In this paper we present a novel scientific machine learning reinterpretation of the well-known Kalman Filter, we explain its flexibility in dealing with partially-unknown models and show its effectiveness in a couple of situations where the classic Kalman Filter is problematic.

State Estimation of Partially Unknown Dynamical Systems with a Deep Kalman Filter

Chinellato E.;Marcuzzi F.
2024

Abstract

In this paper we present a novel scientific machine learning reinterpretation of the well-known Kalman Filter, we explain its flexibility in dealing with partially-unknown models and show its effectiveness in a couple of situations where the classic Kalman Filter is problematic.
2024
Computational Science – ICCS 2024
International Conference on Computational Science (ICCS) 2024
9783031637742
9783031637759
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3532161
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