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.
Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces.Journal of Global Optimization, 11(4):341–359
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NUBO is a transparent, modular Python package implementing established Bayesian optimization techniques for bounded, constrained, and mixed input spaces.
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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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NUBO: A Transparent Python Package for Bayesian Optimization
NUBO is a transparent, modular Python package implementing established Bayesian optimization techniques for bounded, constrained, and mixed input spaces.