| Physics-informed Gaussian process regression generalizes linear PDE solvers M Pförtner, I Steinwart, P Hennig, J Wenger arXiv preprint arXiv:2212.12474, 2022 | 27 | 2022 |
| Posterior and computational uncertainty in gaussian processes J Wenger, G Pleiss, M Pförtner, P Hennig, JP Cunningham Advances in Neural Information Processing Systems 35, 10876-10890, 2022 | 20 | 2022 |
| Probnum: Probabilistic numerics in python J Wenger, N Krämer, M Pförtner, J Schmidt, N Bosch, N Effenberger, ... arXiv preprint arXiv:2112.02100, 2021 | 19 | 2021 |
| Uncertainty Quantification for Fourier Neural Operators T Weber, E Magnani, M Pförtner, P Hennig ICLR 2024 Workshop on AI4DifferentialEquations In Science, 2024 | 4 | 2024 |
| Reparameterization invariance in approximate Bayesian inference H Roy, M Miani, CH Ek, P Hennig, M Pförtner, L Tatzel, S Hauberg arXiv preprint arXiv:2406.03334, 2024 | 3 | 2024 |
| Computation-Aware Kalman Filtering and Smoothing M Pförtner, J Wenger, J Cockayne, P Hennig arXiv preprint arXiv:2405.08971, 2024 | 2 | 2024 |
| Sample Path Regularity of Gaussian Processes from the Covariance Kernel N Da Costa, M Pförtner, L Da Costa, P Hennig arXiv preprint arXiv:2312.14886, 2023 | 2 | 2023 |
| Linearization Turns Neural Operators into Function-Valued Gaussian Processes E Magnani, M Pförtner, T Weber, P Hennig arXiv preprint arXiv:2406.05072, 2024 | 1 | 2024 |
| Probabilistic Wind Speed Downscaling for Future Wind Power Assessment N Effenberger, M Pförtner, P Hennig, N Ludwig EGU24, 2024 | 1 | 2024 |
| FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning T Cinquin, M Pförtner, V Fortuin, P Hennig, R Bamler arXiv preprint arXiv:2407.13711, 2024 | | 2024 |
| Scaling up Probabilistic PDE Simulators with Structured Volumetric Information T Weiland, M Pförtner, P Hennig arXiv preprint arXiv:2406.05020, 2024 | | 2024 |
Philipp HennigUniversity of TübingenBestätigte E-Mail-Adresse bei uni-tuebingen.de
