- #1
friend
- 1,452
- 9
I'm wondering which theoretical physics programmes actually try to calculate the classical 4D dimensionality of spacetime that we observe. Thanks.
friend said:Are there efforts that try to use a Feynman type path integral whose paths wind through different dimensions for the same point, or something like that? For it seems arbitrary to label an event with a 1D coordinate or a 3D or 5D coordinate if all you're trying to do is distinquish one event from another. All that's required is to have a different number or set of numbers for each event.
friend said:Does this sound like any study programme out there?
friend said:Are there efforts that try to use a Feynman type path integral whose paths wind through different dimensions for the same point, or something like that? For it seems arbitrary to label an event with a 1D coordinate or a 3D or 5D coordinate if all you're trying to do is distinquish one event from another. All that's required is to have a different number or set of numbers for each event. I'm thinking that maybe such a transdimensional path integral would have a classical path of our familiar 4D universe. Does this sound like any study programme out there?
apeiron said:So a directed action in 3-space would be very obvious and distinctive. In one axis, something happened, while in the other two - with equal crisp definiteness - nothing did. But the same action in 390-space, or 4,000,033-space would be "lost" comparatively. The higher the dimensionality, the less anything overall would seem to have changed?
Is this the logic of your approach?
Programmes that calculate dimensionality are computer programs that use mathematical algorithms to determine the dimensions of a given dataset or set of data points. These programs are commonly used in data analysis and machine learning applications.
These programmes work by first analyzing the data to determine the number of variables or features present. Then, they use various mathematical techniques such as principal component analysis or singular value decomposition to determine the number of dimensions needed to represent the data accurately.
The main benefit of using programmes that calculate dimensionality is that they can help simplify complex data and reduce the number of variables needed to represent it. This can improve the efficiency and accuracy of data analysis and machine learning models.
One limitation of these programmes is that they are only as accurate as the data they are given. If the data is noisy or contains outliers, it may affect the accuracy of the dimensionality calculations. Additionally, these programmes may not be suitable for all types of data and may require some manual tuning.
While programmes that calculate dimensionality can be used for a wide range of data types, they may not be suitable for all types of data. For example, they may not be effective for data that has highly non-linear relationships or data with a large number of categorical variables. It's essential to understand the limitations of these programmes and choose the appropriate one for the type of data being analyzed.