AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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High-fidelity simulations of time-dependent partial differential equations (PDEs) are computationally expensive, motivating data-driven reduced-order surrogates for many-query tasks such as uncertainty quantification, design optimization, data assimilation, and optimal control. However, existing surrogate models often...
Deep Koopman Sensing is proposed, a data-driven reduced-order DA framework that combines a nonlinear autoencoder with parameter-conditioned linear latent dynamics approximating the Koopman operator, and shows that latent dynamics should be designed for the downstream estimation task rather than selected solely for fore...
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