| Towards experimental handbooks in catalysis A Trunschke, G Bellini, M Boniface, SJ Carey, J Dong, E Erdem, L Foppa, ... Topics in Catalysis 63, 1683-1699, 2020 | 53 | 2020 |
| Variational Monte Carlo—bridging concepts of machine learning and high-dimensional partial differential equations M Eigel, R Schneider, P Trunschke, S Wolf Advances in Computational Mathematics 45, 2503-2532, 2019 | 50 | 2019 |
| Pricing high-dimensional Bermudan options with hierarchical tensor formats C Bayer, M Eigel, L Sallandt, P Trunschke SIAM Journal on Financial Mathematics 14 (2), 383-406, 2023 | 24 | 2023 |
| Convergence bounds for empirical nonlinear least-squares M Eigel, R Schneider, P Trunschke ESAIM: Mathematical Modelling and Numerical Analysis 56 (1), 79-104, 2022 | 23 | 2022 |
| A block-sparse Tensor Train Format for sample-efficient high-dimensional Polynomial Regression M Götte, R Schneider, P Trunschke Frontiers in Applied Mathematics and Statistics 7, 702486, 2021 | 12 | 2021 |
| Convergence bounds for nonlinear least squares and applications to tensor recovery P Trunschke arXiv preprint arXiv:2108.05237, 2021 | 9 | 2021 |
| Efficient approximation of high-dimensional exponentials by tensor networks M Eigel, N Farchmin, S Heidenreich, P Trunschke International Journal for Uncertainty Quantification 13 (1), 2023 | 5 | 2023 |
| Generic construction and efficient evaluation of flow network DAEs and their derivatives in the context of gas networks T Streubel, C Strohm, P Trunschke, C Tischendorf Operations Research Proceedings 2017: Selected Papers of the Annual …, 2018 | 5 | 2018 |
| Adaptive nonintrusive reconstruction of solutions to high-dimensional parametric PDEs M Eigel, N Farchmin, S Heidenreich, P Trunschke SIAM Journal on Scientific Computing 45 (2), A457-A479, 2023 | 4 | 2023 |
| Optimal sampling for stochastic and natural gradient descent R Gruhlke, A Nouy, P Trunschke arXiv preprint arXiv:2402.03113, 2024 | 2 | 2024 |
| Convergence bounds for local least squares approximation P Trunschke arXiv preprint arXiv:2208.10954, 2022 | 2 | 2022 |
| Optimal sampling for least squares approximation with general dictionaries P Trunschke, A Nouy arXiv preprint arXiv:2407.07814, 2024 | 1 | 2024 |
| Almost-sure quasi-optimal approximation in reproducing kernel Hilbert spaces P Trunschke, A Nouy arXiv preprint arXiv:2407.06674, 2024 | 1 | 2024 |
| Sample Complexity Bounds for the Local Convergence of Least Squares Approximation P Trunschke Analysis and Applications, 2024 | 1 | 2024 |
| Weighted sparsity and sparse tensor networks for least squares approximation P Trunschke, A Nouy, M Eigel arXiv preprint arXiv:2310.08942, 2023 | 1 | 2023 |
| Mini-Workshop: Nonlinear Approximation of High-dimensional Functions in Scientific Computing M Oster, J Schütte, P Trunschke Oberwolfach Reports 20 (4), 2771-2808, 2024 | | 2024 |
| Optimal sampling for stochastic and natural gradient descent Robert Gruhlke R Gruhlke, A Nouy, P Trunschke arXiv, 2024 | | 2024 |
| On the theory and practice of tensor recovery for high-dimensional partial differential equations P Trunschke Technische Universität Berlin, 2022 | | 2022 |
| Laboratoire de Mathématiques Jean Leray Nantes Université Nantes, France P Trunschke arXiv preprint arXiv:2208.10954, 2022 | | 2022 |
| Convergence bounds for nonlinear least squares for tensor recovery. P Trunschke CoRR, 2022 | | 2022 |
Martin EigelWeierstrass Institute for Applied Analysis and Stochastics Berlin在 wias-berlin.de 的电子邮件经过验证
