2 citations · 4 across the 3 of their papers we have counts for
5 papers
Distributional Gradient Matching for Learning Uncertain Neural Dynamics Models
Lenart Treven, Philippe Wenk, Florian Dörfler +1
Differential equations in general and neural ODEs in particular are an essential technique in continuous-time system identification. While many deterministic learning algorithms ha…
SLEIPNIR: Deterministic and Provably Accurate Feature Expansion for Gaussian Process Regression with Derivatives
Emmanouil Angelis, Philippe Wenk, Bernhard Schölkopf +2
Gaussian processes are an important regression tool with excellent analytic properties which allow for direct integration of derivative observations. However, vanilla GP methods sc…
AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs
Gabriele Abbati, Philippe Wenk, Michael A Osborne +3
Stochastic differential equations are an important modeling class in many disciplines. Consequently, there exist many methods relying on various discretization and numerical integr…
ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems
Philippe Wenk, Gabriele Abbati, Michael A Osborne +3
Parameter inference in ordinary differential equations is an important problem in many applied sciences and in engineering, especially in a data-scarce setting. In this work, we in…
Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEs
Philippe Wenk, Alkis Gotovos, Stefan Bauer +3
Parameter identification and comparison of dynamical systems is a challenging task in many fields. Bayesian approaches based on Gaussian process regression over time-series data ha…