Machine learning can be used for uncovering quark-gluon secrets. Advanced new methods are needed.
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It is simple. Very small particles spin at high energies and interact with other particles to create what we call “quarkgluon Plasma”. 888 عربي
These complex processes cannot be studied on high-performance computing systems.
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Neural networks
Dr. Andreas Ipp from the Institute for Theoretical Physics, Wien states that simulating a quark-gluon plasma requires a lot of computing time.
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Image recognition is similar to using neural networks. A neural network can be used to identify whether a cat is visible in certain photos. مباريات اليورو ٢٠٢١
This technique is not applicable to quarkgluon Plasma, however, because quantum fields that mathematically describe the particles and the forces between them can be represented in different ways. Ie. Ie. Quantum theories have a problem in that mathematically permitted changes can be more complicated than they appear. However, they can still be used to describe exactly the same physical state.
The structure of the network includes gauge symmetries
Dr. David I. Muller stated, “If these gauge symmetries aren’t taken into consideration, you won’t be able to understand the results of computer simulators. Muller believes that it is simpler to teach a neural network how to solve gauge symmetries on its own. According to tests, these networks can also learn how to deal with simulation data for quark-gluon plasma.
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Andreas Ipp asserts that it is possible to make predictions about these systems using neural networks — such as what quarkgluon Plasma will look at a later time point. The system does not need to be able to calculate every step of the timeline. This means it will only provide basicly valid results.
These methods will require time to simulate CERN’s atomic core collisions. ما هو الكازينو The new neural network type is a promising tool for describing physical phenomena that are impossible to model using other computational methods.
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