A quantum inspired approach to learning dynamical laws from data—block-sparsity and gauge-mediated weight sharing
J Fuksa,
M Götte,
I Roth
et al.
Abstract: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 scalable and numerically robust method for this task, utilizing efficient block-sparse tensor train representations of dynamical laws, inspired by similar approaches in quantum many-body systems. Low-rank tensor train representations have been previously derived for dynamical laws of one-dimensional systems. We extend this resu… Show more
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