Problems deriving tensor equations

In summary, tensor equations are mathematical equations used to describe the relationship between tensors, which represent physical quantities and their properties. They are commonly used to solve problems involving multiple dimensions and can be derived using tensor calculus. However, deriving tensor equations can be challenging due to the complex properties of tensors and the notation used. These equations are essential in various scientific fields, including physics, engineering, and computer science, for modeling physical phenomena and solving real-world problems. They are also applied in machine learning and artificial intelligence for data analysis and pattern recognition.
  • #1
latentcorpse
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On page 14 in the notes attached in this thread:
https://www.physicsforums.com/showthread.php?t=457609

(i)In the definition given just above eqn 22, it says "In other words, [itex]\phi_\alpha \cdot \lambda[/itex] is a smooth map from [itex]I[/itex] to [itex]\mathbb{R}^n[/itex] for all charts [itex]\phi_\alpha[/itex]. Where do they pull this from?

(ii) I cannot see how they derive equation 24.

From equation 23, we have

[itex]X_p(f) = \{ \frac{d}{dt} [ f ( \lambda(t))] \}_{t=0}[/itex]
and we can rewrite [itex]f \cdot \lambda = f \cdot \phi^{-1} \cdot \phi \cdot \lambda[/itex] and then I am sure it is just an application of the chain rule but I just cannot see it! In particular why evaluate the 1st bracket at [itex]\phi(p)[/itex] and not [itex]t=0[/itex] and also the [itex]f \cdot \phi^{-1}[/itex] go together to give the [itex]F(x)[/itex] in the 1st brack adn teh [itex]\lambda[/itex] is clearly still in the 2nd bracket, but where has the [itex]\phi[/itex] gone?

Thanks
 
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  • #2
!Answer (i): The definition of $\phi_\alpha \cdot \lambda$ as a smooth map from I to $\mathbb{R}^n$ is pulled from the fact that $\lambda$ is assumed to be a smooth curve in $M$ and that $\phi_\alpha$ is a diffeomorphism from $M$ to $\mathbb{R}^n$. Since $\phi_\alpha$ is a diffeomorphism, it is a bijective, smooth function. As a result, the composition of two smooth functions is also smooth. Answer (ii): To derive equation 24, we start by using the definition of $X_p(f)$ given in equation 23, which states that $X_p(f) = \{ \frac{d}{dt} [ f ( \lambda(t))] \}_{t=0}$,where $\lambda : I \rightarrow M$ is a smooth curve such that $\lambda(0)=p$. Then, since $f \cdot \lambda$ is a composition of two smooth functions, we can apply the chain rule to obtain $X_p(f) = \{ \frac{d}{dt} [ f ( \lambda(t))] \}_{t=0} = \{\frac{d}{dt}[f \cdot \phi^{-1} \cdot \phi \cdot \lambda(t)]\}_{t=0} = \{Df_{\phi(p)} \cdot D(\phi^{-1})_{\phi(p)} \cdot \lambda'(0)\}$,where $F(x) = f \cdot \phi^{-1}$ is the function obtained by composing $f$ and $\phi^{-1}$. Finally, we use the fact that, since $\phi$ is a diffeomorphism, we have $D(\phi^{-1})_{\phi(p)} \cdot \phi'(p) = I$, which yields equation 24.
 

1. What are tensor equations?

Tensor equations are mathematical equations that describe the relationship between tensors, which are objects that represent physical quantities and their properties. These equations are used in fields such as physics, engineering, and mathematics to model and solve various problems.

2. What are some common problems that can be solved using tensor equations?

Tensor equations can be used to solve problems involving physical quantities that vary in multiple dimensions, such as stress and strain in materials, fluid flow, and electromagnetic fields. They are also useful in solving problems related to relativity, elasticity, and quantum mechanics.

3. How are tensor equations derived?

Tensor equations are derived using the principles of tensor calculus, which involves manipulating tensors and their components using mathematical operations such as differentiation and integration. These operations are based on the laws of vector and matrix algebra, but are extended to multiple dimensions.

4. What are some challenges in deriving tensor equations?

Deriving tensor equations can be challenging because tensors have complex properties, such as covariance and contravariance, which must be carefully considered when performing operations. Additionally, the notation used in tensor calculus can be difficult to interpret and manipulate, making it important to have a solid understanding of the underlying concepts.

5. How are tensor equations used in scientific research?

Tensor equations are used in a wide range of scientific research, including physics, engineering, and computer science. They are essential in modeling and predicting physical phenomena, as well as developing advanced technologies and solving real-world problems. Tensor equations are also used in machine learning and artificial intelligence, where they are applied to data analysis and pattern recognition tasks.

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