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Poster
Scalable Variational Inference for Dynamical Systems
Nico S Gorbach · Stefan Bauer · Joachim M Buhmann

Mon Dec 04 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #2
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Gradient matching is a promising tool for learning parameters and state dynamics of ordinary differential equations. It is a grid free inference approach, which, for fully observable systems is at times competitive with numerical integration. However, for many real-world applications, only sparse observations are available or even unobserved variables are included in the model description. In these cases most gradient matching methods are difficult to apply or simply do not provide satisfactory results. That is why, despite the high computational cost, numerical integration is still the gold standard in many applications. Using an existing gradient matching approach, we propose a scalable variational inference framework which can infer states and parameters simultaneously, offers computational speedups, improved accuracy and works well even under model misspecifications in a partially observable system.

Keywords: Variational Inference · Dynamical Systems
[ Paper] 

Author Information

Nico S Gorbach (Swiss Federal Institute of Technology Zurich (ETHZ))
Stefan Bauer (ETH Zürich)
Joachim M Buhmann (ETH Zurich)

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