arXiv subjects

Pavel Chvykov

Publications and source records attributed to Pavel Chvykov.

8 recordsLinked to original sources

arXiv2025

Emergent order from mixed chaos at low temperature

This paper explores a novel connection between a thermodynamic and a dynamical systems perspective on emergent dynamical order. We provide evidence for a conjecture that Hamiltonian systems with mixed chaos spontaneously find regular behavior when minimally coupled to a thermal bath at sufficiently low temperature. Numerical evidence across a diverse set of five dynamical systems supports this conjecture, and allows us to quantify corollaries about the organization timescales and disruption of order at higher temperatures. Balancing the damping-induced phase-space contraction against thermal exploration, we are able to predict the transition temperatures in terms of the relaxation timescales, indicating a novel nonequilibrium fluctuation-dissipation relation, and formally connecting the thermodynamic and dynamical systems views. Our findings suggest that for a wide range of real-world systems, coupling to a cold thermal bath leads to emergence of robust, non-trivial dynamical order, rather than a mere reduction of motion as in equilibrium.

nlin.AO↗

arXiv2022

Emergent Microrobotic Oscillators via Asymmetry-Induced Order

Spontaneous low-frequency oscillations on the order of several hertz are the drivers of many crucial processes in nature. From bacterial swimming to mammal gaits, the conversion of static energy inputs into slowly oscillating electrical and mechanical power is key to the autonomy of organisms across scales. However, the fabrication of slow artificial oscillators at micrometre scales remains a major roadblock towards the development of fully-autonomous microrobots. Here, we report the emergence of a low-frequency relaxation oscillator from a simple collective of active microparticles interacting at the air-liquid interface of a peroxide drop. Their collective oscillations form chemomechanical and electrochemical limit cycles that enable the transduction of ambient chemical energy into periodic mechanical motion and on-board electrical currents. Surprisingly, the collective can oscillate robustly even as more particles are introduced, but only when we add a single particle with modified reactivity to intentionally break the system's permutation symmetry. We explain such emergent order through a novel thermodynamic mechanism for asymmetry-induced order. The energy harvested from the stabilized system oscillations enables the use of on-board electronic components, which we demonstrate by cyclically and synchronously driving microrobotic arms. This work highlights a new strategy for achieving low-frequency oscillations at the microscale that are otherwise difficult to observe outside of natural systems, paving the way for future microrobotic autonomy.

nlin.AO↗

arXiv2021

Causal Geometry

Information geometry has offered a way to formally study the efficacy of scientific models by quantifying the impact of model parameters on the predicted effects. However, there has been little formal investigation of causation in this framework, despite causal models being a fundamental part of science and explanation. Here we introduce causal geometry, which formalizes not only how outcomes are impacted by parameters, but also how the parameters of a model can be intervened upon. Therefore we introduce a geometric version of "effective information" -- a known measure of the informativeness of a causal relationship. We show that it is given by the matching between the space of effects and the space of interventions, in the form of their geometric congruence. Therefore, given a fixed intervention capability, an effective causal model is one that matches those interventions. This is a consequence of "causal emergence," wherein macroscopic causal relationships may carry more information than "fundamental" microscopic ones. We thus argue that a coarse-grained model may, paradoxically, be more informative than the microscopic one, especially when it better matches the scale of accessible interventions -- as we illustrate on toy examples.

cs.IT↗

arXiv2021

Low rattling: A predictive principle for self-organization in active collectives

Self-organization is frequently observed in active collectives, from ant rafts to molecular motor assemblies. General principles describing self-organization away from equilibrium have been challenging to identify. We offer a unifying framework that models the behavior of complex systems as largely random, while capturing their configuration-dependent response to external forcing. This allows derivation of a Boltzmann-like principle for understanding and manipulating driven self-organization. We validate our predictions experimentally in shape-changing robotic active matter, and outline a methodology for controlling collective behavior. Our findings highlight how emergent order depends sensitively on the matching between external patterns of forcing and internal dynamical response properties, pointing towards future approaches for design and control of active particle mixtures and metamaterials.

cond-mat.stat-mech↗

arXiv2020

Beyond RG: from parameter flow to metric flow

Complex systems with many degrees of freedom are typically intractable, but some of their behaviors may admit simpler effective descriptions. The question of when such effective descriptions are possible remains open. The paradigmatic approach where such "emergent simplicity" can be understood in detail is the renormalization group (RG). Here, we show that for general systems, without the self-similarity symmetry required by the RG construction, the RG flow of model parameters is replaced by a more general flow of the Fisher Information Metric on the model manifold. We demonstrate that the systems traditionally studied with RG comprise special cases where this metric flow can be induced by a parameter flow, keeping the global geometry of the model-manifold fixed. In general, however, the geometry may deform, and metric flow cannot be reduced to a parameter flow -- though this could be achieved at the cost of augmenting the manifold by one new parameter, as we discuss. We hope that our framework can clarify how ideas from RG may apply in a broader class of complex systems.

cond-mat.stat-mech↗

arXiv2018

Least Rattling Feedback from Strong Time-scale Separation

In most interacting many-body systems associated with some "emergent phenomena," we can identify sub-groups of degrees of freedom that relax on dramatically different time-scales. Time-scale separation of this kind is particularly helpful in nonequilibrium systems where only the fast variables are subjected to external driving; in such a case, it may be shown through elimination of fast variables that the slow coordinates effectively experience a thermal bath of spatially-varying temperature. In this work, we investigate how such a temperature landscape arises according to how the slow variables affect the character of the driven quasi-steady-state reached by the fast variables. Brownian motion in the presence of spatial temperature gradients is known to lead to the accumulation of probability density in low temperature regions. Here, we focus on the implications of attraction to low effective temperature for the long-term evolution of slow variables. After quantitatively deriving the temperature landscape for a general class of overdamped systems using a path integral technique, we then illustrate in a simple dynamical system how the attraction to low effective temperature has a fine-tuning effect on the slow variable, selecting configurations that bring about exceptionally low force fluctuation in the fast-variable steady-state. We furthermore demonstrate that a particularly strong effect of this kind can take place when the slow variable is tuned to bring about orderly, integrable motion in the fast dynamics that avoids thermalizing energy absorbed from the drive. We thus point to a potentially general feedback mechanism in multi-time-scale active systems, that leads to the exploration of slow variable space, as if in search of fine-tuning for a "least rattling" response in the fast coordinates.

cond-mat.stat-mech↗

arXiv2012

Real-time MOKE microscopy made simple

We present a simple and effective instrument for simultaneous real-time imaging and hysteresis of the anisotropic magnetic domain dynamics in thin films using the Magneto-Optical Kerr Effect (MOKE). We furthermore illustrate that magnetic imaging allows a more accurate interpretation of the magnetization reversal processes than the conventional hysteresis characterization. In particular, we present a case where the onset of a double-step reversal observed in imaging remains invisible in the spatially integrated hysteresis loops. When complemented by precise tuning of the external magnetic field orientation, our system reveals the singular anisotropic variations of the domain dynamics near the hard-axis in epitaxial thin films, thus shedding light on the reported, but as yet unexplained, hard axis coercivity behavior.

physics.ins-det↗

arXiv2012

Microdroplet target synthesis for kilohertz ultrafast lasers

We have developed a method for producing spatially stable micron-scale liquid targets of flexible shapes at kilohertz repetition rate for use in air and vacuum, by perturbing 5 and 30 μm diameter streams with femtosecond laser pulses and monitoring the temporal development of the perturbation. Using water, we have produced features such as 2.1 μm diameter droplet and 1.3 μm diameter neck with less than 0.3 μm shot-to-shot variation, with prospects for further reduction in size and variability. The use of such micron-scale targets can be expected to prevent conductive heat dissipation, enhance field strength for ion acceleration and allow spatially-deterministic laser-cluster experiments.

physics.flu-dyn↗