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ORCIDNicholas Krämer
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2020 – today
- 2022
[j1]Jonathan Oesterle
, Nicholas Krämer, Philipp Hennig, Philipp Berens:
Probabilistic solvers enable a straight-forward exploration of numerical uncertainty in neuroscience models. J. Comput. Neurosci. 50(4): 485-503 (2022)- [c5]Nicholas Krämer, Jonathan Schmidt, Philipp Hennig:
Probabilistic Numerical Method of Lines for Time-Dependent Partial Differential Equations. AISTATS 2022: 625-639 - [c4]Nicholas Krämer, Nathanael Bosch, Jonathan Schmidt, Philipp Hennig:
Probabilistic ODE Solutions in Millions of Dimensions. ICML 2022: 11634-11649 - [i8]Emilia Magnani, Nicholas Krämer, Runa Eschenhagen, Lorenzo Rosasco, Philipp Hennig:
Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs. CoRR abs/2208.01565 (2022) - 2021
- [c3]Nicholas Krämer, Philipp Hennig:
Linear-Time Probabilistic Solution of Boundary Value Problems. NeurIPS 2021: 11160-11171 - [c2]Jonathan Schmidt, Nicholas Krämer, Philipp Hennig:
A Probabilistic State Space Model for Joint Inference from Differential Equations and Data. NeurIPS 2021: 12374-12385 - [i7]Jonathan Schmidt, Nicholas Krämer, Philipp Hennig:
A Probabilistic State Space Model for Joint Inference from Differential Equations and Data. CoRR abs/2103.10153 (2021) - [i6]Nicholas Krämer, Philipp Hennig:
Linear-Time Probabilistic Solutions of Boundary Value Problems. CoRR abs/2106.07761 (2021) - [i5]Nicholas Krämer, Nathanael Bosch, Jonathan Schmidt, Philipp Hennig:
Probabilistic ODE Solutions in Millions of Dimensions. CoRR abs/2110.11812 (2021) - [i4]Nicholas Krämer, Jonathan Schmidt, Philipp Hennig:
Probabilistic Numerical Method of Lines for Time-Dependent Partial Differential Equations. CoRR abs/2110.11847 (2021) - [i3]Jonathan Wenger, Nicholas Krämer, Marvin Pförtner, Jonathan Schmidt, Nathanael Bosch, Nina Effenberger, Johannes Zenn, Alexandra Gessner, Toni Karvonen, François-Xavier Briol, Maren Mahsereci, Philipp Hennig:
ProbNum: Probabilistic Numerics in Python. CoRR abs/2112.02100 (2021) - 2020
- [c1]Hans Kersting, Nicholas Krämer, Martin Schiegg, Christian Daniel, Michael Tiemann, Philipp Hennig:
Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems. ICML 2020: 5198-5208 - [i2]Hans Kersting, Nicholas Krämer, Martin Schiegg, Christian Daniel, Michael Tiemann, Philipp Hennig:
Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems. CoRR abs/2002.09301 (2020) - [i1]Nicholas Krämer, Philipp Hennig:
Stable Implementation of Probabilistic ODE Solvers. CoRR abs/2012.10106 (2020)
Coauthor Index
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