ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems
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Publication:6314171
arXiv1902.06278MaRDI QIDQ6314171
Author name not available (Why is that?)
Publication date: 17 February 2019
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.
Has companion code repository: https://github.com/sdi1100041/SLEIPNIR
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