MATHEMATICAL APPROACHES ON FOURIER-BASED APPLICATIONS IN ARTIFICAL INTELLIGENCE
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https://doi.org/10.5281/zenodo.15714148Anahtar Kelimeler:
fourier analysis, artifical intelligence, mathematicsÖzet
Fourier analysis enables us to convert the domain of functions into frequency domain. Thus, it is helpful tool to figure out and interpret the big data. Furthermore, Fourier based applications in artifical intelligence are very significant in terms of analyzing the data and developing effective models. In this research, we focus on the connection between Fourier analysis and artifical intelligence and this paper includes the mathematical background of Fourier analysis and their application to artifical intelligence such as neural operators, spectral learning. This study links usual harmonic analysis with data-driven intelligence.
Referanslar
Bronstein M., Geometric Deep Learning: Going Beyond Euclidean Data, IEEE Signal Processing Magazine, 2017.
Li Z., Kovachki N., Azizzadenesheli K., Fourier Neural Operator for Parametric PDEs, arXiv:2010.08895, 2020.
Mallat S., A Wavelet Tour of Signal Processing, Academic Press, 1999.
Tancik M. et al., Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains, NeurIPS, 2020.
Xu Z., Zhang Y., Wang Y., Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks, arXiv:1901.06523, 2019.
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Telif Hakkı (c) 2025 ARCENG (INTERNATIONAL JOURNAL OF ARCHITECTURE AND ENGINEERING) ISSN: 2822-6895
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