Multiple correct answers allow an AI to learn just as much from 20 times less data.
Physical embedding uses non-identifiable parameters as a resource to improve how signals are reconstructed. Scientists usually consider it a failure when multiple parameter combinations produce the same result, but this method uses that ambiguity to increase stability. This approach reduces the amount of training data needed by a factor of 20 while maintaining accuracy. It allows for the creation of high-quality physical models in fields where data is extremely expensive to collect. The flaw of uncertainty actually becomes the key to more efficient machine learning.
Discovery of unobservable parameters via physical embedding
arXiv · 2604.15615
Recovering a source signal from indirect measurements often requires estimating latent parameters, such as wireless channel states or MRI coil sensitivities, that cannot be directly observed. Here, we introduce Physics-Embedded Inverse Learning (PEIL), in which a learned estimator predicts these parameters and a fixed, physics-based inverse operator uses them to reconstruct the signal, so that training requires only the source signal as supervision. In systems where multiple parameter combinatio