Work

Artificial-viscosity schedules for Buckley–Leverett PINNs: code and reproducibility archive

Year: 2026

Type: software

Abstract: Software and experimental evidence accompanying Separating Artificial-Viscosity Annealing from the L-BFGS Viscosity in Physics-Informed Neural Networks for Two-Phase Flow. Includes training configurations, saved model weights, predictions, per-run measurements, finite-volume reference fields, and scripts to reconstruct all 15 figures and 18 tables. Separate exploratory open-outlet and boundary-conditioned experiment blocks use 15 paired seeds. Fixed-seed GPU training is not bitwise reproducible; the archive records the measured repeatability limitation. Funded by the Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan, grant AP23489431.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.22926131

Authors: Mukhambetzhanov, Saltanbek, Kenzhebek, Yerzhan, Bekele, Samson Dawit, Tastanova, Saida, Lemma, Dagmawi, Imankulov, Timur

Institutions: Al-Farabi Kazakh National University, Tashkent University of Information Technology, Addis Ababa University

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7214096921

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