Two UQ methods, intrusive PCE and non-intrusive Wasserstein-based sensitivity, are developed to optimize excitations for better parameter identification and demonstrated on vehicle models with experimental validation.
SciPy 1.0: fundamental algorithms for scientific computing in Python.Nature Methods, 17(3):261–272
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Hashin-Shtrikman bounds under-predict taste in 77% of cases; a hybrid model with eight chemistry proxies reduces error 27-62% and enables constrained inverse design of recipes.
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Uncertainty Quantification Methods for Optimal Excitation Design in Parameter Identification
Two UQ methods, intrusive PCE and non-intrusive Wasserstein-based sensitivity, are developed to optimize excitations for better parameter identification and demonstrated on vehicle models with experimental validation.
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Predicting food taste with bound-driven optimization
Hashin-Shtrikman bounds under-predict taste in 77% of cases; a hybrid model with eight chemistry proxies reduces error 27-62% and enables constrained inverse design of recipes.