Abstract
Our objective is to calculate the derivatives of data corrupted by noise. This is a challenging task as even small amounts of noise can result in significant errors in the computation. This is mainly due to the randomness of the noise, which can result in high-frequency fluctuations. To overcome this challenge, we suggest an approach that involves approximating the data by eliminating high-frequency terms from the Fourier expansion of the given data with respect to the polynomial-exponential basis. This truncation method helps to regularize the issue, while the use of the polynomial-exponential basis ensures accuracy in the computation. We demonstrate the effectiveness of our approach through numerical examples in one and two dimensions.
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Funding
The works of PMN, TTL, and LHN were partially supported by National Science Foundation grant no. DMS-2208159, and by funds provided by the Faculty Research Grant program at UNC Charlotte Fund no. 111272, and by the CLAS small grant provided by the College of Liberal Arts & Sciences, UNC Charlotte.
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Nguyen, P.M., Le, T.T., Nguyen, L.H. et al. Numerical Differentiation by the Polynomial-Exponential Basis. J. Appl. Ind. Math. 17, 928–942 (2023). https://doi.org/10.1134/S1990478923040191
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DOI: https://doi.org/10.1134/S1990478923040191