Introductory Notes on Learning$^2$
Although machine learning can be used to predict the evolution of physical systems from data, a formulation that learns only the system state at each time leaves the temporal and dynamical structure of the solution to be resolved within a broad hypothesis space. We introduce Learning$^2$, a representation-level framework that structures this space by coupling a primary representation to a second representation through a known physical transformation. The resulting cross-representation constraint restricts the effective hypothesis space and provides an ante-hoc, physically interpretable criterion for excluding solutions that satisfy the primary representation alone. We instantiate Learning...
| Authors | Sai Siddharth, Maniarasu Ravi |
|---|---|
| Primary category | math.NA |
| Published | 6 September 2026 |
| Last updated at source | 6 September 2026 |
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