AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Policy gradient methods for on-policy reinforcement learning (RL) can become unstable when expensive, scarce target-domain data yield noisy gradient estimates. We address this challenge by complementing limited high-fidelity (HF) target-domain data with abundant, cheap, but biased low-fidelity (LF) data, e.g., from a s...
This work establishes a probabilistic version of parametric operator inference, casting the learning problem as a Bayesian linear regression, and designs a sequential adaptive sampling scheme to select new training parameter vectors that promote ROM stability and accuracy globally in the parameter domain.
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