Generalizing Functional Integration for Piecewise-Smooth Manifolds

In summary, there is currently no general theory of functional integration, unlike in complex analysis where the concept of "a path integral" existed. Feynman developed an approach for evaluating functional integrals for paths in spacetime, but it is unclear if the set of all piecewise-differentiable paths can be decomposed into infinitesimal line segments and how this affects the integral's value. The axiom of choice may also lead to complications. There are some results that can be generalized to infinite-dimensional integrals, such as the Poisson sum-formula.
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I'm intrigued by the fact that apparently no general theory of functional integration has been developed - something along the lines of Riemann, Cauchy, and Lebesgue. Feynman developed an approach to evaluating functional integrals for paths in spacetime - but I'm wondering whether it is possible to generally decompose the set of all piecewise-differentiable paths into infinitesimal line segments - and whether our choice of decomposition affects the value of the integral. Might the axiom of choice lead to complications? And how might one generalize this to general functions defined on piecewise-smooth manifolds in arbitrary dimension and with arbitrary signature?
 
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  • #2
Well in complex analysis the notion of "a path integral" existed. Cauchy new about it.
 
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Although is not the case of knowing exact functional integral formulae, there are many results that can be generalized to Infinite-dimensional integrals as for example Poisson sum-formula.

[tex] \sum_{m=-\infty}^{\infty}F[x_{0}(t)+m\delta (t-t')]= \int \mathcal D[x(t)]\sum_{m=-\infty}^{\infty}exp(2\pi m\int_{a}^{b} dt x(t))F[x(t)+x_{0} (t)] [/tex]
 
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1. What is Functional Integration?

Functional Integration is a process used in science to understand the relationship between different variables and how they interact with each other to produce a specific outcome. It involves analyzing data and using statistical methods to determine the functional relationships between variables.

2. How is Functional Integration different from other data analysis techniques?

Unlike other data analysis techniques, Functional Integration focuses on understanding the underlying functional relationships between variables, rather than just looking at correlations. It allows for a more comprehensive understanding of how different variables influence each other and the overall outcome.

3. What types of data can be analyzed using Functional Integration?

Functional Integration can be applied to a wide range of data types, including numerical, categorical, and even textual data. It is particularly useful for analyzing complex and multi-dimensional data sets.

4. How is Functional Integration used in scientific research?

Functional Integration is commonly used in scientific research to identify and understand the complex relationships between variables in a system. It can be used in fields such as biology, economics, psychology, and many others to gain insights into how different factors influence a particular phenomenon.

5. What are the benefits of using Functional Integration in data analysis?

One of the main benefits of Functional Integration is that it allows for a more in-depth analysis of data, leading to a better understanding of the underlying relationships between variables. It can also help to reveal hidden patterns and insights that may not be apparent through other data analysis techniques.

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