AI 中文总结
该研究以驱动群体储备池模型为对象，发现活性物质系统的熵产生可作为计算性能指标，揭示耗散与物理计算的关联及通用原则。
AI 中文摘要
物理系统可通过其自然动力学处理信息，为传统数字计算提供替代方案。储备池计算提供了一个基础框架，利用非线性基底将输入映射为丰富的动力学状态，再通过简单线性层读取。活性物质基底是引人注目的例子，它们持续消耗能量并产生熵。理论上，熵产生（EP）可描述不可逆性及与平衡态的距离，但仍不清楚其是否可追踪计算能力。我们通过分析驱动群体储备池模型解决这一概念缺口：从相空间收缩计算系统EP，从热流计算浴EP，并将二者与Lorenz-63任务的预测性能直接关联。通过力参数扫描，我们发现对驱动的最强响应及耗散的动力学区域与峰值性能重合，其中固有（最小耗散）与驱动转移热（最大耗散）的动力学差异最显著。总体而言，驱动功及驱动与非驱动EP的相对差异紧密反映性能态势；由广义刘维尔方程估计量推导的系统EP与热流提供互补诊断和度量，在性能最优区域最稳健。这些结果扩展了此前关于耗散对计算重要的预期，明确了其具有预测性的时机与方式，还将推理能力与固有动力学关联，为物理计算确立通用原则，并指出EP可作为储备池及其他基础基底的筛选指标。
英文摘要
Physical systems can process information through their natural dynamics, offering alternatives to conventional digital computing. Reservoir computing offers a basic framework by using a nonlinear substrate to map inputs into rich dynamical states read out by a simple linear layer. Active matter substrates are striking examples; they continuously consume energy and produce entropy. Theoretically, entropy production (EP) can describe the irreversibility and distance from equilibrium. But it remains unclear whether it can track computational capabilities. We address this conceptual gap by analyzing a driven swarm reservoir model. The system EP is computed from phase-space contraction and the bath EP from heat flow, separately, and put in direct association to prediction performance on a Lorenz-63 task. Via force parameter scans, we show that dynamical regimes with the strongest response to a driver as well as dissipation coincide with peak performance. Therein, the dynamical discrepancy between innate (minimal dissipation) and driven transferred heat (maximal dissipation) is sharpest. Generally, driver work and relative differences of driven and undriven EP closely mirror the performance landscape. The system EP, derived from a generalized Liouville-equation estimator, and heat flow provide complementary diagnostics and metrics, which are most robust in the best-performing regime. These results extend prior expectations that dissipation matters for computation by identifying when and how it becomes predictive. They also relate inference power to innate dynamics, pointing to generic principles for physical computing and where EP offers a screening metric for reservoirs and other base substrates.
