arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1908.09332nlin.AOcond-mat.dis-nncond-mat.stat-mech
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双稳态弹簧无序力学网络中的驱动特异性适应

Drive-specific adaptation in disordered mechanical networks of bistable springs

Hridesh Kedia, Deng Pan, Jean-Jacques Slotine, Jeremy L. England

AI总结:

本文数值研究受驱双稳态弹簧无序力学网络,揭示吸引子会针对外驱模式微调,呈现低功吸收和超常稳定性,从而支持在广阔构型景观中发现目标响应状态。

摘要:

具有许多稳定构型的系统在自然界中大量存在,无论是在生命体还是无生命物质中。它们固有的非线性以及对微小扰动的敏感性,使其研究颇具挑战,尤其是在存在外部驱动时,因为驱动会改变不同吸引子的相对稳定性。在这种情况下,人们可能会问:外部驱动的特定模式与受驱多稳态系统所选择的特定吸引子状态之间是否存在某种明确关系。为深入理解这一问题,我们通过数值方法研究了受驱的双稳态弹簧无序力学网络;每个弹簧都有两个稳定静止长度,使这些网络拥有大量稳定构型,从而捕捉了一大类多稳态系统的基本物理性质。我们发现,受驱无序多稳态力学网络的吸引子状态会针对外部施力模式进行精细调节,以从该施力中吸收较低的功。此外,我们发现,对于给定的功吸收水平,这些驱动特异性吸引子状态甚至比预期更加稳定。我们的结果表明,对这些系统广阔构型空间的受驱探索会偏向于与驱动环境具有特殊关系的状态,因此可用于在拥有多样构型广阔景观的系统中“发现”具有所需响应特性的状态。

英文摘要:

Systems with many stable configurations abound in nature, both in living and inanimate matter. Their inherent nonlinearity and sensitivity to small perturbations make them challenging to study, particularly in the presence of external driving, which can alter the relative stability of different attractors. Under such circumstances, one may ask whether any clear relationship holds between the specific pattern of external driving and the particular attractor states selected by a driven multistable system. To gain insight into this question, we numerically study driven disordered mechanical networks of bistable springs which possess a vast number of stable configurations arising from the two stable rest lengths of each spring, thereby capturing the essential physical properties of a broad class of multistable systems. We find that the attractor states of driven disordered multistable mechanical networks are fine-tuned with respect to the pattern of external forcing to have low work absorption from it. Furthermore, we find that these drive-specific attractor states are even more stable than expected for a given level of work absorption. Our results suggest that the driven exploration of the vast configuration space of these systems is biased towards states with exceptional relationship to the driving environment, and could therefore be used to `discover' states with desired response properties in systems with a vast landscape of diverse configurations.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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