Forward-operator geometry analysis shows identifiability limits in ultrasonic models arise from parameter coupling, anisotropic sensitivity, and stochastic variability, with combined observables improving conditioning.
Autonomouscharacterizationofgrainsizedistributionusingnonlinearlambwaves based on deep learning.The Journal of the Acoustical Society of America, 152(3):1913–1921
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Identifiability Limits in Ultrasonic Microstructure Characterisation: A Canonical and Stochastic Framework
Forward-operator geometry analysis shows identifiability limits in ultrasonic models arise from parameter coupling, anisotropic sensitivity, and stochastic variability, with combined observables improving conditioning.