[HN Gopher] NeuralDEM - Real-Time Simulation of Industrial Parti...
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NeuralDEM - Real-Time Simulation of Industrial Particulate Flows
Author : lnyan
Score : 54 points
Date : 2024-11-15 10:56 UTC (7 days ago)
(HTM) web link (nx-ai.github.io)
(TXT) w3m dump (nx-ai.github.io)
| londons_explore wrote:
| This is nice, but I believe a simpler design could work better.
|
| Simply make a model which transforms a 3d section of an image to
| an embedding vector. Make another model which can reverse the
| process (ie. encoder-decoder). Do that for every tile of a
| starting state.
|
| Make an 'upscale' and 'downscale' model which can take a grid of
| embedding vectors and return a new vector representing the whole.
|
| Then make an 'advance time' model, which takes an embedding
| vector and advances time by a given number of
| seconds/microseconds/days.
|
| Now train all the models end to end to ensure that all
| combinations of upscaling/downscaling/advancing/encoding/decoding
| produce similar outputs to traditional physics models.
|
| Use an ensemble of models or a sampling scheme to find places
| where outputs do not closely match, and insert more training data
| from the physical simulation at those points.
| Azrael3000 wrote:
| Some previous work by some of the same people [0] where I was
| taking part in. Seems like this is a significant step up from the
| previous work including a novel idea that resolves quite a few
| issues we had. Love to see that.
|
| [0]: https://ml-jku.github.io/bgnn/
| szvsw wrote:
| Interesting. I wonder what parts of this approach could be
| adapted to DEM models of solids. For those unaware - even though
| DEM is naturally chosen for fluids, depending on how you
| configure the force laws between particles you can easily model
| solids as well, where each particle is essentially a chunk of
| material. There are then some interesting choices to make about
| (a) what kind of lattice you set the initial particles up in, and
| (b) how you tune the force flaws to get the macroscopic
| properties you want around stiffness, etc and (c) if you use
| multiple "types" of particles forming a composite etc.
| Azrael3000 wrote:
| I'm sorry, but DEM is not for fluid simulation. It's used to
| simulate granular materials by default. Also the hopper
| discharge that is shown does not contain any fluid. The fluid
| is usually modeled using a different tool (e.g. using the
| finite volume method) which is then coupled to the particles.
| szvsw wrote:
| Okay, fair, I was using fluid loosely (and inaccurately) to
| mean both granular and fluid behavior. But there's nothing
| inherently incompatible between fluid dynamics and the
| discrete element method as far as I am aware, just like there
| is nothing inherently incompatible with solids. Sure SPH or
| LBM or FVM are the more traditional choices for fluids and
| computationally more tractable in most cases, but they aren't
| necessarily "more right."
|
| Awesome paper on how powerful particle based methods can be:
|
| https://www.sciencedirect.com/science/article/pii/S187775032.
| ..
|
| And a fun image of a DEM solid model of fracture:
|
| http://www.cba.mit.edu/media/DEM/index.html
| Azrael3000 wrote:
| No worries. I would still consider these methods to be very
| different from each other. SPH, FVM and so on are methods
| to discretize continuum equations. If you have a continuum
| equation that describes your granular material you can use
| them and DEM kind of interchangeably. But often times such
| continuum equations do not exist for granular media or they
| break down in certain flow regimes. DEM on the other hand
| is not based on the continuum representation. Instead it is
| based on interaction forces that originate from particles
| being close by. While it might be possible to link these
| two, afaik nobody has done this, but I'm no longer active
| in the field.
| szvsw wrote:
| Take a look at the paper I linked, specifically section
| 4, which illustrates finding force laws to match the
| desired dynamics of a real physical material (in this
| case, Delrin, including elastic and plastic deformation.
|
| I guarantee you will like this paper!
| al_th wrote:
| Interesting work.
|
| Given, the recent noise around this paper
| https://arxiv.org/pdf/2407.07218 about "weak baselines" in ML x
| CFD work, I wonder how it resonates with this specific work..
|
| I am not super familiar with DEM, but I know that other particle
| based model such as SPH benefit immensely from GPU acceleration.
| Does it make sense to compare with a CPU implementation ?
|
| Besides, the output of the NeuralDEM seems to be rather coarse
| fields, correct ? In that sense, and again I'm not an expert of
| granular models so I might be entirely wrong, but does it make
| sense to compare with a method that is under a very different set
| of constraints ? Could we think about a numerical model that
| would allow to compute the same quantities in a much more
| efficient way, for example ?
| Azrael3000 wrote:
| Regarding your questions, yes, DEM also benefits a lot from GPU
| acceleration. So you can compare it to a CPU based code, but
| obviously there's an order of magnitude you can gain via GPU.
|
| Usually you are not interested in the fine fields anyways.
| Think of some fine powder in a big process, where there are
| trillions of real particles inside. You can't and don't want to
| simulate that. Mostly you are interested in these course
| quantities anyways and getting statistical data, so for that
| there's no need for the fine resolution.
|
| Regarding the numerical model that can compute these things in
| a more efficient way, they don't always exist. When you move to
| large numbers of particles you can sometimes go to continuum
| models, but they might not always behave as the real thing, as
| it's really difficult to find governing equations for such
| materials.
| sps44 wrote:
| I haven't heard of this paper, very interesting read! Thank you
| for bringing it up here. Resonates very well with the (little)
| experience I have from playing around with CNN-based surrogate
| models years ago.
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