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physics.comp-ph2020
Discovering Symmetry Invariants and Conserved Quantities by Interpreting Siamese Neural Networks
Sebastian J. Wetzel, Roger G. Melko, Joseph Scott +2
In this paper, we introduce interpretable Siamese Neural Networks (SNN) for similarity detection to the field of theoretical physics. More precisely, we apply SNNs to events in spe…
physics.comp-ph2019
Spectral Reconstruction with Deep Neural Networks
Lukas Kades, Jan M. Pawlowski, Alexander Rothkopf +5
We explore artificial neural networks as a tool for the reconstruction of spectral functions from imaginary time Green's functions, a classic ill-conditioned inverse problem. Our a…