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.
H., Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence, MIT press
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.NE 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
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.