Code_BayesianLC.zip from Bayesian parameter identification in the Landau–de Gennes theory for nematic liquid crystals

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.