Avramenko, OlhaSontikov, Maksym2026-02-112026-02-112025Avramenko 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.https://ekmair.ukma.edu.ua/handle/123456789/38312This 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.enPhysics-informed neural networks (PINNs)automated computational frameworkadaptive learning ratenonlinear partial differential equationconference materialsConference materials