Computing stable configurations of confined smectic liquid crystals with a deep variational framework
Read the original on arXiv Machine Learning →The paper introduces a deep variational framework (DVF) for computing stable configurations of confined smectic liquid crystals using a modified Landau–de Gennes model. By representing orientational and positional order parameters on a regular reference domain and incorporating physical confinement through coordinate mappings, the DVF overcomes spectral bias with a warmup penalty, enabling robust recovery of oscillatory smectic states. The method reproduces known smectic‑A defect structures, predicts new layer morphologies in various confinement geometries, and even forecasts a chevron‑like smectic‑C state on a tangent‑anchored sphere.
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