Nanang Susyanto, Utti Marina Rifanti, Jayrold P. Arcede, Dedi Rohendi, Yudi Soeharyadi, Jalina Widjaja, Siti Fatimah, Kartika Yulianti, Sofihara Al Hazmy
Physics-informed neural networks (PINNs) offer a promising approach for epidemic modeling by integrating mechanistic disease dynamics with flexible function approximation. Yet, hyperparameter choices — especially physics regularization weights — remain largely heuristic. This study develops a systematic data-adaptive approach for hyperparameter selection within a SUC-PINNs (Susceptible–Unconfirmed–Confirmed PINNs) applied to COVID-19 data from six countries. Synthetic experiments confirmed methodological validity, with parameter recovery errors ranging from 3.4% to 26.9% and all true values captured by bootstrap confidence intervals, but hyperparameters optimal for synthetic data did not generalize to real-world conditions, underscoring the need for data-adaptive tuning.A comprehensive grid search revealed substantial cross-country heterogeneity, with optimal physics regularization varying by a factor of four hundred, reflecting differing epidemic complexities. Higher regularization improved parameter stability but introduced accuracy loss when excessive, although estimated reproduction numbers remained consistent across settings. Beyond COVID-19, the findings highlight the broader importance of data-adaptive hyperparameter selection for applying PINNs to diverse infectious disease systems. The results provide practical guidance for regularization selection, favoring lower regularization for moderate-quality data, stronger regularization for complex dynamics, and bootstrap procedures for uncertainty quantification. © 2026 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license. http://creativecommons.org/licenses/by/4.0/
Universitas Gadjah Mada, Yogyakarta, Indonesia; Caraga State University, Butuan City, Philippines; Universitas Pendidikan Indonesia, Bandung, Indonesia; Institut Teknologi Bandung, Bandung, Indonesia
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