AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
We have 2 of 12 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
LatentDDM is identified as a promising design principle for physical foundation models that improves 20-step field rollouts in fast-pitching airfoil flow, both zero-shot and after few-shot calibration.
A factorized latent-conditioning formulation is introduced that jointly learns a neural operator and a low-dimensional latent representation through factorized prediction, trajectory-decoupled sampling, and dimension selection that enables generalization to previously unseen system instances.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.