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Re:Lagrangian Neural Networks by whatshisface on Thursday March 12, 2020 @10:13AM (#22557049)
What's so great about it? I had that class too and I didn't think it was so hot. Maybe I missed something.
Re:Lagrangian Neural Networks by pas on Thursday March 12, 2020 @12:01PM (#22558172)
Elegantly deriving the optimal path, which also happens to be what reality usually does? (Because many problems are "easily" transformed into energy minimization problems [principle of least action].)

Conservation laws (and/or the associated symmetries) might be more fascinating, but that all builds on Lagrangian mechanics.

Re:Lagrangian Neural Networks by whatshisface on Thursday March 12, 2020 @12:04PM (#22558209)
Why would you say that reality "minimizes action" when you could just as easily say it "solves differential equations?" They are two equivalent ways of looking at the same thing, and you can't really tell which one nature does.
Re:Lagrangian Neural Networks by rrss on Thursday March 12, 2020 @12:37PM (#22558635)
"solves differential equations" doesn't tell you anything about which differential equations. "minimizes action" gives you a way to get the differential equations corresponding to pretty much every scenario.
Re:Lagrangian Neural Networks by whatshisface on Thursday March 12, 2020 @02:22PM (#22559959)
Specifying the action is equivalent to specifying the differential equations. Knowing that you start with a Lagrangian doesn't tell you more than knowing you start with some differential equations.
Re:Lagrangian Neural Networks by Brain_Thief on Thursday March 12, 2020 @01:45PM (#22559559)
But are the statements "reality minimizes action" and "reality solves differential equations" actually, literally equivalent to each other? It strikes me that this is not in fact the case. There are solutions to systems describing skyscraper sway that have the building stand at a terrifying lean angle; what is supposed to be selecting the particular solutions that we observe? A purely random walk through the solution space seems unsatisfying as an answer.
Re:Lagrangian Neural Networks by whatshisface on Thursday March 12, 2020 @02:26PM (#22560003)
They are actually, literally equivalent to each other. Differential equations aren't a "purely random walk," they are a statement of what is about to happen based on what is happening. You have three formalisms: Newtonian (differential equations), Lagrangian (action minimization), and Hamiltonian (differential equations, but derived from a scalar field kind of like in Lagrangian mechanics). They are all different ways of writing down the same thing, and their advantages and disadvantages are related to situational convenience.
Re:Lagrangian Neural Networks by Brain_Thief on Thursday March 12, 2020 @04:02PM (#22561135)
Thank you for the response, I find this very interesting.

So is it true to say that the production of a particular solution to a differential equation is a statement about how a system will behave based on its initial conditions, and that the statement captures within it the principle of action minimization by virtue of the fact that it is a derivation of information from natural laws?

Re:Lagrangian Neural Networks by whatshisface on Thursday March 12, 2020 @05:22PM (#22561892)
"Action is minimized" and "F = ma" are the same in the sense that x+5=0 and x+6=1 are the same. In both cases you can go from one to the other with a sequence of deductions. There's no way to tell which one is the "natural law" because you can take either one as a fact and derive the other as a consequence.
Re:Lagrangian Neural Networks by Brain_Thief on Friday March 13, 2020 @05:36PM (#22571074)
Interesting - your response makes sense. Thanks again for the information.
Re:Lagrangian Neural Networks by pas on Thursday March 12, 2020 @03:43PM (#22560938)
I just assume physicists are weirdos like that, they want reality to do easy and nice things, not math :)

As you see it clicks for many people, and doesn't mean much for others. Back then it was a very welcome click.

After all, it should be possible to find antiderivatives and solve ODEs without doing the dance with substitutions and change of variables, but still, we find it many magnitudes easier than just looking at it and divining the correct solution.