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Programs Diploma thesis - Neural Network Application in Physical Problems

Hello everybody. I am currently on my last year of Computational Physics education. More and more I am interested in Machine Learning and Neural Network. Time has come for me to choose diploma work thesis, so I am searching for interesting ideas where I can merge my interest in neural networks and my background in physics.

I am looking for something like spectral analysis of galaxies or quasars using neural networks. Or maybe, use ML algorithm for clustering particle collision at CERN(divide ggH and VBF for example).
In a nutshell, I want to build my neural network, feed it with some Monte Carlo data, train it, and then evaluate it on real data.

Do you have some guidance, some ideas, useful links, or anything that can help.
Thank you in advance, I really appreciate it.
 
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There's a field called symbolic regression where ML techniques are used to discover the equations describing a physical system or collection of data.

Cornell had done a project that eventually became the commercial product Eureqa. The 2009 Cornell project discovers the equations of motion for a compound pendulum system using data collected about position and time of the moving pendulum bob. Wired magazine did an article on it and they published a couple of papers on the scheme.

https://en.wikipedia.org/wiki/Symbolic_regression
 

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