Origins and mitigation of double descent in reduced order modeling
Latent low-dimensional structure in datasets of natural and engineered systems enables their sparse sensing, or full-state reconstruction from historical data and very few carefully chosen localized measurements. Depending on the reconstruction algorithm, sensor locations, and measurement noise, the reconstruction risk curves demonstrate a diversity of patterns including a dramatic peak in error known as double descent in Machine Learning literature. Here we explore those scenarios under a unified Data-Noise Averaging theory. Qualitatively, we formulate sufficient criteria for double descent to emerge through a catastrophic amplification of a pathological signal in reconstruction. Quantit...
| Authors | Andrei A. Klishin, J. Nathan Kutz, Krithika Manohar |
|---|---|
| Primary category | stat.ML |
| Published | 29 July 2026 |
| Last updated at source | 29 July 2026 |
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