Information decomposition in complex systems via machine learning.

One of the fundamental steps toward understanding a complex system is identifying variation at the scale of the system's components that is most relevant to behavior on a macroscopic scale. Mutual information provides a natural means of linking variation across scales of a system due to its independence of functional relationship between observables. However, characterizing the manner in which information is distributed across a set of observables is computationally challenging and generally infeasible beyond a handful of measurements. Here, we propose a practical and general methodology that uses machine learning to decompose the information contained in a set of measurements by jointly optimizing a lossy compression of each measurement. Guided by the distributed information bottleneck as a learning objective, the information decomposition identifies the variation in the measurements of the system state most relevant to specified macroscale behavior. We focus our analysis on two paradigmatic complex systems: a Boolean circuit and an amorphous material undergoing plastic deformation. In both examples, the large amount of entropy of the system state is decomposed, bit by bit, in terms of what is most related to macroscale behavior. The identification of meaningful variation in data, with the full generality brought by information theory, is made practical for studying the connection between micro- and macroscale structure in complex systems.

Machine-Learning Optimized Measurements of Chaotic Dynamical Systems via the Information Bottleneck.

Deterministic chaos permits a precise notion of a "perfect measurement" as one that, when obtained repeatedly, captures all of the information created by the system's evolution with minimal redundancy. Finding an optimal measurement is challenging and has generally required intimate knowledge of the dynamics in the few cases where it has been done. We establish an equivalence between a perfect measurement and a variant of the information bottleneck. As a consequence, we can employ machine learning to optimize measurement processes that efficiently extract information from trajectory data. We obtain approximately optimal measurements for multiple chaotic maps and lay the necessary groundwork for efficient information extraction from general time series.

Tuning the mechanical impedance of disordered networks for impact mitigation.

Disordered-Network Mechanical Materials (DNMM), comprised of random arrangements of bonds and nodes, have emerged as mechanical metamaterials with the potential for achieving fine control over their mechanical properties. Recent computational studies have demonstrated this control whereby an extremely high degree of mechanical tunability can be achieved in disordered networks via a selective bond removal process called pruning. In this study, we experimentally demonstrate how pruning of a disordered network alters its macroscopic dynamic mechanical response and its capacity to mitigate impact. Impact studies with velocities ranging from 0.1 m s-1 to 1.5 m s-1 were performed, using a mechanical impactor and a drop tower, on 3D printed pruned and unpruned networks comprised of materials spanning a range of stiffness. High-speed videography was used to quantify the changes in Poisson's ratio for each of the network samples. Our results demonstrate that pruning is an efficient way to reduce the transmitted force and impulse from impact in the medium strain rate regime (101 s-1 to 102 s-1). This approach provides an interesting alternative route for designing materials with tailored impact mitigating properties compared to random material removal based on open cell foams.

Memory in Nonmonotonic Stress Relaxation of a Granular System.

We demonstrate experimentally that a granular packing of glass spheres is capable of storing memory of multiple strain states in the dynamic process of stress relaxation. Modeling the system as a noninteracting population of relaxing elements, we find that the functional form of the predicted relaxation requires a quantitative correction which grows in severity with each additional memory and is suggestive of interactions between elements. Our findings have implications for the broad class of soft matter systems that display memory and anomalous relaxation.