This is probably a stupid question, but could an approach like this be used for more efficient ray-tracing?
Physics Forests
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Re: Physics Forests
#12This is probably a stupid question, but could an approach like this be used for more efficient ray-tracing?
Not a stupid question at all! Not exactly the same approach, but there's been some recent interesting research on neural net and ML driven global illumination:
[0] http://www.jiapingwang.com/files/shadebot_sig13.pdf
[1] http://cgg.mff.cuni.cz/~jirka/papers/2014/olpm/On-lineLearni...
Re: Physics Forests
#13So 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?
Re: Physics Forests
#14That 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?
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?
Re: Physics Forests
#15If it isn't based on the Navier Stokes equations, can I actually use this to reliably simulate fluid behavior? Or is this more useful for creating pseudo-realistic water scenes?
The main goal of mechanics is finding solutions for important variables of interest, from which every other dynamical variable can be easily computed. In fluid mechanics, the main objective for a given problem is finding a velocity field (once you have this, you're done). Since the NS equations are non-linear partial differential equations, finding exact solutions is impossible in most cases. Some notable non-trivial exceptions exist [1]. Navier-Stokes is extremely successful in describing flows in many real physical situations. It is believed, but has not been experimentally confirmed, that Navier-Stokes successfully describes turbulent flow. Most physical theories have a fairly well known domain in which they are applicable and not applicable (Newtonian physics breaks down at velocities near the speed of light). Currently, we don't even know if solutions to NS always exist, never mind make physical sense [2]. With this in mind, we have to acknowledge the uncertainty of the correctness of NS in describing an arbitrary flow. It is backed by well-grounded theory (parts of the equations can be derived from conservation laws) and works under many different conditions. This comment landed up longer than I expected, but my main point is that the question can we "reliably simulate fluid behavior" for an arbitrary fluid - is largely up in the air [3].
However, we should restrict our attention to flows that we very strongly believe are correctly described by NS. From the original paper:
>> The main problem of our method is the same as the weakness of all machine learning approaches; the learning methods are not capable to extrapolate the model far outside the data observed during training.
The training data are existing numerical simulations of specific fluid flows. The learning algorithm learned fluid flow dynamics from simulations. This is extremely impressive. It also achieves a significant speed up in simulation time - which is also very impressive. However, as the authors say, it does not generalise well. So, in short the answer to your first question is probably "sometimes yes, but in general not really".
edit: I should add that the simulations in the paper are decidedly based on Navier-Stokes.
[1] https://en.wikipedia.org/wiki/Hagen%E2%80%93Poiseuille_flow_...
[2] https://en.wikipedia.org/wiki/Navier%E2%80%93Stokes_existenc...
Re: Physics Forests
#16This is probably a stupid question, but could an approach like this be used for more efficient ray-tracing?
Great results on relighting 2d images using neural nets here as well: http://research.microsoft.com/en-us/um/people/yuedong/projec...
Re: Physics Forests
#17That 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?
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.
Re: Physics Forests
#18Reminds me a lot of various machine learning approaches to molecular dynamics - basically trying to fit a cheap function to the energy landscape. I wonder if the more classical (rather than quantum) nature of these systems makes it more approachable?
Re: Physics Forests
#19That 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?
It occurred to me and I work as a web developer. Just think of things in abstract enough terms and possibilities like this pop out. Having the technical sophistication and time to do the work is another matter.
Re: Physics Forests
#20It would be interesting to see an analysis of the models inferred by this method, to see what kinds of approximations they're learning.