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It is shown that false-science induction arises when legitimate physical objects and measurements are paired incorrectly, driving neural surrogates to faithfully learn record-induced associations that do not correspond to the true object-outcome relationship while marginal data distributions remain unchanged.
Cross-parameter relinking is introduced, a data-poisoning primitive that makes a triggered input select a valid solution from the same PDE family under an incorrect physical parameter.
This framework enables the reliable and highly efficient deployment of scientific surrogates without requiring ground-truth data and establishes computable sufficient conditions that rigorously certify exact decisions for strongly monotone discretizations.
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