Machine Learning – Thoughts About Fusion

Since plasma is hot. Then could fusion be about vibration. Applying this to machine learning. Assuming the particles have something to do with the parameters in the matrices.

I wonder. Could you have vibrating parameter or weight matrices?

So the idea is what problems arise with a dynamic weight matrix.

Then for fusion. What are the targets. Are there signatures of the new material that needs to be met?

There has got to be a reason for vibrations. Maybe the noise changes all the signatures a little. To get the ?gradient optimization going again.

Then I wonder. Could you get ?like a surrounding network of ?slightly but different material due to the heat. Like layers in batteries work together and so on. But these ?are more dynamic.

Machine Learning Fusion Guess – Einsteins Time Function Could Be The Way The Universe Controls Fusion Decisions Using An ?External A.I Observer

The way I see it. If you have something like high velocity noise particles. Then some machine learning model() over that. Could perhaps change decisions over time.

That is. Change the ?exact time for an event in the process. By changing time like described by time dilation examples.

Basically there exist a ‘why’ time dilation would exist.

So my guess is that there exist a model(fast_noise_particles) == A.I observer In fusion. The noise particles is here I think the plasma.