Multifidelity linear regression for scientific machine learning from scarce data

arXiv:2403.08627v1 Announce Type: cross
Abstract: Machine learning (ML) methods, which fit to data the parameters of a given parameterized model class, have garnered significant interest as potential methods for learning surrogate models for complex engineering systems for which traditional simulation is expensive. However, in many scientific and engineering settings, generating high-fidelity data on which to train ML models is expensive, and the available budget for generating training data is limited. ML models trained on the resulting scarce high-fidelity data have …

abstract arxiv class cs.ce cs.lg data engineering however linear linear regression machine machine learning parameters regression simulation stat.ml systems type