AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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The results indicate that physically meaningful molecular trajectories can emerge directly from physics-only supervision, supporting the feasibility of trajectory-unsupervised neural solvers for molecular dynamics.
A surrogate-recommendation framework is introduced that predicts the most suitable BO surrogate from inexpensive dataset characteristics and establishes FruBO as a reproducible, compute-aware baseline for Bayesian Optimization and provides practical guidance for surrogate selection under limited computational and experimental budgets.
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