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Estimation for Compositional Data using Measurements from Nonlinear Systems using Artificial Neural Networks
Our objective is to estimate the unknown compositional input from its output response through an unknown system after estimating the inverse of the original system with a training set.
A Learning Theory in Linear Systems under Compositional Models
We present a learning theory for the training of a linear system operator having an input compositional variable and propose a Bayesian inversion method for inferring the unknown variable from an output of a noisy linear system.
A Dictionary Approach to EBSD Indexing
We discretize the domain of the forward model onto a dense grid of Euler angles and for each measured pattern we identify the most similar patterns in the dictionary.
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