7 citations · 22 across the 7 of their papers we have counts for
3 papers · 2 filters
Probabilistic ODE Solutions in Millions of Dimensions
Nicholas Krämer, Nathanael Bosch, Jonathan Schmidt +1
Probabilistic solvers for ordinary differential equations (ODEs) have emerged as an efficient framework for uncertainty quantification and inference on dynamical systems. In this w…
Linear-Time Probabilistic Solutions of Boundary Value Problems
Nicholas Krämer, Philipp Hennig
We propose a fast algorithm for the probabilistic solution of boundary value problems (BVPs), which are ordinary differential equations subject to boundary conditions. In contrast…
A Probabilistic State Space Model for Joint Inference from Differential Equations and Data
Jonathan Schmidt, Nicholas Krämer, Philipp Hennig
Mechanistic models with differential equations are a key component of scientific applications of machine learning. Inference in such models is usually computationally demanding, be…