A supervised neural network classifies 1- vs 2- vs 3-cavity microwave wave-chaotic systems from raw impedance spectra with up to 100% held-out accuracy, but the recurrent-network 'forecast' is tested on quasi-periodic repeats of training conditions.
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Classification and prediction of wave chaotic systems with machine learning techniques
A supervised neural network classifies 1- vs 2- vs 3-cavity microwave wave-chaotic systems from raw impedance spectra with up to 100% held-out accuracy, but the recurrent-network 'forecast' is tested on quasi-periodic repeats of training conditions.