Neural surrogate models enable 785x faster optimization of glider wings with stability constraints using multiple algorithms including PSO, GA, and Bayesian optimization, yielding improved aerodynamic qualities.
Radial basis function surrogates for uncertainty quantification and aerody- namic shape optimization under uncertainties
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Neural Surrogate-assisted Glider Wing Design with Stability Analysis and Multi-method Optimization
Neural surrogate models enable 785x faster optimization of glider wings with stability constraints using multiple algorithms including PSO, GA, and Bayesian optimization, yielding improved aerodynamic qualities.