DT² trains digital twins to preserve pairwise policy rankings from fitted Q-evaluation on offline data rather than minimizing one-step transition errors, improving policy ranking and reducing decision regret.
arXiv preprint arXiv:2202.03881 , year=
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LEADS is an LLM-agent framework that discovers hybrid models for cardiac EP digital twins by treating domain knowledge as an action space, outperforming human-designed and other LLM-based hybrids on synthetic and real data.
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$\text{DT}^2$: Decision-Targeted Digital Twins
DT² trains digital twins to preserve pairwise policy rankings from fitted Q-evaluation on offline data rather than minimizing one-step transition errors, improving policy ranking and reducing decision regret.
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Learning Cardiac Electrophysiology Digital Twins Through Agentic Discovery of Hybrid Structure
LEADS is an LLM-agent framework that discovers hybrid models for cardiac EP digital twins by treating domain knowledge as an action space, outperforming human-designed and other LLM-based hybrids on synthetic and real data.