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Estimation for Compositional Data using Measurements from Nonlinear Systems using Artificial Neural Networks
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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. The proposed methods using artificial neural networks (ANNs) can compete with the optimal bounds for linear systems, where convex optimization theory applies, and demonstrate promising results for nonlinear system inversions. We performed extensive experiments by designing numerous different types of nonlinear systems.
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Cited by 1 Pith paper
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MCMC-Net: Accelerating Markov Chain Monte Carlo with Neural Networks for Inverse Problems
A CNN surrogate for the forward model is used inside pCN-MCMC to accelerate Bayesian inversion for EIT, DOT, and QPAT, with accuracy that sometimes falls below the FEM baseline.
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