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Nicholas Krämer
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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
202020
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, 12374-12385, 2021
182021
Stable Implementation of Probabilistic ODE Solvers
N Krämer, P Hennig
arXiv preprint arXiv:2012.10106, 2020
142020
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
122022
ProbNum: Probabilistic Numerics in Python
J Wenger, N Krämer, M Pförtner, J Schmidt, N Bosch, N Effenberger, ...
arXiv preprint arXiv:2112.02100, 2021
92021
Probabilistic ODE solutions in millions of dimensions
N Krämer, N Bosch, J Schmidt, P Hennig
International Conference on Machine Learning, 11634-11649, 2022
82022
Linear-Time Probabilistic Solutions of Boundary Value Problems
N Krämer, P Hennig
Advances in Neural Information Processing Systems 34, 2021
82021
Numerical uncertainty can critically affect simulations of mechanistic models in neuroscience
J Oesterle, N Krämer, P Hennig, P Berens
bioRxiv, 2021.04. 27.441605, 2021
12021
Probabilistic solvers enable a straight-forward exploration of numerical uncertainty in neuroscience models
J Oesterle, N Krämer, P Hennig, P Berens
Journal of Computational Neuroscience 50 (4), 485-503, 2022
2022
Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs
E Magnani, N Krämer, R Eschenhagen, L Rosasco, P Hennig
arXiv preprint arXiv:2208.01565, 2022
2022
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