Bayesian Parameter Identification in the Landau-de Gennes Theory for Nematic Liquid Crystals
arXiv:2504.16029 · doi:10.1098/rspa.2025.0355
Abstract
This manuscript establishes a pathway to reconstruct material parameters from measurements within the Landau-de Gennes model for nematic liquid crystals. We present a Bayesian approach to this inverse problem and analyse its properties using given, simulated data for benchmark problems of a planar bistable nematic device. In particular, we discuss the accuracy of the Markov chain Monte Carlo approximations, confidence intervals and the limits of identifiability.
25 pages
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