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Teaching physics to neural networks removes 'chaos blindness'

phys.org

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Re: Teaching physics to neural networks removes 'chaos blindness'

#11

Post by jefft255 »

mhh__ wrote: post #7
keenmaster wrote: post #2

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.

Vehicle dynamics is a fairly accurate science these days (50/50 for the tires)

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.

Re: Teaching physics to neural networks removes 'chaos blindness'

#13

Post by [deleted] »

mywittyname wrote: post #9

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…

[deleted]

Re: Teaching physics to neural networks removes 'chaos blindness'

#14

Post by mhh__ »

jefft255 wrote: post #11
mhh__ wrote: post #7

Earlier quoted context omitted.

Vehicle dynamics is a fairly accurate science these days (50/50 for the tires)

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.

Is the issue in the surface modelling? I don't think I've ever seen a physical tire model for loose terrain

Re: Teaching physics to neural networks removes 'chaos blindness'

#15

Post by Fricken »

keenmaster wrote: post #2

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.

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.

Re: Teaching physics to neural networks removes 'chaos blindness'

#16

Post by seesawtron »

joshlk wrote: post #10
mywittyname wrote: post #9

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…

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.

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

Re: Teaching physics to neural networks removes 'chaos blindness'

#17

Post by jefft255 »

mhh__ wrote: post #14
jefft255 wrote: post #11

Earlier quoted context omitted.

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.

Is the issue in the surface modelling? I don't think I've ever seen a physical tire model for loose terrain

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.

Re: Teaching physics to neural networks removes 'chaos blindness'

#19

Post by solotronics »

mhh__ wrote: post #7
keenmaster wrote: post #2

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.

Vehicle dynamics is a fairly accurate science these days (50/50 for the tires)

Racing teams and big car manufacturers have incredibly accurate models of vehicle dynamics.

Re: Teaching physics to neural networks removes 'chaos blindness'

#20

Post by [deleted] »

joshlk wrote: post #10
mywittyname wrote: post #9

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…

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.

[deleted]
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