Differentiable hybrid neural modeling for fluid-structure interaction

2023/03/23 by Xiantao Fan, Jianxun Wang, Fan, Xiantao +1 · 3 citations
Engineering · Physics and Astronomy · #Computational Engineering #Computational Physics (physics.comp-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Finance #Fluid Dynamics and Vibration Analysis #Lattice Boltzmann Simulation Studies #Model Reduction and Neural Networks #and Science (cs.CE)

paper · pdf · doi:10.48550/arxiv.2303.12971

openalex publication_date 2023/03/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Solving complex fluid-structure interaction (FSI) problems, which are described by nonlinear partial differential equations, is crucial in various scientific and engineering applications. Traditional computational fluid dynamics based solvers are inadequate to handle the increasing demand for large-scale and long-period simulations. The ever-increasing availability of data and rapid advancement in deep learning (DL) have opened new avenues to tackle these challenges through data-enabled modeling. The seamless integration of DL and classic numerical techniques through the differentiable programming framework can significantly improve data-driven modeling performance. In this study, we propose a differentiable hybrid neural modeling framework for efficient simulation of FSI problems, where the numerically discretized FSI physics based on the immersed boundary method is seamlessly integrated with sequential neural networks using differentiable programming. All modules are programmed in JAX, where automatic differentiation enables gradient back-propagation over the entire model rollout trajectory, allowing the hybrid neural FSI model to be trained as a whole in an end-to-end, sequence-to-sequence manner. Through several FSI benchmark cases, we demonstrate the merit and capability of the proposed method in modeling FSI dynamics for both rigid and flexible bodies. The proposed model has also demonstrated its superiority over baseline purely data-driven neural models, weakly-coupled hybrid neural models, and purely numerical FSI solvers in terms of accuracy, robustness, and generalizability.

Cited by

• JAX-AMG: A GPU-Accelerated Differentiable Sparse Linear Solver Library for JAX
• Differentiable Autoencoding Neural Operator for Interpretable and Integrable Latent Space Modeling
• Learning Fluid-Structure Interaction with Physics-Informed Machine Learning and Immersed Boundary Methods
• PICT -- A Differentiable, GPU-Accelerated Multi-Block PISO Solver for Simulation-Coupled Learning Tasks in Fluid Dynamics

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