Can anyone explain very roughly what's going on here? I'm familiar with physics engines, but couldn't make heads or tails of the paper or wikipedia's article on random forests...
Physics Forests
21–30 of 33 posts
Re: Physics Forests
#22I want to see a webgl shader implementation of this!
Re: Physics Forests
#23Can anyone explain very roughly what's going on here? I'm familiar with physics engines, but couldn't make heads or tails of the paper or wikipedia's article on random forests...
Particularly, I'm wondering if this method converges to the exact solution under all conditions (for various definitions of "converging").
Re: Physics Forests
#24So in my layman's understanding of this, instead of a normal physics engine, which simulates particle movement in small timesteps, this treats the whole system as more of statistical problem? How does machine learning factor into this?
ML is just a must-have nowadays.
Re: Physics Forests
#25Can anyone explain very roughly what's going on here? I'm familiar with physics engines, but couldn't make heads or tails of the paper or wikipedia's article on random forests...
They found ways to skip intermediate computation because of reasons. That's how deep I understand it.
Re: Physics Forests
#26Earlier quoted context omitted.
Random Forests are part of how the LHC found the higgs, based on my armchair understanding of the slide decks. Measurement, Monte Carlo and Machine Learning form an interesting triangle. My impression of the LHC pipe is they used monte carlo model sims to train up classifiers that would flag the data relevant for distinguishing alternate models.
The major difference there is that LHC data measures processes which are inherently stochastic (because QM), so using these methods is pretty natural. The OP, however, is applying the same methods to highly complex, but nominally deterministic problems.
I'm curious about this. A lot of us in the computational world have to deal with able but slow iterative methods for things like fluid dynamics and electromagnetic simulations. I'd be interested to know if this works with MHD, and how well it does.
Re: Physics Forests
#27That is... an unusual use of regression, to say the least. When I see something sufficiently off the wall, I always wonder how the authors happened to think of that. It also raises a lot of questions like, how much of physics can be usefully approximated by some random forests? Could you replace most of a physics engine with an appropriate small neural network?
Neural nets are seen as universal function approximators when you study them in detail. While it isn't the usual "neural net news" you see posted online, this is exactly what ANNs are meant to do.
Re: Physics Forests
#28Earlier quoted context omitted.
The major difference there is that LHC data measures processes which are inherently stochastic (because QM), so using these methods is pretty natural. The OP, however, is applying the same methods to highly complex, but nominally deterministic problems.
I'm curious about this. A lot of us in the computational world have to deal with able but slow iterative methods for things like fluid dynamics and electromagnetic simulations. I'd be interested to know if this works with MHD, and how well it does.
It probably would, but it is harder to get performance guarantees. Seems like a taylor expansion on steroids, but with the taylor expansion you get analytical tractability (to prove correct convergence) along with speed, which you don't get in this case.
Re: Physics Forests
#29That is... an unusual use of regression, to say the least. When I see something sufficiently off the wall, I always wonder how the authors happened to think of that. It also raises a lot of questions like, how much of physics can be usefully approximated by some random forests? Could you replace most of a physics engine with an appropriate small neural network?
Given that ANNs are universal function approximators, it is natural that one would use them to actually generate a simplified model of simulations. What you see here, taking an already accurate model and generating an approximate implementation, is definitely rare but not unexpected. Long-running simulations can have their run-time drastically compressed simply by developing a neural network capable of being parallelized on GPUs, as in this paper. The ANN run time could be further compressed by generating a single-hidden-layer equivalent. Even though they are known to be exponentially larger than multiple hidden layers, the implementation can be valuable in real-time systems where analysis latency is at a premium, e.g. image recognition in machine control systems.
Re: Physics Forests
#30Earlier quoted context omitted.
Random Forests are part of how the LHC found the higgs, based on my armchair understanding of the slide decks. Measurement, Monte Carlo and Machine Learning form an interesting triangle. My impression of the LHC pipe is they used monte carlo model sims to train up classifiers that would flag the data relevant for distinguishing alternate models.
Not to hijack the thread, but would you mind posting a link to the LHC slide deck you mentioned?
I watched the announcement live stream. I'm sure the video and slides are up somewhere. Sorry I don't have it at hand.