Prof. Dr. Daniel Potts

Potts, D., & Weidensager, L. (2024). Variable transformations in combination with wavelets and ANOVA for high-dimensional approximation. Advances in Computational Mathematics, 50, 53. https://doi.org/10.1007/s10444-024-10147-2

Weidensager, L., Krumm, D., Potts, D., & Odenwald, S. (2024). Estimating vertical ground reaction forces from plantar pressure using interpretable high-dimensional approximation. Sports Engineering, 27(3). https://doi.org/10.1007/s12283-023-00444-2

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.

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). 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

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