无需重建复杂系统分布，用采样数据推算熵产生量。
Inferring entropy production in many-body systems using nonequilibrium maximum entropy
- 基于非平衡最大熵原理与凸对偶，仅用轨迹观测样本推断熵产生。
- 在1000自旋模型和神经元脉冲数据上验证，可得平均熵产生下界。
- 能分解不同相互作用阶次的熵贡献，物理意义清晰。
我们提出一种在高维随机系统（包括多体系统和长记忆非马尔可夫系统）中推断熵产生（EP）的方法。传统方法因计算与统计限制，在此类系统中难以应用。本方法利用非平衡最大熵原理与凸对偶，仅需轨迹可观测量（如时空关联）的样本，即可推断轨迹级熵产生及平均熵产生的下界。无需重构高维概率分布或速率矩阵，也无需假设离散状态或分体动力学。此外，该方法支持熵产生的层次分解，反映不同相互作用阶次的贡献，并具有直观的“热力学不确定性关系”物理解释。我们在含1000自旋的无序非平衡自旋模型和大规模神经脉冲数据集上验证了其数值性能。
原文摘要 · Abstract (English)
We propose a method for inferring entropy production (EP) in high-dimensional stochastic systems, including many-body systems and non-Markovian systems with long memory. Standard techniques for estimating EP become intractable in such systems due to computational and statistical limitations. We infer trajectory-level EP and lower bounds on average EP by exploiting a nonequilibrium analogue of the Maximum Entropy principle, along with convex duality. Our approach uses only samples of trajectory observables, such as spatiotemporal correlations. It does not require reconstruction of high-dimensional probability distributions or rate matrices, nor impose any special assumptions such as discrete states or multipartite dynamics. In addition, it may be used to compute a hierarchical decomposition of EP, reflecting contributions from different interaction orders, and it has an intuitive physical interpretation as a "thermodynamic uncertainty relation." We demonstrate its numerical performance on a disordered nonequilibrium spin model with 1000 spins and a large neural spike-train dataset.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。