A Recently, I want to write something about data in physics

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The discussion highlights the intersection of machine learning and physics, emphasizing the importance of data in both fields. Participants explore whether traditional physics models could be replaced by neural networks that predict experimental outcomes. Concerns are raised about the limitations of machine learning in providing explanations for scientific discoveries. An example from Cornell illustrates how data-driven methods can yield new insights, although challenges remain in publishing findings without a theoretical framework. The conversation concludes with a note on practical applications of such data-driven approaches in agriculture.
Pring
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Nowadays, the machine learning of computer science is hot. It is based on data, and drove by data. Thus, a question is naturally coming out: the data in physics, and the models of data. I think it is a really empirical way to know how physicists do the same thing as the computer scientists. So could you give some examples about the data in physics or give the links?
 
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Good. Is thee anything we can help you with? A question, perhaps ?

Did you notice your post got an 'advanced' tag, meaning PhD level ?
 
Pring said:
Nowadays, the machine learning of computer science is hot. It is based on data, and drove by data. Thus, a question is naturally coming out: the data in physics, and the models of data. I think it is a really empirical way to know how physicists do the same thing as the computer scientists.
Are you investigating the question, whether the current physics (mathematical formulas) will be once replaced by neutral networks, which predict the outcome of experiments?
 
A.T. said:
Are you investigating the question, whether the current physics (mathematical formulas) will be once replaced by neutral networks, which predict the outcome of experiments?
That's a horror idea! I just think of the key idea 'from data to model' in physics instead of investigating it.
 
There is a fundamental weakness in using machine learning methods in science. Once we discover something we need to be able to explain it but that might not be possible.

Cornell has a program that extracted the equations of motion for a compound mechanical pendulum using lots of data with no knowledge of physics. It worked so they tried it on some cellular data and discovered some behavior in cells never before quantified as an equation. However the biologists said they couldn't publish because they didn't have a theory to explain the result.

https://www.wired.com/2009/04/Newtonai/
 
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jedishrfu said:
https://www.wired.com/2009/04/Newtonai/

If you follow that link deep enough, you discover it spawned a company, which was then bought by an agricultural seed company, which uses it to evaluate hybrid seeds to market to farmers! You may even be eating the results in your next meal.

So it appears there was a use for the program after-all!
 
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