Are you investigating the question, whether the current physics (mathematical formulas) will be once replaced by neutral networks, which predict the outcome of experiments?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.
That's a horror idea! I just think of the key idea 'from data to model' in physics instead of investigating it.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?
jedishrfu said:
Data is crucial in physics as it provides evidence and supports theories and hypotheses. It allows scientists to make accurate predictions and draw conclusions about the physical world.
Data in physics can be collected through various methods such as experiments, observations, and simulations. Instruments and tools are also used to gather precise measurements and observations.
In physics, both quantitative (numerical) and qualitative (descriptive) data are commonly used. Quantitative data is used for calculations and statistical analysis, while qualitative data is used to describe and explain phenomena.
Data in physics is analyzed through mathematical and statistical methods. This includes plotting graphs, calculating averages and standard deviations, and using equations and models to interpret the data.
One of the main challenges in working with data in physics is ensuring its accuracy and reliability. Other challenges include dealing with a large amount of data, selecting appropriate analysis methods, and interpreting the data accurately to draw meaningful conclusions.