If electrons are also networks then they to have network functionality. Then I wonder if there exist something like trained atomic objects like electrons or ions.
So the idea is to use electrons full efficiency capacity. Electrons subjected to obstacles should then have prior obstacle training. Electrons that need to ?cooperate should then have this kind of training.
So maybe the electric car could have groups of different electrons working in areas of their previous training. So you classify the path into groups. From inside the parts of battery to the parts of the motor. Where different trained electron abilities are needed.
Then I got some inspiration from Blender. An open source 3D rendering program. I speculated before that noise could be a factor in the performance of machine learning. So in blender I saw that they used noise reduction for every function. Here I wonder if the same could be done with the electric car battery system.
What I speculate is. If you noise reduce the output functions of a physical object like the ion or electron. This might show defined filtered solutions resembling differential equation solutions but perhaps more complex. Like biological evolution could have been some basic filter of random.
My assumption is that a machine learning network is capable of taking advantage every input thrown at it.
So with a universe of networks I wonder. Could this mean that the universe takes advantage of things not so obvious. Like the in between space objects the atoms create in a material.
So if atoms have properties. Could empty space objects with a somewhat defined border also have properties?
Are there more not so thought of objects?
It could be that the object have important information properties. Like the border of an object is a object you can take information from. That is. Statistics about the border. Something similiar you do in machine learning where you might take the avarage smoothed border curve as input.
In my opinion. Those who setup rules for inventions have not understood. Like in fusion or battery development. Setting up simple rules that everybody needs to follow just will just result in incremental progress.
We need groundbreaking inventions. One way to figure out those is to look a the man made ”rules” of the innovative process.
Here you just set generate new questions to the rules. Questions are like finding errors in the rule assumption.
So for a battery. A non moving part battery is more safe and can be placed in a phone. But that does not mean its the best for a car. I mean the car is big enough for a moving part car engine. So why not a moving part battery.
So the aim of a groundbreaking invention is probably to invent a ”moving parts battery machine” for vehicles.
Even if you have to provide a little input energy to the battery ion transport. I wonder if it could be worth it. I mean a zero ion velocity would mean zero efficiency. So maybe you could get higher efficiency with increasing velocity.
Here I guess that maybe the battery could be unrolled into a Tape Battery. Increasing tape width will increase number of ions per minute at some speed. Maybe this is not enough but its a start of a battery machine.
If you know the answer to the question why can you calculate the how?
I think its possible. If you think about. It could work like a filter for random. So make a guess calculation. Look back and check to see if it makes sense to the physics network. Iterate until it all make sense. A bit crude algorithm though.
One thing that has to be learned from prior observable data is the network assessment of what makes sense. A bit like in machine learning.