物理系统采样新方法 Quenched Ensemble Sampling 发布
Quenched Ensemble Sampling
朋友，DeepSeek 的研究者们搞了个新方法叫 Quenched Ensemble Sampling，专门解决物理系统相变时的采样问题，比 tempering 更厉害，还能用在模型比较里。
这篇论文提出了一种名为 Quenched Ensemble Sampling 的新方法，用于解决物理系统中相变时的采样难题。该方法通过将硬能量约束扩展为排斥势能族，解决了传统 Nested sampling 在高维度下的应用限制。实验表明，该方法在估计边际似然和绘制后验样本方面，在第一类相变中表现优于 tempering 等流行方法。研究还将其应用于贝叶斯神经网络模型比较，并成功应用于高维连续晶格场理论中的第一类相变和配分函数估计。
Quenched Ensemble Sampling
Some of the sharpest challenges in sampling from the energy functions of physical systems arise at phase transitions, where the density of states changes abruptly and many sampling algorithms stall. Nested sampling is a particle method that traverses the density of states under a hard energy constraint and is known to be robust to such transitions, but its application in high dimension is limited by the difficulty of sampling under that constraint. In this work we introduce Quenched Ensemble Sampling, which generalises the hard constraint to a family of repulsive potentials at the energy boundary. This preserves the quenched path of monotonically decreasing energy while making the constrained target amenable to scalable gradient-based kernels. We demonstrate on synthetic models of phase transitions that our method estimates the marginal likelihood and draws posterior samples across a first-order transition where popular alternatives such as tempering fail. We apply the procedure to marginal likelihood estimation in Bayesian neural networks, enabling model comparison between network architectures. Finally, in a high-dimensional continuous lattice field theory, we show that this method traverses a first-order transition and estimates the partition function.