StocBench: A Benchmark for Generative Modeling of Stochastic Dynamics

2026-08-23

AI Summary

研究使用StocBench评估了基于传输和蒸馏的生成模型在有限推理预算下对随机流体流动的概率预测性能，对比了不同方法在一步和多步预测中的准确性和谱保持能力。

Abstract

We benchmark transport-based generative models as well as distillation-based few-step methods for the probabilistic forecasting of stochastic fluid flows, with a particular focus on performance under limited inference budgets. All methods are evaluated on a two-dimensional Kolmogorov flow with stochastic forcing. We measure one-step distributional accuracy against large simulated reference ensembles and assess whether the invariant measure is preserved during autoregressive rollouts via the enstrophy spectrum. On the stochastic task, flow matching achieves the most accurate one-step conditional distribution at high inference budgets, while the second-order exponential integrator DPM-2 is strongest at very low NFE. Few-step distillation methods are competitive with the multi-step methods and preserve the enstrophy spectrum particularly well. A deterministic control task, in which the forcing over the prediction interval is observed, separates aleatoric from epistemic uncertainty. Model performance does not translate between the two settings: the distilled models are competitive on the stochastic task but least accurate on the control task. While stochastic diffusion samplers such as DDPM better preserve the enstrophy spectrum during rollouts in the stochastic setting, deterministic samplers such as DDIM and DPM-2 show better spectral preservation in the deterministic setting.

Problem

• generative models
• stochastic dynamics
• inference budgets
• Kolmogorov flow
• enstrophy spectrum

Innovation

• Stochastic Dynamics
• Generative Modeling
• Benchmarking
• Flow Matching
• Few-step Distillation