Physics Forests
people.inf.ethz.ch
Physics Forests
1–10 of 33 posts
Re: Physics Forests
#2If 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?
Re: Physics Forests
#3That 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?
Re: Physics Forests
#4If 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?
"Our approach showed the potential to be a good replacement
of standard solvers in settings, where running times is more important than the exactness of a simulation, such as in computer games or interactive design."
"In our future work we want to [...] combine learning methods with standard solvers to obtain both fast and highly accurate simulations."
From the paper's conclusion.
Re: Physics Forests
#5That 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?
Predictor-correctors is the principal behind a lot of physics engines. I guess they just got a better predictor.
Re: Physics Forests
#6That 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?
Predictor-correctors is the principal behind a lot of physics engines. I guess they just got a better predictor.
Actually, what they have here is a way of generating predictors from the corrector; which theoretically can be optimized for your domain.
Re: Physics Forests
#7If 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?
It appears to be based on Navier-Stokes, from the abstract:
> We designed a feature vector, directly modelling individual forces and constraints from the Navier-Stokes equations, giving the method strong generalization properties to reliably predict positions and velocities of particles in a large time step setting on yet unseen test videos.
Re: Physics Forests
#8That 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?
Check out NeuroAnimator[0] from 1997 (also early work from Hinton). It covers some of this using local-spaced hierarchies of neural networks that predict the deltas for the next time-step.
Re: Physics Forests
#9That 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.
Re: Physics Forests
#10If 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?
This is the latter. There are a lot of reasons you would not want this for accurate simulation, one being that you basically prime your simulation on your training data. The other one being that SPH is already quite an approximation of Navier Stokes.
But for real-time animation, this is very nice. The problem with particle fluid simulations is that everything scales linearly, but only in the number of operations per particle per simulation timestep. But the more particles you have, the smaller the timestep needs to be in order to not let the simulation explode. And this is a real problem, because you want multi-million particle simulations for realistic water, but then the number of steps you need to compute per frame increases.
They managed to do a single simulation step per frame.