References

Nathanael Bosch, Filip Tronarp and Philipp Hennig. Pick-and-Mix Information Operators for Probabilistic ODE Solvers. In: International Conference on Artificial Intelligence and Statistics (AISTATS) (2022). ↩¹ ↩²

Nathanael Bosch, Philipp Hennig and Filip Tronarp. Probabilistic Exponential Integrators. In: Advances in Neural Information Processing Systems (NeurIPS) (2023). ↩¹ ↩² ↩³ ↩⁴

Filip Tronarp, Nathanael Bosch and Philipp Hennig. Fenrir: Physics-Enhanced Regression for Initial Value Problems. In: International Conference on Machine Learning (ICML) (2022). ↩¹ ↩² ↩³

Filip Tronarp, Hans Kersting, Simo Särkkä and Philipp Hennig. Probabilistic Solutions to Ordinary Differential Equations as Nonlinear Bayesian Filtering: A New Perspective. Statistics and Computing 29, 1297–1315 (2019). ↩¹ ↩²

Nicholas Krämer, Nathanael Bosch, Jonathan Schmidt and Philipp Hennig. Probabilistic ODE Solutions in Millions of Dimensions. In: International Conference on Machine Learning (ICML) (2022). ↩¹ ↩² ↩³

Nathanael Bosch. Stable and Scalable Probabilistic Numerical Solvers for Stiff and High-Dimensional ODEs (2026), arXiv:2606.08203. ↩¹ ↩²

Simo Särkkä and Arno Solin. Applied Stochastic Differential Equations (Cambridge University Press, 2019). ↩¹

Nicholas Krämer and Philipp Hennig. Stable Implementation of Probabilistic ODE Solvers (2020), arXiv:2012.10106. ↩¹ ↩²

Michael Schober, Simo Särkkä and Philipp Hennig. A Probabilistic Model for the Numerical Solution of Initial Value Problems. Statistics and Computing 29, 99–122 (2019). ↩¹

Nathanael Bosch, Philipp Hennig and Filip Tronarp. Calibrated Adaptive Probabilistic ODE Solvers. In: International Conference on Artificial Intelligence and Statistics (AISTATS) (2021). ↩¹ ↩² ↩³

Mohan Wu and Martin Lysy. Data-Adaptive Probabilistic Likelihood Approximation for Ordinary Differential Equations (2023), arXiv:2306.05566. ↩¹ ↩²

Philipp Hennig, Michael A. Osborne and Hans P. Kersting. Probabilistic Numerics: Computation as Machine Learning (Cambridge University Press, 2022).

Hans Kersting and Philipp Hennig. Active Uncertainty Calibration in Bayesian ODE Solvers. In: Conference on Uncertainty in Artificial Intelligence (UAI) (2016).

Hans Kersting, T. J. Sullivan and Philipp Hennig. Convergence Rates of Gaussian ODE Filters. Statistics and Computing 30, 1791–1816 (2020).

Filip Tronarp, Simo Särkkä and Philipp Hennig. Bayesian ODE Solvers: The Maximum a Posteriori Estimate. Statistics and Computing 31, 23 (2021).