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Finding Symmetry Breaking Order Parameters with Euclidean Neural Networks | | Tess E. Smidt
; Mario Geiger
; Benjamin Kurt Miller
; | | Date: |
4 Jul 2020 | | Abstract: | Curie’s principle states that "when effects show certain asymmetry, this
asymmetry must be found in the causes that gave rise to them". We demonstrate
that symmetry equivariant neural networks uphold Curie’s principle and this
property can be used to uncover symmetry breaking order parameters necessary to
make input and output data symmetrically compatible. We prove these properties
mathematically and demonstrate them numerically by training a Euclidean
symmetry equivariant neural network to learn symmetry breaking input to deform
a square into a rectangle. | | Source: | arXiv, 2007.2005 | | Services: | Forum | Review | PDF | Favorites | |
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