Optimal Linear Baseline Models for Scientific Machine Learning

This project develops optimal linear baseline models for benchmarking scientific machine learning architectures. The goal is to provide principled rank-constrained encoder-decoder maps that make it easier to understand when more expressive learned models are providing real value.

The preprint is available on arXiv. Links to the journal version and any related project materials will be added here as they become available.