Trajectory Translator for Amortized Field Reconstructors under Observation-Interface Shift

Amortized field reconstruction models learn to infer complete physical fields from partial observations under the sensing configurations represented during training. When training is restricted to a fixed sensing trajectory, the learned reconstruction map may become specialized to its observation interface. At deployment, however, novel or adaptively selected trajectories can introduce observation-interface distribution shift, constituting an out-of-distribution (OOD) setting. We introduce Trajectory Translator (TT), an add-on module that maps deployment observations to virtual observations on the original observation interface, guiding field reconstruction without modifying the frozen reconstructor. Under a finite-state Bayesian formulation, we characterize when bounded virtual observations preserve deployment evidence exactly. For deterministic reconstructors, we relate squared reconstruction risk to approximation of the conditional mean and evaluate TT through empirical reconstruction errors. Experiments across physical-field benchmarks show that TT improves reconstruction under the evaluated observation-interface shifts while keeping the underlying reconstructors frozen, reducing relative reconstruction error by up to 88.9% for the Transformer on the plate-strain benchmark. These results demonstrate that virtual observations on the original interface can effectively steer a frozen reconstructor toward more accurate field estimates. At deployment, TT translates each observation sequence or block in a single forward pass. In the measured offline settings, TT adds only 0.39–0.57 ms per complete sequence, introducing little additional computational overhead. Code is available at https://anonymous.4open.science/r/Trajectory-Translator-3D38/.