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arXiv 2303.12971physics.comp-phcs.CE
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流固耦合问题的可微混合神经网络建模

Differentiable hybrid neural modeling for fluid-structure interaction

Xiantao Fan, Jian-Xun Wang

摘要:

求解由非线性偏微分方程描述的复杂流固耦合问题在各种科学和工程应用中具有重要意义。传统的基于计算流体动力学的求解器不足以处理对大规模和长周期模拟的日益增长的需求。数据可用性的不断提高和深度学习(DL)的快速发展为通过数据建模解决这些挑战开辟了新的途径。通过可微编程框架,DL和经典数值技术的无缝集成可以显着提高数据驱动的建模性能。在这项研究中,我们提出了一个可微的混合神经建模框架的FSI问题的有效模拟,其中数值离散化的FSI物理的基础上浸入边界法无缝集成与序列神经网络使用可微编程。所有模块都是用JAX编程的,其中自动微分可以在整个模型滚动轨迹上进行梯度反向传播,从而允许混合神经FSI模型作为一个整体以端到端、序列到序列的方式进行训练。通过几个FSI基准案例,我们证明了所提出的方法的优点和能力,在建模FSI动力学的刚性和柔性体。所提出的模型也证明了其优于基线纯数据驱动的神经模型,弱耦合混合神经模型,纯数值FSI求解器的准确性,鲁棒性和可推广性。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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