7 citations · 15 across the 6 of their papers we have counts for
6 papers
Learning Stable Deep Dynamics Models for Partially Observed or Delayed Dynamical Systems
Andreas Schlaginhaufen, Philippe Wenk, Andreas Krause +1
Learning how complex dynamical systems evolve over time is a key challenge in system identification. For safety critical systems, it is often crucial that the learned model is guar…
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…