| Solving high-dimensional parabolic PDEs using the tensor train format L Richter, L Sallandt, N Nüsken International Conference on Machine Learning, 8998-9009, 2021 | 46 | 2021 |
| Approximating optimal feedback controllers of finite horizon control problems using hierarchical tensor formats M Oster, L Sallandt, R Schneider SIAM Journal on Scientific Computing 44 (3), B746-B770, 2022 | 29 | 2022 |
| Approximating the stationary Hamilton-Jacobi-Bellman equation by hierarchical tensor products M Oster, L Sallandt, R Schneider arXiv preprint arXiv:1911.00279, 2019 | 19 | 2019 |
| Approximative policy iteration for exit time feedback control problems driven by stochastic differential equations using tensor train format K Fackeldey, M Oster, L Sallandt, R Schneider Multiscale Modeling & Simulation 20 (1), 379-403, 2022 | 16 | 2022 |
| 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 | 14 | 2023 |
| Approximating the stationary bellman equation by hierarchical tensor products M Oster, L Sallandt, R Schneider arXiv preprint arXiv:1911.00279, 2019 | 6 | 2019 |
| Approximating the stationary Hamilton–Jacobi–Bellman equation by hierarchical tensor products. arXiv, 2019 M Oster, L Sallandt, R Schneider arXiv preprint arXiv:1911.00279, 1911 | 6 | 1911 |
| Computing high-dimensional value functions of optimal feedback control problems using the Tensor-train format LJ Sallandt | 4 | 2022 |
| From continuous-time formulations to discretization schemes: tensor trains and robust regression for BSDEs and parabolic PDEs L Richter, L Sallandt, N Nüsken arXiv preprint arXiv:2307.15496, 2023 | 1 | 2023 |
