What is the Topological Structure of Hypersurfaces in General Relativity?

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In summary: It covers some of the same material as Blau but in a more mathematical, rigorous way.In summary, the conversation discusses studying Hypersurfaces and the intrinsic/extrinsic geometry of GR with the aim of understanding its Hamiltonian formalism. Although the notes being studied lack mathematical rigor, a suggestion is made to read a mathematical text, specifically John Lee's 'Riemannian Manifolds', which is known for its rigour and ease of understanding. The downside is that the book does not specifically address Pseudo-Riemannian manifolds used in GR, but other texts like O'Neil's book on semi-Riemannian geometry can be helpful. The discussion then delves into the definition of an embedding and the stronger
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Hi, I am studying Hypersurfaces and the intrinsic/extrinsic geometry from http://www.blau.itp.unibe.ch/newlecturesGR.pdf with the aim of understanding the Hamiltonian formalism of GR. Although interesting, the notions introduced in these notes lack mathematical rigor.

I am looking for a text which introduces these topics with the full topological structure (written by a mathematician would be better). Thanks.
 
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When frustrated by the lack of mathematical rigour in GR texts I turned to John Lee's 'Riemannian Manifolds', which is a mathematical text that is, as expected, rigorous and (in my opinion) reasonably easy to follow. I think it was somebody here on physicsforums that recommended it to me.

There is a prequel by Lee: 'Smooth Manifolds', which may also be useful, depending on what notions you need to use. I have not felt the need to buy it yet but the result is that I am a little undercooked on vector field flows and Lie derivatives.

The downside of Lee's book is that it doesn't specifically address Pseudo-Riemannian manifolds, which is what are used in GR. In many cases the distinction doesn't matter. But sometimes it does and then you have to adapt Lee's proofs to the Pseudo-Riemannian case yourself. Unfortunately I don't know of any mathematical texts (ie not by physicists) that address Pseudo-Riemannian manifolds. Perhaps others can suggest some.
 
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I haven't looked in the notes, what exactly do you fine non-rigorous? Have you looked at O'Neil's book on semi-Riemannian geometry?
 
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Thanks andrewkirk, I will look into John Lee's 'Riemannian Manifolds'

martinbn said:
I haven't looked in the notes, what exactly do you fine non-rigorous? Have you looked at O'Neil's book on semi-Riemannian geometry?
For instance the author defines an embedding as a map
[tex]
\Phi: \Sigma =\Sigma_n \hookrightarrow M_{n+1}
[/tex]
Now this definition raises questions such as
  • What is the nature of this map (homeomorphism or diffeomorphism)? Wikipedia gives more rigorous definition of [itex]\Phi[/itex] as a homeomorphism onto its image.
  • The author says that this embedding is represented by the parametric equations [itex]\Phi:x^{\alpha}(y^a)[/itex] How are these equations actually composed? I can only think as [itex]y^{\alpha}(\Phi^{-1}(\text{something with }x^a))[/itex] where [itex]y^{\alpha}, x^a[/itex] are charts of [itex]M,\Sigma[/itex] respectively.
I think the author does not care about the structures like topology and even manifold in the notes.

The references mentioned in Wikipedia are https://en.wikipedia.org/wiki/Embedding#References, but I was not sure which one to follow.

I will also look in O'Neill's book on semi-Riemannian geometry. Thanks again.
 
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For the first one, he actually explains what he means by an embedding, at the end of page 308 and the next page. About the notations it should be clear from the examples that he gives.
 
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martinbn said:
For the first one, he actually explains what he means by an embedding, at the end of page 308 and the next page. About the notations it should be clear from the examples that he gives.
I understand what he is explaining and I think it should be enough to serve the purpose (which is understanding the canonical GR). But again it would be nice to have a complete formal definition at one's disposal (like the way Carroll does in his notes).

About the map [itex]\Phi[/itex], the author says
Strictly speaking, such a map is called an (injective) immersion, while an embedding has to satisfy a slightly stronger topological condition, but since we are not concerned with global issues, and since I have not even tried to define what a manifold is (beyond the remarks in section 4.11), it would be ridiculous to worry about such things here and this is more than good enough.
So I was wondering what is the stronger topological condition.
 
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O'Neil might be to your taste, but at times it is about geometry that need not be relevant to relativity. You could perhaps read it selectively.
 
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Ravi Mohan said:
So I was wondering what is the stronger topological condition.

The stronger topological condition is that the immersion ## f: M \rightarrow N## is an embedding if ##f ## is a homeomorphism of ##M## with ##f(M)## with the subspace topology. This is the case whenever ##M## is compact for example.
 
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1. What is a hypersurface and why is it important in science?

A hypersurface is a mathematical concept that refers to a surface in a higher-dimensional space. In science, hypersurfaces are important because they allow us to describe and analyze complex systems that cannot be easily visualized in three dimensions. They have applications in many fields, including physics, engineering, and computer science.

2. What are some common techniques for referencing hypersurfaces?

Some common techniques for referencing hypersurfaces include using parametric equations, implicit equations, and level sets. Each of these methods has its own advantages and is suitable for different types of hypersurfaces. Researchers may also use techniques such as differential geometry and topology to study and describe hypersurfaces.

3. How do hypersurfaces differ from regular surfaces?

Hypersurfaces differ from regular surfaces in that they exist in a higher-dimensional space. While regular surfaces can be described by two parameters (such as x and y coordinates), hypersurfaces require more parameters to be fully described. Additionally, hypersurfaces may exhibit more complex behaviors and properties due to their higher dimensionality.

4. What are some real-world examples of hypersurfaces?

Hypersurfaces can be found in many real-world applications, such as weather forecasting, fluid dynamics, and image processing. In weather forecasting, hypersurfaces are used to model atmospheric pressure and temperature patterns. In fluid dynamics, they are used to represent the flow of liquids and gases. In image processing, they are used to analyze and classify digital images.

5. How are hypersurfaces used in machine learning and artificial intelligence?

Hypersurfaces have gained popularity in the fields of machine learning and artificial intelligence due to their ability to handle high-dimensional data. They are used to represent and classify complex data sets, such as images, text, and sound. Hypersurfaces also have applications in neural networks, where they are used to model and optimize the behavior of artificial neurons.

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