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Given observations of a physical system, identifying the underlying non-linear governing equation is a fundamental task, necessary both for gaining understanding and generating deterministic future predictions.
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Approximate solutions for the bilinear form computational problem
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Density matrix formulation for quantum renormalization groups
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Schuch, N., Wolf, M. M., Verstraete, F., and Cirac, J. I · 2008
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Matrix product states, projected entangled pair states, and variational renormalization group methods for quantum spin systems
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Distilling free-form natural laws from experimental data
Schmidt, M. and Lipson, H · 2009
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Cohen, N., Sharir, O., and Shashua, A · 2015
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Tensor completion in hierarchical tensor representations
Rauhut, H., Schneider, R., and Stojanac, Z · 2015
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A data–driven approximation of the Koopman operator: Extending dynamic mode decomposition
Williams, M. O., Kevrekidis, I. G., and Rowley, C. W · 2015
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Discovering governing equations from data by sparse identification of nonlinear dynamical systems
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A programming guide for tensor networks with global su(2) symmetry
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Data-driven discovery of coordinates and governing equations
Champion, K., Lusch, B., Kutz, J. N., and Brunton, S. L · 2019
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Multidimensional approximation of non-linear dynamical systems
Gelß, P., Klus, S., Eisert, J., and Schütte, C · 2019
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Stable ALS approximation in the TT-format for rank-adaptive tensor completion
Grasedyck, L. and Krämer, S · 2019
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Tensor-based EDMD for the Koopman analysis of high-dimensional systems
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Low rank tensor decompositions for high dimensional data approximation, recovery and prediction
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https://github.com/RoteKekse/systemrecovery , 2020
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