Floating-Body Hydrodynamic Neural Networks

• URL: http://arxiv.org/abs/2509.13783v1
• Date: Wed, 17 Sep 2025 07:51:35 GMT
• Title: Floating-Body Hydrodynamic Neural Networks
• Authors: Tianshuo Zhang, Wenzhe Zhai, Rui Yann, Jia Gao, He Cao, Xianglei Xing,
• Abstract summary: We propose a physics-structured framework that predicts interpretable parameters such as directional added masses, drag coefficients, and a streamfunction-based flow, and couples them with analytic equations of motion.<n>Compared with Hamiltonian and Lagrangian neural networks, FHNN more effectively handles dissipative dynamics while preserving interpretability, which bridges the gap between black-box learning and transparent system identification.
• Score: 8.501171043928354
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fluid-structure interaction is common in engineering and natural systems, where floating-body motion is governed by added mass, drag, and background flows. Modeling these dissipative dynamics is difficult: black-box neural models regress state derivatives with limited interpretability and unstable long-horizon predictions. We propose Floating-Body Hydrodynamic Neural Networks (FHNN), a physics-structured framework that predicts interpretable hydrodynamic parameters such as directional added masses, drag coefficients, and a streamfunction-based flow, and couples them with analytic equations of motion. This design constrains the hypothesis space, enhances interpretability, and stabilizes integration. On synthetic vortex datasets, FHNN achieves up to an order-of-magnitude lower error than Neural ODEs, recovers physically consistent flow fields. Compared with Hamiltonian and Lagrangian neural networks, FHNN more effectively handles dissipative dynamics while preserving interpretability, which bridges the gap between black-box learning and transparent system identification.

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