Difference between revisions of "Notes:Differential (manifolds)"
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− | : '' | + | : ''These notes are taken from [[Books:Introduction to Smooth Manifolds - John M. Lee]] unless otherwise noted'' |
− | + | The [[skeleton (meta)|skeleton]] of what is needed for manifolds is remarkably small. It just looks verbose because of the large amounts of discussion involved. This page is my attempt to "compress" the skeleton of what is needed for understanding the differential into one place. I have applied [[recursive decent (meta)|recursive decent]] to solve this, which means picking something, finding out about what it depends on, and picking them, until a foundation of known facts is reached. | |
==Definitions== | ==Definitions== | ||
+ | * '''Topological n-manifold''' - A [[topological space]], {{Top.|M|J}} that is: | ||
+ | *# [[Hausdorff]] ({{AKA}}: [[T2]]) | ||
+ | *# [[Second countable topological space]] | ||
+ | *# [[Locally Euclidean of dimension n|Locally Euclidean of dimension {{n}}]] - {{M|1=\forall p\in M\exists U\in\mathcal{J}\exists\varphi:U\rightarrow \hat{U}\mathop{\subseteq}_{\text{open} }\mathbb{R}^n[p\in U\implies U\cong_\varphi\hat{U}]}}<ref group="Note">Check this formulation</ref> | ||
+ | * '''[[Chart]]''' - [[Tuple]] {{M|(U,\varphi)}} ([[Mathematicians are lazy]], short for {{M|(U,\varphi:U\in\mathcal{J}\rightarrow \hat{U}\mathop{\subseteq}_\text{open}\mathbb{R}^n)}}, where {{M|\varphi}} is a [[homeomorphism]] between {{M|U}} and {{M|\hat{U} }}) | ||
* '''Smoothness of a map ({{AKA}}: {{M|C^\infty}}''' - a map, {{M|f:U\subseteq\mathbb{R}^n\rightarrow V\subseteq\mathbb{R}^m}} is ''smooth'' if it has continuous partial derivatives of all orders. | * '''Smoothness of a map ({{AKA}}: {{M|C^\infty}}''' - a map, {{M|f:U\subseteq\mathbb{R}^n\rightarrow V\subseteq\mathbb{R}^m}} is ''smooth'' if it has continuous partial derivatives of all orders. | ||
* '''[[Smooth map]]''' - Given [[smooth manifold|smooth manifolds]], {{M|M}} and {{M|N}} and a [[map]], {{M|F:M\rightarrow N}}. {{M|F}} is a smooth map if: | * '''[[Smooth map]]''' - Given [[smooth manifold|smooth manifolds]], {{M|M}} and {{M|N}} and a [[map]], {{M|F:M\rightarrow N}}. {{M|F}} is a smooth map if: | ||
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* Changing coordinates - {{MM|1=\frac{\partial}{\partial x_i}\Big\vert_p=\frac{\partial \bar{x}^j}{\partial x^i}(\varphi(p))\frac{\partial}{\partial \bar{x}^j}\Big\vert_p }} - using {{ESC}} | * Changing coordinates - {{MM|1=\frac{\partial}{\partial x_i}\Big\vert_p=\frac{\partial \bar{x}^j}{\partial x^i}(\varphi(p))\frac{\partial}{\partial \bar{x}^j}\Big\vert_p }} - using {{ESC}} | ||
** Note that this is actually a vector (as there's an implicit sum over {{M|j}}. | ** Note that this is actually a vector (as there's an implicit sum over {{M|j}}. | ||
+ | |||
+ | The idea is to extend the definitions such that when we are dealing with manifolds that are open chunks of {{M|\mathbb{R}^n}}, we have the Jacobian as usual, and we extend this to a more general case. Much like the extension of continuity from metric to topological spaces. | ||
+ | ==Todo== | ||
+ | * Note that {{M|1=(x^1,\ldots,x^n)=(x^1(x),\ldots,x^n(x)):=\varphi(x)}} for a [[chart]] {{M|(U,\varphi)}} | ||
+ | ** Then expressions like {{MM|\frac{\partial \bar{x}^j}{\partial x^i}(\varphi(p))}} make significantly more sense, as really it is just {{MM|\frac{\partial \bar{x}^j}{\partial [\varphi(x)]^i}(\varphi(p))}} where {{M|[\cdot]^i}} is the {{M|i}}<sup>th</sup> component of a vector in {{M|\mathbb{R}^n}} (recall: {{M|\varphi(U)\subseteq\mathbb{R}^n}}) | ||
+ | *** Expand on this. | ||
+ | * There's another kind of [[derivation]] - although I suspect it is ALSO [[linear map|{{M|K}}-linear]] and satisfies that rule, however it slightly different. | ||
==Notes== | ==Notes== | ||
<references group="Note"/> | <references group="Note"/> |
Latest revision as of 14:24, 16 May 2016
- These notes are taken from Books:Introduction to Smooth Manifolds - John M. Lee unless otherwise noted
The skeleton of what is needed for manifolds is remarkably small. It just looks verbose because of the large amounts of discussion involved. This page is my attempt to "compress" the skeleton of what is needed for understanding the differential into one place. I have applied recursive decent to solve this, which means picking something, finding out about what it depends on, and picking them, until a foundation of known facts is reached.
Contents
[hide]Definitions
- Topological n-manifold - A topological space, (M,J) that is:
- Hausdorff (AKA: T2)
- Second countable topological space
- Locally Euclidean of dimension n - ∀p∈M∃U∈J∃φ:U→ˆU⊆openRn[p∈U⟹U≅φˆU][Note 1]
- Chart - Tuple (U,φ) (Mathematicians are lazy, short for (U,φ:U∈J→ˆU⊆openRn), where φ is a homeomorphism between U and ˆU)
- Smoothness of a map (AKA: C∞ - a map, f:U⊆Rn→V⊆Rm is smooth if it has continuous partial derivatives of all orders.
- Smooth map - Given smooth manifolds, M and N and a map, F:M→N. F is a smooth map if:
- ∀p∈M ∃(U,φ)∈AM ∃(V,ψ)∈AN[p∈U∧F(p)∈V∧F(U)⊆V⟹ψ∘F∘φ−1:φ(U)→ψ(V) is smooth][Note 2]
- Derivation - a map, ω:C∞(M)→R that is linear and satisfies the Leibniz rule:
- ∀f,g∈C∞(M)[w(fg)=f(a)w(g)+g(a)w(f)] (sometimes called the product rule)
- Tangent space to M at p TpM is a vector space called the tangent space to M at p, it's the set of all derivations of C∞(M)
- Differential of F at p. For smooth manifolds, M and N and a smooth map, F:M→N we define the differential of F as p∈M as:
- dFp:TpM→TF(p)M given by: dFp:v↦{:C∞(N)→R:f↦v(f∘F)
Moving about
- Changing coordinates - ∂∂xi|p=∂ˉxj∂xi(φ(p))∂∂ˉxj|p - using Template:ESC
- Note that this is actually a vector (as there's an implicit sum over j.
The idea is to extend the definitions such that when we are dealing with manifolds that are open chunks of Rn, we have the Jacobian as usual, and we extend this to a more general case. Much like the extension of continuity from metric to topological spaces.
Todo
- Note that (x1,…,xn)=(x1(x),…,xn(x)):=φ(x) for a chart (U,φ)
- Then expressions like ∂ˉxj∂xi(φ(p)) make significantly more sense, as really it is just ∂ˉxj∂[φ(x)]i(φ(p)) where [⋅]i is the ith component of a vector in Rn (recall: φ(U)⊆Rn)
- Expand on this.
- Then expressions like ∂ˉxj∂xi(φ(p)) make significantly more sense, as really it is just ∂ˉxj∂[φ(x)]i(φ(p)) where [⋅]i is the ith component of a vector in Rn (recall: φ(U)⊆Rn)
- There's another kind of derivation - although I suspect it is ALSO K-linear and satisfies that rule, however it slightly different.