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One-shot backpropagation for multi-step prediction in physics-based system identification -- EXTENDED VERSION
Cesare Donati, Martina Mammarella, Fabrizio Dabbene, Carlo Novar · 2023-10-31 · via cs.LG updates on arXiv.org

The aim of this paper is to present a novel physics-based framework for the identification of dynamical systems, in which the physical and structural insights are reflected directly into a backpropagation-based learning algorithm. The main result is a method to compute in closed form the gradient of a multi-step loss function, while enforcing physical properties and constraints. The derived algorithm has been exploited to identify the unknown inertia matrix of a space debris, and the results show the reliability of the method in capturing the physical adherence of the estimated parameters.

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