Taylor series for the general distance integral

In summary, the conversation discusses transforming the 1D gaussian distribution from flat space to curved space and the property that the integral of the distribution is equal to 1. The exponential term in the distribution is then transformed to curved space coordinates using a differential transformation. The 1D distribution is also generalized to 3D flat space and the distance of a line segment in 3D flat space is calculated. The question posed is about the Taylor series expansion of the integral of the general distance in curved space coordinates. The answer provided is a linear approximation of the function, but it is unclear if higher order terms should be included.
  • #1
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Background:

I'm trying to transform the gaussian distribution from flat space to curved space. I start with the flat, 1D gaussian distribution in the form

[tex]\[{\textstyle{1 \over {{{(\pi {\Delta ^2})}^{{\raise0.5ex\hbox{$\scriptstyle 1$}
\kern-0.1em/\kern-0.15em
\lower0.25ex\hbox{$\scriptstyle 2$}}}}}}}{e^{{{ - {{({x_1} - {x_0})}^2}} \mathord{\left/
{\vphantom {{ - {{({x_1} - {x_0})}^2}} {{\Delta ^2}}}} \right.
\kern-\nulldelimiterspace} {{\Delta ^2}}}}}\][/tex]

And this has the property that

[tex]\[\int_{ - \infty }^{ + \infty } {{\textstyle{1 \over {{{(\pi {\Delta ^2})}^{{\raise0.5ex\hbox{$\scriptstyle 1$}
\kern-0.1em/\kern-0.15em
\lower0.25ex\hbox{$\scriptstyle 2$}}}}}}}{e^{{{ - {{({x_1} - {x_0})}^2}} \mathord{\left/
{\vphantom {{ - {{({x_1} - {x_0})}^2}} {{\Delta ^2}}}} \right.
\kern-\nulldelimiterspace} {{\Delta ^2}}}}}} d{x_1}\,\, = \,\,1\][/tex]

But the distance in the exponential, [tex]\[{{x_1} - {x_0}}\][/tex], does not tranform simply in curved space as, [tex]\[{q^i} - q_0^i\][/tex],

where [tex]\[{x^j} = {x^j}({q^i})\][/tex], and the [tex]\[{q^i}\][/tex] are the general curved space coordinates.



So I consider the exponential term in the form

[tex]\[{e^{{{ - {{({x_1} - {x_0})}^2}} \mathord{\left/
{\vphantom {{ - {{({x_1} - {x_0})}^2}} {{\Delta ^2}}}} \right.
\kern-\nulldelimiterspace} {{\Delta ^2}}}}}\, = \,{e^{{{ - {{(\int_{{x_0}}^{{x_1}} {dx} )}^2}} \mathord{\left/
{\vphantom {{ - {{(\int_{{x_0}}^{{x_1}} {dx} )}^2}} {{\Delta ^2}}}} \right.
\kern-\nulldelimiterspace} {{\Delta ^2}}})}}\][/tex],

Then the integral in the exponential can be transformed differentially to the curved space coordinates.

the 1D, flat gaussian generalizes to 3D flat space as,

[tex]\[{\textstyle{1 \over {{{(\pi {\Delta ^2})}^{{\raise0.5ex\hbox{$\scriptstyle 1$}
\kern-0.1em/\kern-0.15em
\lower0.25ex\hbox{$\scriptstyle 2$}}}}}}}{e^{{{ - ({{({x_1} - {x_0})}^2} + {{({y_1} - {y_0})}^2} + {{({z_1} - {z_0})}^2})} \mathord{\left/
{\vphantom {{ - ({{({x_1} - {x_0})}^2} + {{({y_1} - {y_0})}^2} + {{({z_1} - {z_0})}^2})} {{\Delta ^2}}}} \right.
\kern-\nulldelimiterspace} {{\Delta ^2}}}}}\][/tex]

And the differential distance of some line segment in 3D flat space is,

[tex]\[ds = {({(dx)^2} + {(dy)^2} + {(dz)^2})^{1/2}} = {({(\frac{{dx}}{{dt}})^2} + {(\frac{{dy}}{{dt}})^2} + {(\frac{{dz}}{{dt}})^2})^{1/2}} \cdot dt = {({\delta _{ij}}d{x^i}d{x^j})^{{\raise0.5ex\hbox{$\scriptstyle 1$}
\kern-0.1em/\kern-0.15em
\lower0.25ex\hbox{$\scriptstyle 2$}}}}\][/tex]

which generalizes to curved q-space as

[tex]\[ds = {({g_{ij}}d{q^i}d{q^j})^{{\raise0.5ex\hbox{$\scriptstyle 1$}
\kern-0.1em/\kern-0.15em
\lower0.25ex\hbox{$\scriptstyle 2$}}}} = {({g_{ij}}\frac{{d{q^i}}}{{dt}}\frac{{d{q^j}}}{{dt}})^{{\raise0.5ex\hbox{$\scriptstyle 1$}
\kern-0.1em/\kern-0.15em
\lower0.25ex\hbox{$\scriptstyle 2$}}}} \cdot dt\][/tex]

So that the exponential of the integral transforms as

[tex]\[{e^{ - {{(\int_{{x_0}}^{{x_1}} {dx} )}^2}/{\Delta ^2}}} \to \,\,\,\,\,{e^{ - {{(\int_{{t_0}}^t {{{({g_{ij}}d{q^i}d{q^j})}^{{\raise0.5ex\hbox{$\scriptstyle 1$}
\kern-0.1em/\kern-0.15em
\lower0.25ex\hbox{$\scriptstyle 2$}}}}} )}^2}/{\Delta ^2}}} = \,\,\,{e^{ - {{(\int_{{t_0}}^t {{{({g_{ij}}\frac{{d{q^i}}}{{dt'}}\frac{{d{q^j}}}{{dt'}})}^{{\raise0.5ex\hbox{$\scriptstyle 1$}
\kern-0.1em/\kern-0.15em
\lower0.25ex\hbox{$\scriptstyle 2$}}}} \cdot dt'} )}^2}/{\Delta ^2}}}\][/tex]

But the square of the integral looks to be quite messy. I will not know the [tex]\[{q^i}(t)\][/tex] to begin with. And I will end up integrating everything that's already in the exponent. So I'm looking for a Taylor series expansion for the squared integral. I won't want to get an expansion for the integral squared since that will only give me copies of the integral. So I want an expansion of only the integral, and I will then square it for the leading terms in [tex]\[d{q^i}d{q^j}\][/tex]. Has anyone ever seen a Taylor series expansion for the general distance integral? Thank you.
 
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  • #2
So the question is what is the Taylor series of

[tex]\[f(t) = \int_{{t_0}}^t {{{({g_{ij}}d{q^i}d{q^j})}^{{\raise0.5ex\hbox{$\scriptstyle 1$}
\kern-0.1em/\kern-0.15em
\lower0.25ex\hbox{$\scriptstyle 2$}}}}} = \int_{{t_0}}^t {{{({g_{ij}}\frac{{d{q^i}}}{{dt'}}\frac{{d{q^j}}}{{dt'}})}^{{\raise0.5ex\hbox{$\scriptstyle 1$}
\kern-0.1em/\kern-0.15em
\lower0.25ex\hbox{$\scriptstyle 2$}}}} \cdot dt'} \][/tex]

Generally, the Taylor series expansion is

[tex]\[f(t) = \sum\limits_{n = 0}^\infty {{f^{(n)}}({t_0}) \cdot {{(t - {t_0})}^n}/n!\,\,\, = } \,\,f({t_0}) + f'({t_0})(t - {t_0}) + f''({t_0}){(t - {t_0})^2}/2! + f'''({t_0}){(t - {t_0})^3}/3! + ...\][/tex]

If f(t) as defined by the integral above is expanded about t0,

[tex]\[f({t_0}) = 0\][/tex] since [tex]\[\int_{{t_0}}^{{t_0}} {...\,dt} = 0\][/tex]

And,

[tex]\[f'({t_0}) = {({g_{ij}}\frac{{d{q^i}}}{{dt}}\frac{{d{q^j}}}{{dt}})^{{\raise0.5ex\hbox{$\scriptstyle 1$}
\kern-0.1em/\kern-0.15em
\lower0.25ex\hbox{$\scriptstyle 2$}}}}{|_{t = {t_0}}}\][/tex] since [tex]\[\frac{d}{{dt}}\int_{{t_0}}^t {F(t')dt' = F(t)} \][/tex]

And,

[tex]\[f''({t_0}) = {\textstyle{1 \over 2}}{({g_{ij}}\frac{{d{q^i}}}{{dt}}\frac{{d{q^j}}}{{dt}})^{ - {\raise0.5ex\hbox{$\scriptstyle 1$}
\kern-0.1em/\kern-0.15em
\lower0.25ex\hbox{$\scriptstyle 2$}}}}({{g'}_{ij}}\frac{{d{q^i}}}{{dt}}\frac{{d{q^j}}}{{dt}} + {g_{ij}}\frac{{{d^2}{q^i}}}{{d{t^2}}}\frac{{d{q^j}}}{{dt}} + {g_{ij}}\frac{{d{q^i}}}{{dt}}\frac{{{d^2}{q^j}}}{{d{t^2}}}){|_{t = {t_0}}}\][/tex]

So a linear approximate of f(t) is

[tex]\[f(t) \approx {({g_{ij}}\frac{{d{q^i}}}{{dt}}\frac{{d{q^j}}}{{dt}})^{{\raise0.5ex\hbox{$\scriptstyle 1$}
\kern-0.1em/\kern-0.15em
\lower0.25ex\hbox{$\scriptstyle 2$}}}}{|_{t = {t_0}}} \cdot (t - {t_0})\][/tex]

My question is do I include higher or terms, [tex]\[f''({t_0}){(t - {t_0})^2}/2!\][/tex] and higher?
 

1. What is a Taylor series for the general distance integral?

A Taylor series for the general distance integral is a mathematical representation of a function that can be used to approximate the value of the function at a particular point. It is based on the idea that any smooth function can be approximated by a polynomial function.

2. How is a Taylor series for the general distance integral calculated?

A Taylor series for the general distance integral is calculated by taking the derivatives of the function at a given point and using those values to create a polynomial expression. The more terms included in the series, the more accurate the approximation will be.

3. What is the significance of the Taylor series for the general distance integral?

The Taylor series for the general distance integral is significant because it allows us to approximate the value of a function at a specific point without having to explicitly calculate the function at that point. This can be useful in many applications, such as in physics and engineering.

4. What are the limitations of using a Taylor series for the general distance integral?

One limitation of using a Taylor series for the general distance integral is that it only provides an approximation of the function at a specific point and may not accurately represent the function as a whole. Additionally, the series may not converge for all functions, making it unusable in some cases.

5. How is a Taylor series for the general distance integral used in real-world applications?

A Taylor series for the general distance integral is commonly used in physics and engineering, where it can be used to approximate the behavior of a system or model. It can also be used in numerical analysis to solve problems involving differential equations and in economics to model economic relationships.

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