A Probabilistic State Space Model for Joint Inference from Differential Equations and Data. - Jonathan Schmidt 001; Nicholas Krämer; Philipp Hennig (NeurIPS, 2021)

ProbNum: Probabilistic Numerics in Python. - Jonathan Wenger; Nicholas Krämer; Marvin Pförtner; Jonathan Schmidt 001; Nathanael Bosch; Nina Effenberger; Johannes Zenn; Alexandra Gessner; Toni Karvonen; François-Xavier Briol; Maren Mahsereci; Philipp Hennig (CoRR, 2021)

Probabilistic Numerical Method of Lines for Time-Dependent Partial Differential Equations. - Nicholas Krämer; Jonathan Schmidt 001; Philipp Hennig (AISTATS, 2022)

Probabilistic ODE Solutions in Millions of Dimensions. - Nicholas Krämer; Nathanael Bosch; Jonathan Schmidt 001; Philipp Hennig (ICML, 2022)

The Rank-Reduced Kalman Filter: Approximate Dynamical-Low-Rank Filtering In High Dimensions. - Jonathan Schmidt 001; Philipp Hennig; Jörg Nick; Filip Tronarp (NeurIPS, 2023)

Probabilistic ODE solvers for integration error-aware numerical optimal control. - Amon Lahr; Filip Tronarp; Nathanael Bosch; Jonathan Schmidt 001; Philipp Hennig; Melanie N. Zeilinger (L4DC, 2024)

Spatiotemporally Coherent Probabilistic Generation of Weather from Climate. - Jonathan Schmidt 001; Luca Schmidt; Felix Strnad; Nicole Ludwig 002; Philipp Hennig (CoRR, 2024)