Code for ensemble ranking-based adaptive sampling. This repository contains the code implementation for the paper:
Optimizing Adaptive Sampling via Policy Ranking
(arXiv:2410.15259v1 [q-bio.BM], October 20, 2024)
This work introduces a modular framework for adaptive sampling, utilizing metric-driven ranking to dynamically identify the most effective sampling policies. By systematically evaluating and ranking an ensemble of policies, this approach enables policy switching across rounds, significantly improving convergence speed and sampling performance.
- Adaptive Sampling Ensemble: Dynamically selects the optimal policy based on real-time data.
- Improved Efficiency: Outperforms single-policy sampling by exploring conformational space more effectively.
- Versatility: Integrates any adaptive sampling policy, making it highly flexible and modular.
- On-the-Fly Algorithms: Includes novel algorithms to approximate ranking and decision-making during simulations.
Coming soon...