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Publikationen

Kircheis, M., & Potts, D. (2023). Fast and direct inversion methods for the multivariate nonequispaced fast Fourier transform. Frontiers in Applied Mathematics and Statistics, 9. https://doi.org/10.3389/fams.2023.1155484
Kopnarski, L., Rudisch, J., Potts, D., Voelcker-Rehage, C., & Lippert, L. (2023). (in press). Predicting Object Weights from Giver’s Kinematics in Handover Actions. In B. Meyer, U. Thomas, & O. Kanoun (Eds.), Hybrid Societies - Humans Interacting with Embodied Technologies (Vol. 1). Springer.
Kämmerer, L., Potts, D., & Taubert, F. (2023). Nonlinear approximation in bounded orthonormal product bases. Sampling Theory, Signal Processing, and Data Analysis, 21(1). https://doi.org/10.1007/s43670-023-00057-7
Lakshmanan, R., Pichler, A., & Potts, D. (2023). Nonequispaced Fast Fourier Transform Boost for the Sinkhorn Algorithm. ETNA - Electronic Transactions on Numerical Analysis, 58, 289–315. https://doi.org/10.1553/etna_vol58s289
Lippert, L., Potts, D., & Ullrich, T. (2023). Fast hyperbolic wavelet regression meets ANOVA. Numerische Mathematik, 154(1-2). https://doi.org/10.1007/s00211-023-01358-8
Bartel, F., Potts, D., & Schmischke, M. (2022). Grouped Transformations and Regularization in High-Dimensional Explainable ANOVA Approximation. SIAM Journal on Scientific Computing, 44(3). https://doi.org/10.1137/20M1374547
Kircheis, M., Potts, D., & Tasche, M. (2022). Nonuniform fast Fourier transforms with nonequispaced spatial and frequency data and fast sinc transforms. Numerical Algorithms. https://doi.org/10.1007/s11075-022-01389-6
Kircheis, M., Potts, D., & Tasche, M. (2022). On regularized Shannon sampling formulas with localized sampling. Sampling Theory, Signal Processing, and Data Analysis, 20(20). https://doi.org/10.1007/s43670-022-00039-1
Kämmerer, L., Potts, D., & Taubert, F. (2022). The uniform sparse FFT with application to PDEs with random coefficients. Sampling Theory, Signal Processing, and Data Analysis, 20(2). https://doi.org/10.1007/s43670-022-00037-3
Potts, D., & Schmischke, M. (2022). Learning multivariate functions with low-dimensional structures using polynomial bases. Journal of Computational and Applied Mathematics, 403, 113821. https://doi.org/10.1016/j.cam.2021.113821
Potts, D., & Schmischke, M. (2022). Interpretable Transformed ANOVA Approximation on the Example of the Prevention of Forest Fires. Frontiers in Applied Mathematics and Statistics, 8, 795250. https://doi.org/10.3389/fams.2022.795250
Potts, D., & Schmischke, M. (2021). Interpretable Approximation of High-Dimensional Data. SIAM Journal on Mathematics of Data Science, 3(4). https://doi.org/10.1137/21M1407707
Potts, D., & Schmischke, M. (2021). Approximation of high-dimensional periodic functions with Fourier-based methods. SIAM Journal on Numerical Analysis, 59, 9. https://doi.org/10.1137/20M1354921
Potts, D., & Tasche, M. (2021). Continuous window functions for NFFT. Advances in Computational Mathematics, 47, 53. https://doi.org/10.1007/s10444-021-09873-8
Potts, D., & Tasche, M. (2021). Uniform error estimates for nonequispaced fast Fourier transforms. Sampling Theory, Signal Processing, and Data Analysis, 19, 17. https://doi.org/10.1007/s43670-021-00017-z