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arXiv Machine Learning By Allan Peter Engsig-Karup

Conformalized Physics-Informed Weak Regression for Parameter Estimation in Epidemic Models

Read the original on arXiv Machine Learning →

The paper introduces physics-informed weak regression (PIWR) for estimating parameters in compartmental epidemic models, avoiding the need for data derivatives and jointly estimating initial states. PIWR, combined with split conformal prediction, provides distribution‑free uncertainty intervals with finite‑sample coverage guarantees. Across eleven benchmark models and real influenza outbreak data, PIWR achieves orders of magnitude higher accuracy than existing physics‑informed neural networks while delivering calibrated confidence bands in under two minutes per fit.

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