| Differentiable likelihoods for fast inversion of’likelihood-free’dynamical systems H Kersting, N Krämer, M Schiegg, C Daniel, M Tiemann, P Hennig International Conference on Machine Learning, 5198-5208, 2020 | 16 | 2020 |
| Stable Implementation of Probabilistic ODE Solvers N Krämer, P Hennig arXiv preprint arXiv:2012.10106, 2020 | 9 | 2020 |
| A probabilistic state space model for joint inference from differential equations and data J Schmidt, N Krämer, P Hennig Advances in Neural Information Processing Systems 34, 2021 | 7 | 2021 |
| Probabilistic ODE Solutions in Millions of Dimensions N Krämer, N Bosch, J Schmidt, P Hennig arXiv preprint arXiv:2110.11812, 2021 | 4 | 2021 |
| Probabilistic Numerical Method of Lines for Time-Dependent Partial Differential Equations N Krämer, J Schmidt, P Hennig International Conference on Artificial Intelligence and Statistics, 625-639, 2022 | 2 | 2022 |
| Linear-Time Probabilistic Solutions of Boundary Value Problems N Krämer, P Hennig Advances in Neural Information Processing Systems 34, 2021 | 2 | 2021 |
| ProbNum: Probabilistic Numerics in Python J Wenger, N Krämer, M Pförtner, J Schmidt, N Bosch, N Effenberger, ... arXiv preprint arXiv:2112.02100, 2021 | 2 | 2021 |
| Numerical uncertainty can critically affect simulations of mechanistic models in neuroscience J Oesterle, N Krämer, P Hennig, P Berens bioRxiv, 2021 | 1 | 2021 |
| Probabilistic solvers enable a straight-forward exploration of numerical uncertainty in neuroscience models J Oesterle, N Krämer, P Hennig, P Berens bioRxiv, 2021 | | 2021 |
Philipp HennigUniversity of Tübingen & Max Planck Institute for Intelligent SystemsVerified email at tue.mpg.de
