AI RESEARCH
On the Equivariant Learning of the $Q$-tensor Order Parameter
arXiv CS.LG
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ArXi:2605.27679v1 Announce Type: cross We construct and evaluate group-equivariant neural networks for the prediction of the two-dimensional $Q$-tensor order parameter of nematic liquid crystals from synthetically generated microscopic textures. Seven architectures, equivariant to cyclic groups $C_k$ of order $k$ for $k=4,\,8,\,16,\,32,\,64,\,128,\, 256$, are built using a combination of weight-sharing constraints, equivariant activations and regularization techniques.