用模拟器反馈优化生成流模型，加速天文反问题求解且精度提升53%。
Flow Matching for Posterior Inference with Simulator Feedback
- 预训练生成流，仅用少量参数微调并引入模拟器反馈
- 相比经典方法精度提升53%，推理速度最快快67倍
- 适合需要快速高精度模拟反演的物理与天文学研究
基于流的生成建模是解决物理科学中逆问题的强大工具，可实现采样与似然评估，推理速度远超传统方法。本文提出通过模拟器提供的额外控制信号来精炼生成流模型：若模拟器可微，控制信号可包含梯度和特定问题的代价函数；否则可完全从模拟输出中学习。所提方法先预训练流网络，仅在微调阶段引入模拟器反馈，因此只需极少额外参数与计算量。我们在多个仿真推断基准问题上验证设计选择，并将带模拟器反馈的流匹配方法与经典MCMC方法对比，应用于强引力透镜系统建模这一天文学中的挑战性逆问题。结果表明，引入模拟器反馈使精度提升53%，性能媲美传统方法，同时推理速度最高提升67倍。
原文摘要 · Abstract (English)
Flow-based generative modeling is a powerful tool for solving inverse problems in physical sciences that can be used for sampling and likelihood evaluation with much lower inference times than traditional methods. We propose to refine flows with additional control signals based on a simulator. Control signals can include gradients and a problem-specific cost function if the simulator is differentiable, or they can be fully learned from the simulator output. In our proposed method, we pretrain the flow network and include feedback from the simulator exclusively for finetuning, therefore requiring only a small amount of additional parameters and compute. We motivate our design choices on several benchmark problems for simulation-based inference and evaluate flow matching with simulator feedback against classical MCMC methods for modeling strong gravitational lens systems, a challenging inverse problem in astronomy. We demonstrate that including feedback from the simulator improves the accuracy by $53\%$, making it competitive with traditional techniques while being up to $67$x faster for inference.
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