[HN Gopher] Teaching physics to neural networks removes 'chaos b...
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Teaching physics to neural networks removes 'chaos blindness'
Author : JacobLinney
Score : 61 points
Date : 2020-06-22 04:58 UTC (1 days ago)
(HTM) web link (phys.org)
(TXT) w3m dump (phys.org)
| keenmaster wrote:
| I've said this before, but I think that a lack of physical
| modeling might be the key barrier for AV technology. Human
| drivers have a mental model of physics that they've honed for
| 17-18 hours a day since they were born.
| Fricken wrote:
| Don't sell biology short like that. Human driver are born with
| a mental model of physics that's been honed 24 hours a day
| since before they were diatoms.
| mhh__ wrote:
| Vehicle dynamics is a fairly accurate science these days (50/50
| for the tires)
| solotronics wrote:
| Racing teams and big car manufacturers have incredibly
| accurate models of vehicle dynamics.
| mhh__ wrote:
| But not outside those teams. If you want to put something
| together in a few weeks your options are relatively limited
| in that collecting accurate data is fairly hard. The actual
| dynamics of the car is fairly simple but the forces applied
| to it are quite hard to model (I don't know how much
| Michelin charge to use TameTire but I'm guessing not cheap)
| jefft255 wrote:
| I'm working on autonomous off-road vehicles, and while this
| is (probably) true for autonomous cars, dynamics modeling for
| wheeled robots on rough terrain is another beast where these
| approaches could very much help.
| mhh__ wrote:
| Is the issue in the surface modelling? I don't think I've
| ever seen a physical tire model for loose terrain
| jefft255 wrote:
| People in space robotics have been working on that (moon
| and mars rovers need to deal with this). Perception is
| also a bottleneck; you have to see rocks, root, grass,
| mud and predict the effects on the dynamics.
| [deleted]
| CardenB wrote:
| You are likely correct. I think most researchers would agree,
| however. The bigger issue is actually learning how to form
| complex models. People want networks to just learn this
| implicitly, believing that we would likely impose
| counterproductive models. Other people simply struggle to
| incorporate models into the training process.
| piyh wrote:
| 2 minute papers has good videos on neural nets learning
| physical modeling
|
| https://www.youtube.com/watch?v=2Bw5f4vYL98
| CyberDildonics wrote:
| This isn't something that has never been thought of. Jim Keller
| described many problems like changing lanes as a matter of
| ballistics.
| cmehdy wrote:
| This sounds like the opposite of what Richard Sutton seemed to
| advocate for in his "Bitter Lesson"[0]. I don't know nearly
| enough to advocate for one thing or the other, but it is
| fascinating to see that those approaches seem to compete as we
| venture into the unknown.
|
| [0] http://incompleteideas.net/IncIdeas/BitterLesson.html
| athesyn wrote:
| This sounds pretty terrifying.
| jefft255 wrote:
| But... why?
| [deleted]
| vajrabum wrote:
| I believe this refers to work presented in this journal article.
| https://journals.aps.org/pre/abstract/10.1103/PhysRevE.101.0...
|
| Abstract: Artificial neural networks are universal function
| approximators. They can forecast dynamics, but they may need
| impractically many neurons to do so, especially if the dynamics
| is chaotic. We use neural networks that incorporate Hamiltonian
| dynamics to efficiently learn phase space orbits even as
| nonlinear systems transition from order to chaos. We demonstrate
| Hamiltonian neural networks on a widely used dynamics benchmark,
| the Henon-Heiles potential, and on nonperturbative dynamical
| billiards. We introspect to elucidate the Hamiltonian neural
| network forecasting.
| mywittyname wrote:
| Why do you need a neural network when you have the Hamiltonian
| mechanics of the system modeled? I've always understood
| Langrangian/Hamiltonian mechanics to be methods of modeling the
| behavior of a system through the decomposition of the external
| constraints and forces acting on a body. In other words you can
| understand a complex model by doing some calculus on the less
| complex constituents of the model.
|
| I'm probably misunderstanding what the accomplished, but it
| sounds like they've increased the accuracy of a neural network
| model of a system, notably for edge cases, by training it on
| complete a complete model of said system.
| joshlk wrote:
| For some systems even with the Lagrangian/Hamiltonian setup
| your solving differential equations with numerical techniques
| that has error. It might be that the neural networks has less
| error than the standard techniques. This is a guess.
| seesawtron wrote:
| Hamiltonian NNs are not a new thing. There was a NIPS 2019
| paper [0] that attempted to do that same for some toy
| problems.
|
| In general the idea of including model or context-based
| information into neural networks goes along the line of
| Kahneman's System I and System II of the human mind. System I
| is the "emotional" brain that is fast and makes decisions
| quickly while System II is the "rational" brain that is slow
| and expensive and takes time to compute a response.
| Researchers have been trying to develop ML models that
| utilize this dichotomy by building corresponding dual modules
| but the major challenge remains in efficiently embedding the
| assumptions of the world dynamics into the models.
|
| [0] https://arxiv.org/abs/1906.01563 [1]
| https://en.wikipedia.org/wiki/Thinking,_Fast_and_Slow
| [deleted]
| [deleted]
| gajomi wrote:
| > it sounds like they've increased the accuracy of a neural
| network model of a system, notably for edge cases, by training
| it on complete a complete model of said system.
|
| Not quite. It's really just that they require the dynamics to
| be Hamiltonian, which would be highly atypical of the kind of
| dynamics an otherwise unconstrained neural network would learn.
| This is reflected in their loss function, the first of which
| learn an arbitrary second order differential equation, the
| second of which enforces Hamiltonian dynamics.
|
| I don't understand how this was considered novel enough to
| warrant at PRE paper.
|
| Here is a link to the paper:
|
| https://journals.aps.org/pre/pdf/10.1103/PhysRevE.101.062207
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