ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems
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Author / Producer
Date
2020
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
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 introduce a novel generative modeling approach based on constrained Gaussian processes and leverage it to build a computationally and data efficient algorithm for state and parameter inference. In an extensive set of experiments, our approach outperforms the current state of the art for parameter inference both in terms of accuracy and computational cost. It also shows promising results for the much more challenging problem of model selection.
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Publication status
published
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Editor
Book title
Volume
34 (04)
Pages / Article No.
6364 - 6371
Publisher
AAAI
Event
34th AAAI Conference on Artificial Intelligence (AAAI 2020)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
03908 - Krause, Andreas / Krause, Andreas
09664 - Schölkopf, Bernhard / Schölkopf, Bernhard
02661 - Institut für Maschinelles Lernen / Institute for Machine Learning
02150 - Dep. Informatik / Dep. of Computer Science
Notes
Funding
815943 - Reliable Data-Driven Decision Making in Cyber-Physical Systems (EC)