11 citations · 13 across the 4 of their papers we have counts for
4 papers
A quantum inspired approach to learning dynamical laws from data -- block-sparsity and gauge-mediated weight sharing
J. Fuksa, M. Götte, I. Roth +1
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 pro…
A block-sparse Tensor Train Format for sample-efficient high-dimensional Polynomial Regression
Michael Götte, Reinhold Schneider, Philipp Trunschke
Low-rank tensors are an established framework for high-dimensional least-squares problems. We propose to extend this framework by including the concept of block-sparsity. In the co…
Particle Number Conservation and Block Structures in Matrix Product States
Markus Bachmayr, Michael Götte, Max Pfeffer
The eigenvectors of the particle number operator in second quantization are characterized by the block sparsity of their matrix product state representations. This is shown to gene…
Tensor network approaches for learning non-linear dynamical laws
A. Goeßmann, M. Götte, I. Roth +3
Given observations of a physical system, identifying the underlying non-linear governing equation is a fundamental task, necessary both for gaining understanding and generating det…