7 citations · 15 across the 6 of their papers we have counts for
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cs.LG2021★ 4 cited
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…
cs.LG2021★ 1 cited
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…