameters.
But with more physics based (or should I just say more detailed?) models you can't utilize the measurement directly. You have to inverse a set of model parameters.
thanks for the clarification, it helps raise my level of knowledge above "next to nothing"
what does it mean to inverse a set of model parameters?
what i mean by "simulating the dynamics" instead of doing interpolation of static data points is keeping track of a few points in the carcass along the circumference of the tire and giving them some basic interactions among each other.
the idea would be to attempt to capture some of the subtleties that get missed by summing the forces they represent into one vector.
the neural net idea isn't bad either. i assume it would be possible to train one to take all input torques and current state and predict if the tire is currently exhibiting non-linear behaviour. a few of these nets might allow the simulation to switch between members in a family of functions.
How complicated can a tire model become
(143 posts, started )
FGED GREDG RDFGDR GSFDG