AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Numerical site-response predictions often deviate from observations, yet correcting these discrepancies is difficult because records are limited in both sensor coverage and number of events. This study proposes the Transfer-Enabled Forced Latent Autoencoder for Response Equations (FLARE-T) to improve these predictions...
FLARE is a forced latent autoencoder for response equations that learns compact response coordinates, identifies sparse input-dependent latent dynamics and decodes equation rollouts to full responses, and predicts long-horizon high-dimensional responses under inputs not used for training.
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