Physics-informed graph neural networks enhance scalability of variational nonequilibrium optimal control.
Physics-informed graph neural networks enhance scalability of variational nonequilibrium optimal control.
复制标题
基于物理的图神经网络增强了变分非平衡最优控制的可扩展性。
DOI：
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发表时间：
2022
影响因子：
4.4
通讯作者：
Grant M. Rotskoff
中科院分区：
文献类型：
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作者：
Jiawei Yan;Grant M. Rotskoff
当一个物理系统被驱离平衡时，其动力学轨迹的统计分布决定了它的许多物理性质。描述动态可观测量分布的性质，如电流或熵产生率，已成为非平衡统计力学的中心问题。渐近地，对于广泛的一类观测量，当动态是马尔可夫时，给定观测量的分布满足大偏差原理，这意味着波动可以通过计算缩放累积量生成函数来表征长时间限制。对于复杂的相互作用系统，计算这个函数并不容易分析（也不经常数值），因此需要开发强大的数值技术来进行这种计算，以探索非平衡材料的性质。在这里，我们描述了一个算法，重铸这个任务作为一个最优控制问题，可以解决变化。我们解决了最优控制力，使用神经网络ananimals是量身定制的物理系统的力量。我们证明，这种方法导致转移和准确的解决方案，在两个系统具有大量的相互作用的粒子。
When a physical system is driven away from equilibrium, the statistical distribution of its dynamical trajectories informs many of its physical properties. Characterizing the nature of the distribution of dynamical observables, such as a current or entropy production rate, has become a central problem in nonequilibrium statistical mechanics. Asymptotically, for a broad class of observables, the distribution of a given observable satisfies a large deviation principle when the dynamics is Markovian, meaning that fluctuations can be characterized in the long-time limit by computing a scaled cumulant generating function. Calculating this function is not tractable analytically (nor often numerically) for complex, interacting systems, so the development of robust numerical techniques to carry out this computation is needed to probe the properties of nonequilibrium materials. Here, we describe an algorithm that recasts this task as an optimal control problem that can be solved variationally. We solve for optimal control forces using neural network ansatz that are tailored to the physical systems to which the forces are applied. We demonstrate that this approach leads to transferable and accurate solutions in two systems featuring large numbers of interacting particles.