One-shot backpropagation for multi-step prediction in physics-based system identification

The aim of this paper is to present a novel general framework for the identification of possibly interconnected systems, while preserving their physical properties and providing accuracy in multi-step prediction. An analytical and recursive algorithm for the gradient computation of the multi-step loss function based on backpropagation is introduced, providing physical and structural insight directly into the learning algorithm. As a case study, the proposed approach is tested for estimating the inertia matrix of a space debris starting from state observations.