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Algorithmic higher-order Fourier analysis

Project description

Extending traditional Fourier analysis to analyse complex data patterns

Fourier analysis is a mathematical tool that breaks down functions into simple harmonics to further understand and analyse data. However, it faces limitations when dealing with highly intricate patterns. With the support of the Marie Skłodowska-Curie Actions programme, the AlgHOF project will use higher-order Fourier analysis, which uses more complex harmonics, to represent functions such as those involving squared terms. While higher-order Fourier analysis has found many applications in pure mathematics, its application in applied mathematics is limited. AlgHOF will develop an algorithm to decompose functions into higher-order harmonics with approximate uniqueness analogous to classical Fourier analysis. The algorithm will be implemented in Python and will be tested in time-series prediction, signal compression and machine learning.

Objective

The main objective of this action is to develop and implement constructive tools in higher-order Fourier analysis and apply them in data analysis and artificial intelligence. Fourier analysis is an extremely powerful tool to analyze functions defined on compact abelian groups. During the past decades, advances in additive combinatorics and ergodic theory have led to the discovery of a new form of representation theory on compact abelian groups that generalizes Fourier analysis. This theory is known as higher-order Fourier analysis. Roughly speaking, while Fourier analysis deals with representing functions in terms of harmonics such as exp(2*pi*i*t*x), Higher Order Fourier analysis deals with representing functions in terms of higher order harmonics such as exp(2*pi*i*t*x^2). This theory has found applications in many areas of pure mathematics but not so many in applied mathematics due to the lack of an analogue of the Fourier Transform. The objectives of this action are thus:

1) Develop an algorithm that decomposes a function in terms of higher-order harmonics. This decomposition will have a notion of approximate uniqueness analogous to that of classical Fourier analysis.

2) Implement such an algorithm in Python and test it in data analysis and artificial intelligence problems where Fourier analysis plays a prominent role, such as time series continuation, signal compression, and machine learning.

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HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

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(opens in new window) HORIZON-MSCA-2024-PF-01

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Coordinator

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 242 260,56
Address
RUE MICHEL ANGE 3
75794 PARIS
France

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Ile-de-France Ile-de-France Paris
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