发表机构
Technical University of Munich（慕尼黑工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对冲击驱动的可压缩多相流基准测试难题，介绍含2.4TB数据集的Neptuna框架，评估多种替代模型家族，研究复合损失与自适应损失平衡，结果显示无单一模型最优，复合损失及SoftAdapt策略有显著改进。
AI 中文摘要
涉及冲击和材料界面的可压缩多相流出现在诸如气泡坍塌和液滴破裂等应用中，由于可压缩性、尖锐不连续性和多相效应的同时存在，为这些流动开发可靠的机器学习替代模型仍然具有挑战性。本文介绍了第一个专门为冲击驱动的可压缩多相流设计的大规模基准，包括2.4TB的高保真2D和3D数据集。在该基准框架上评估了多种替代模型家族，研究了复合损失以及自适应损失平衡，评估包括逐点、光谱、特征聚焦、结构和物理信息等指标。结果表明，没有单一模型在所有数据集和指标上表现最佳，复合损失显著改善了界面保留和光谱保真度，SoftAdapt在自适应加权策略中提供了最一致的改进。
英文摘要
Compressible multiphase flows involving shocks and material interfaces arise in applications such as bubble collapse and droplet breakup, where strong nonlinear interactions produce complex interface deformation, mixing, and multiscale dynamics. Developing reliable machine learning surrogates for these flows remains challenging due to the simultaneous presence of compressibility, sharp discontinuities, and multiphase effects. In this work, we introduce the first large-scale benchmark specifically designed for shock-driven compressible multiphase flows, comprising 2.4 TB of high-fidelity 2D and 3D datasets featuring shock-induced bubble collapse and droplet breakup. We evaluate diverse surrogate model families on our benchmarking framework: Neptuna {https://github.com/tumaer/Neptuna}, including convolutional, spectral, transformer-based, and pre-trained PDE foundation models. Beyond standard MSE training, we investigate composite losses combining MSE with Sobolev, interface-aware, and structure-aware terms, together with adaptive loss balancing using SoftAdapt and GradNorm. Evaluation includes pointwise, spectral, feature-focused, structural, and physics-informed metrics. Results show that no single model performs best across all datasets and metrics, while composite losses significantly improve interface preservation and spectral fidelity. Among adaptive weighting strategies, SoftAdapt provides the most consistent improvements with almost no overhead compared to MSE-only training.
