Data Poisoning Can Slip a "Wrong-Physics" Backdoor into Neural PDE Solvers
Reusable solver archives are becoming standard training data for neural PDE operators, so researchers probed what happens when that data is tampered with. They introduce cross-parameter relinking, a poisoning primitive that makes a triggered input trigger a valid but physically wrong solution from the same PDE family. Across 476 campaigns, Fourier Neural Operators hit a 1.0000 backdoor success rate on advection-diffusion and 2D Navier-Stokes while keeping clean error low. Clean-error checks and smoothness tests don't catch it, exposing a structural validation gap.