Designing self-adaptive network with thermodynamics

Mingyang Bai, Daqing Li
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Abstract

For real-world complex system constantly enduring perturbation, to achieve survival goal in changing yet unknown environments, the central problem is designing a self-adaptation strategy instead of fixed control strategies, which enables system to adjust its internal multi-scale structure according to environmental feedback. Inspired by thermodynamics, we develop a self-adaptive network utilizing only macroscopic information to achieve desired landscape through reconfiguring itself in unknown environments. By continuously estimating environment entropy, our designed self-adaptive network can adaptively realize desired landscape represented by topological measures. The adaptability of this network is achieved under several scenarios, including confinement on phase space and geographic constraint. The adaptation process is described by relative entropy corresponding to the Boltzmann H function, which decreases with time following unique power law distinguishing our self-adaptive network from memoryless systems. Moreover, we demonstrate the transformability of our self-adaptive network, as a critical mechanism of complex system resilience, allowing for transitions from one target landscape to another. Compared to data-driven methods, our self-adaptive network is understandable without careful choice of learning architecture and parameters. Our designed self-adaptive network could help to understand system intelligence through the lens of thermodynamics.
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利用热力学设计自适应网络
对于现实世界中不断经受扰动的复杂系统来说,要在不断变化的未知环境中实现生存目标,核心问题是设计一种自适应策略,而不是固定的控制策略,使系统能够根据环境反馈调整其内部多尺度结构。受热力学的启发,我们开发了一种自适应网络,它只利用宏观信息,通过在未知环境中重新配置自身来实现理想的景观。通过不断估计环境熵,我们设计的自适应网络可以自适应地实现以拓扑测量为代表的理想景观。该网络的适应性是在多种情况下实现的,包括对相空间的限制和地理限制。适应过程由与波尔兹曼 H 函数相对应的相对熵来描述,该熵随时间降低,遵循独特的幂律,将我们的自适应网络与无记忆系统区分开来。此外,我们还展示了自适应网络的可转换性,这是复杂系统复原力的关键机制,允许从一个目标景观转换到另一个目标景观。我们设计的自适应网络有助于从热力学的角度理解系统智能。
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