PINN Modeling of Interfacial Gravity-Capillary Waves
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Date
2025
Authors
Avramenko, Olha
Sontikov, Maksym
Journal Title
Journal ISSN
Volume Title
Publisher
Національний університет "Києво-Могилянська академія"
Abstract
This paper presents an automated computational framework for modeling hydrodynamic processes using physics-informed neural networks (PINNs). The modular system integrates all stages of numerical experimentation — from data generation and model training to validation and accuracy evaluation — ensuring reproducibility, flexibility, and scalability. The framework was verified on the classical problem of interfacial gravity–capillary waves between two incompressible fluids, using the analytical solution as a benchmark for numerical assessment. Computational experiments showed that increasing the number of training points from 400 to 1000 improved accuracy and convergence, with the Extended configuration achieving 98.86% accuracy and a MAPE of 1.14%, while Adaptive_LR remained stable. The results confirm the reliability and efficiency of the proposed PINN-based framework for solving complex hydrodynamic problems governed by nonlinear partial differential equations.
Description
Keywords
Physics-informed neural networks (PINNs), automated computational framework, adaptive learning rate, nonlinear partial differential equation, conference materials
Citation
Avramenko O. PINN Modeling of Interfacial Gravity-Capillary Waves / Avramenko O., Sontikov M. // Теоретичні та прикладні аспекти побудови програмних систем : праці 16 Міжнародної науково-практичної конференції, 23-24 листопада 2025 року, Київ / [за заг. ред. М. М. Глибовця, Т. В. Панченка та ін. ; Факультет інформатики Національного університету "Києво-Могилянська академія" та ін.]. - Київ : НаУКМА, 2025. - С. 29-31.