Effective Training Principles of Physical Reservoirs
Reservoir computers benefit from the inherent complexity of optical phenomena, which provide rich, often nonlinear dynamics. However, training directly on the reservoir's output renders the system prone to overfitting and computationally inefficient during the training phase. In this work, we investigate strategies to mitigate overfitting and reduce computational overhead through output pruning and regularization. We compare loss-minimizing search methods (Equal Search and Branch and Bound) against an output-oriented statistical filtering approach (Variance Filter) and random pruning, highlighting advantages and disadvantages of each approach and the overall importance of informed reservo...
| Authors | Sobhi Saeed, Mehmet Müftüoglu, Glitta R. Cheeran et al. |
|---|---|
| Primary category | physics.optics |
| Published | 8 June 2026 |
| Last updated at source | 8 June 2026 |
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