Conformalized Physics-Informed Weak Regression for Parameter Estimation in Epidemic Models
Compartment models of infectious diseases are often linear in coefficients that are physical parameters or products or ratios of them. We exploit this with closed-form least squares and equip the estimates with distribution-free uncertainty. Physics-informed regression (PIR) fits the parameters to finite-difference derivatives of the data; we introduce physics-informed weak regression (PIWR), which integrates the model in time instead, needs no derivatives of the data, estimates the initial state jointly and is high-order accurate. To quantify uncertainty we wrap both estimators in split conformal prediction, with a calibration population generated by the physics-based model itself, givin...
| Authors | Allan Peter Engsig-Karup |
|---|---|
| Primary category | stat.ML |
| Published | 13 October 2026 |
| Last updated at source | 13 October 2026 |
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