# Block sparsity and gauge mediated weight sharing for learning dynamical laws from data

@inproceedings{Gotte2022BlockSA, title={Block sparsity and gauge mediated weight sharing for learning dynamical laws from data}, author={Martin Gotte and Jan Fuksa and Ingo Roth and Jens Eisert}, year={2022} }

Recent years have witnessed an increased interest in recovering dynamical laws of complex systems in a largely data-driven fashion under meaningful hypotheses. In this work, we propose a method for scalably learning dynamical laws of classical dynamical systems from data. As a novel ingredient, to achieve an efﬁcient scaling with the system size, block sparse tensor trains – instances of tensor networks applied to function dictionaries – are used and the self similarity of the problem is…

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