Kavli Affiliate: Tim H. Taminiau | Summary:High-throughput characterization often requires estimating parameters and model dimension from experimental data of limited quantity and quality. Such data may result in an ill-posed inverse problem, where multiple sets of parameters and model dimensions are consistent with available data. This ill-posed regime may render traditional machine learning and deterministic […]
Continue.. Trans-dimensional Hamiltonian model selection and parameter estimation from sparse, noisy data