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Evaluating Hyperparameter Selection Techniques for the Ratio-Coupled Loss Function

Authors:
Austin B. Schmidt
Pujan Pokhrel
Md Meftahul Ferdaus
Elias Ioup
Mahdi Abdelguerfi
Julian Simeonov

Keywords: Ratio-Coupled Loss; Surrogate Model; Hyperparameter Tuning; Cahn-Hilliard.

Abstract:
When forecasting fixed-location observation nodes with statistical surrogate models, combining datasets during training loss calculation has been shown to improve model accuracy. However, traditional methods for tuning the data ratio, such as grid search or random search, are computationally prohibitive. An alternative online methodology for optimizing the data during training has been previously investigated. While both approaches have been independently validated, they have never been directly compared to each other or to other search techniques. This paper presents a direct comparison to evaluate whether the online approach can serve as a viable replacement for conventional search methods. The Cahn-Hilliard physical equation provides a controlled testing environment for this analysis. The results show that the optimization algorithm may require additional improvements before an out-of-the-box approach is appropriate. However, using the derived optimal hyperparameter in an offline setup provides an improvement in accuracy, which implies the methodology is worthwhile when under time constraints.

Pages: 1 to 7

Copyright: Copyright (c) IARIA, 2025

Publication date: October 26, 2025

Published in: conference

ISBN: 978-1-68558-329-3

Location: Barcelona, Spain

Dates: from October 26, 2025 to October 30, 2025