Teaching physics to neural networks removes 'chaos blindness'
1–10 of 81 posts
Re: Teaching physics to neural networks removes 'chaos blindness'
#2I’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.
Re: Teaching physics to neural networks removes 'chaos blindness'
#3[deleted]
Re: Teaching physics to neural networks removes 'chaos blindness'
#4I’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.
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Re: Teaching physics to neural networks removes 'chaos blindness'
#5I’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.
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.
Re: Teaching physics to neural networks removes 'chaos blindness'
#6I’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.
This isn't something that has never been thought of. Jim Keller described many problems like changing lanes as a matter of ballistics.
Re: Teaching physics to neural networks removes 'chaos blindness'
#7I’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)
Re: Teaching physics to neural networks removes 'chaos blindness'
#8I’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.
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.
2 minute papers has good videos on neural nets learning physical modeling
Re: Teaching physics to neural networks removes 'chaos blindness'
#9Why 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.
Re: Teaching physics to neural networks removes 'chaos blindness'
#10Why 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.