https://www.sciencedaily.com/releases/2022/01/220125093019.htm

Such processes can only be studied using high-performance computers and highly complex computer simulations whose results are difficult to evaluate. Therefore, using artificial intelligence or machine learning for this purpose seems like an obvious idea. Ordinary machine-learning algorithms, however, are not suitable for this task. The mathematical properties of particle physics require a very special structure of neural networks. At TU Wien (Vienna), it has now been shown how neural networks can be successfully used for these challenging tasks in particle physics.

"Simulating a quark-gluon plasma as realistically as possible requires an extremely large amount of computing time," says Dr. Andreas Ipp from the Institute for Theoretical Physics at TU Wien. "Even the largest supercomputers in the world are overwhelmed by this." It would therefore be desirable not to calculate every detail precisely, but to recognise and predict certain properties of the plasma with the help of artificial intelligence.