卷积自编码器用于三维界面多相流的重建
Convolutional autoencoders for the reconstruction of three-dimensional interfacial multiphase flows
AI总结：
本文研究了卷积自编码器在三维界面多相流重建中的应用，发现适度扩散的界面表示在精度与结构保留间取得最佳平衡。
摘要：
我们系统地研究了卷积自编码器在三维界面多相流降维表示中的应用。重点在于相指标的重建，我们考察了界面表示的选择，包括尖锐、扩散和水平集公式，如何影响在不同界面复杂度下的重建准确性。训练和验证使用了具有受控几何复杂度的合成数据集和多相均匀各向同性湍流的高保真模拟。我们证明界面表示在自编码器性能中起关键作用。过于尖锐的界面会导致小尺度特征的丢失，而过于扩散的界面会降低整体准确性。在所有数据集和指标中，适度扩散的界面在保留细尺度结构和实现准确重建之间提供了最佳平衡。这些发现阐明了使用自编码器进行多相流降维的关键限制和最佳实践。通过澄清界面表示如何与卷积神经网络的归纳偏置相互作用，这项工作为将自编码器用于准确状态压缩的训练与用于时间预测或输入输出预测的代理模型的训练解耦奠定了基础。
英文摘要：
We present a systematic investigation of convolutional autoencoders for the reduced-order representation of three-dimensional interfacial multiphase flows. Focusing on the reconstruction of phase indicators, we examine how the choice of interface representation, including sharp, diffuse, and level-set formulations, impacts reconstruction accuracy across a range of interface complexities. Training and validation are performed using both synthetic datasets with controlled geometric complexity and high-fidelity simulations of multiphase homogeneous isotropic turbulence. We show that the interface representation plays a critical role in autoencoder performance. Excessively sharp interfaces lead to the loss of small-scale features, while overly diffuse interfaces degrade overall accuracy. Across all datasets and metrics considered, a moderately diffuse interface provides the best balance between preserving fine-scale structures and achieving accurate reconstructions. These findings elucidate key limitations and best practices for dimensionality reduction of multiphase flows using autoencoders. By clarifying how interface representations interact with the inductive biases of convolutional neural networks, this work lays the foundation for decoupling the training of autoencoders for accurate state compression from the training of surrogate models for temporal forecasting or input-output prediction in latent space.
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