提升复杂系统模拟的预测效率，减少采样步骤。
Efficient probabilistic surrogate modeling techniques for partially-observed large-scale dynamical systems
- 改进流匹配方法，降低生成过程所需采样步数。
- 在三维模拟切片预测中实现高效高精度结果。
- 适合需要快速生成流体输入的工程仿真场景。
本文研究基于概率模型的偏微分方程描述的动力系统预报技术，重点比较多种流匹配范式的扩展方法以减少采样步数。包括直接蒸馏、渐进蒸馏、对抗性扩散蒸馏、Wasserstein GAN 和修正流等方法。实验涵盖多个具有挑战性的系统，特别解决直接预测大规模三维模拟的二维切片问题，为求解器提供高效入口条件生成方案。
原文摘要 · Abstract (English)
This paper is concerned with probabilistic techniques for forecasting dynamical systems described by partial differential equations (such as, for example, the Navier-Stokes equations). In particular, it is investigating and comparing various extensions to the flow matching paradigm that reduce the number of sampling steps. In this regard, it compares direct distillation, progressive distillation, adversarial diffusion distillation, Wasserstein GANs and rectified flows. Moreover, experiments are conducted on a set of challenging systems. In particular, we also address the challenge of directly predicting 2D slices of large-scale 3D simulations, paving the way for efficient inflow generation for solvers.
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