Discovery of unobservable parameters via physical embedding
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ID: 325539
2026
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Abstract
Abstract Recovering a source signal from indirect measurements often depends on parameters that cannot be observed directly, such as wireless channel states or MRI coil sensitivities. We introduce Physics-Embedded Inverse Learning (PEIL), which embeds a fixed, differentiable inverse solver and uses signal recovery to supervise their estimation without parameter labels. PEIL makes parameter selection task-optimal: each setting is judged by the reconstruction it produces through the fixed solver, rather than by its agreement with nominal labels. In locally non-identifiable regimes, different settings yield nearly equivalent reconstructions, allowing the loss to favour those better suited to the fixed solver. Non-identifiability thus becomes a resource for recovery. In high-mobility wireless communications, PEIL generalises to unseen channel profiles and velocities without retraining and reaches comparable recovery performance using 20-fold fewer training samples than baselines trained with parameter labels. At high SNR, it also achieves a lower symbol error rate than an oracle-reference least-squares baseline using true pilot-channel values and fixed interpolation. In parallel MRI, without sensitivity-map labels or a separate calibration step, PEIL discovers stable coil sensitivity maps with magnitude structure consistent with a calibration reference and reconstructs anatomically faithful images. Together, these results show that a fixed inverse solver can make unobservable parameters learnable from signal recovery.
| Reference Key |
openalex_W7154952122
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|---|---|
| Authors | Le Cheng, X Y Liu, Lingjin Kong, Haitao Zhao, Jun Xiong, Fanglin Gu, Xiaoying Zhang, Baoquan Ren, Jibo Wei, Hao Yin |
| Journal | national science review |
| Year | 2026 |
| DOI |
10.1093/nsr/nwag508
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| URL | |
| Keywords | Keywords not found |
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