AI RESEARCH
Machine Generalize Learning in Agent-Based Models: Going Beyond Surrogate Models for Calibration in ABMs
arXiv CS.LG
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ArXi:2509.07013v2 Announce Type: replace Calibrating agent-based epidemic models is computationally demanding. We present a supervised machine learning calibrator that learns the inverse mapping from epidemic time series to SIR parameters. A three-layer bidirectional LSTM ingests 60-day incidence together with population size and recovery rate, and outputs transmission probability, contact rate, and R0