Boundary-layer Features in Limit of Conservative Nonlinear Oscillators

In the double limit of high amplitude (xmax → ∞) and high leading power (x2 N+1, N → ∞), (1+1) dimensional conservative nonlinear oscillatory systems exhibit characteristics akin to boundary layer phenomena. The oscillating entity, x(t), tends to a periodic saw-tooth shape of linear segments, the velocity, x′(t), tends to a periodic step-function and the x − x′ phase-space plot tends to a rectangle. This is demonstrated by transforming x and t into proportionately scaled variables, η and θ, respectively. η(θ) is (2-π) periodic in θ and bounded (|η(θ)| ≤ 1). The boundary-layer characteristics show up by the fact that the deviations of η(θ), η′(θ) and the η − η′ phase-space plot from the sharp asymptotic shapes occurs over a range in θ of O(1/N) near the turning points of the oscillations.

Study of Nonlinear Models of Oscillatory Systems by Applying an Intelligent Computational Technique

In this paper, we have analyzed the mathematical model of various nonlinear oscillators arising in different fields of engineering. Further, approximate solutions for different variations in oscillators are studied by using feedforward neural networks (NNs) based on the backpropagated Levenberg–Marquardt algorithm (BLMA). A data set for different problem scenarios for the supervised learning of BLMA has been generated by the Runge–Kutta method of order 4 (RK-4) with the “NDSolve” package in Mathematica. The worth of the approximate solution by NN-BLMA is attained by employing the processing of testing, training, and validation of the reference data set. For each model, convergence analysis, error histograms, regression analysis, and curve fitting are considered to study the robustness and accuracy of the design scheme.

Challenges in dynamic mode decomposition

Dynamic mode decomposition (DMD) is a powerful tool for extracting spatial and temporal patterns from multi-dimensional time series, and it has been used successfully in a wide range of fields, including fluid mechanics, robotics and neuroscience. Two of the main challenges remaining in DMD research are noise sensitivity and issues related to Krylov space closure when modelling nonlinear systems. Here, we investigate the combination of noise and nonlinearity in a controlled setting, by studying a class of systems with linear latent dynamics which are observed via multinomial observables. Our numerical models include system and measurement noise. We explore the influences of dataset metrics, the spectrum of the latent dynamics, the normality of the system matrix and the geometry of the dynamics. Our results show that even for these very mildly nonlinear conditions, DMD methods often fail to recover the spectrum and can have poor predictive ability. Our work is motivated by our experience modelling multilegged robot data, where we have encountered great difficulty in reconstructing time series for oscillatory systems with intermediate transients, which decay only slightly faster than a period.

The Basis of Navigation Across Species

Animals navigate a wide range of distances, from a few millimeters to globe-spanning journeys of thousands of kilometers. Despite this array of navigational challenges, similar principles underlie these behaviors across species. Here, we focus on the navigational strategies and supporting mechanisms in four well-known systems: the large-scale migratory behaviors of sea turtles and lepidopterans as well as navigation on a smaller scale by rats and solitarily foraging ants. In lepidopterans, rats, and ants we also discuss the current understanding of the neural architecture which supports navigation. The orientation and navigational behaviors of these animals are defined in terms of behavioral error-reduction strategies reliant on multiple goal-directed servomechanisms. We conclude by proposing to incorporate an additional component into this system: the observation that servomechanisms operate on oscillatory systems of cycling behavior. These oscillators and servomechanisms comprise the basis for directed orientation and navigational behaviors. Expected final online publication date for the Annual Review of Psychology, Volume 73 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Theoretical Analysis of Thin Curved Rod Displacements V. P. Lugovoi1)

The paper presents a comparative theoretical analysis of the movements of the curved rods of various curvature forms, which can be applied as tools for ultrasonic treatment of holes in fragile materials. It has been shown that the traditional processing of holes by an ultrasonic method is based on the use of straight rods, in which the amplitudes of displacements on the working – free end corresponds to the value of displacements at the point of its attachment to the ultrasonic oscillation concentrator. Supplementing the configuration of a straight rod with a curvilinear shape in the form of a circular arc or a spiral twisted by one turn will allow obtaining additional displacements caused by the elastic properties of a section with a curved shape. The paper considers several calculated schemes of a curvilinear rod bounded by angles j equal to p/2, p and 2p, fferent direction of the external force action. The obtained results have shown that an increase in the circular arc angle leads to a corresponding increase in the elastic displacement index of the rod free end. In this case, the total displacements of the rod free end will be made from displacements caused by vibrations of the acoustic system and the displacements of a curved thin rod from an external force. Calculations have established that the magnitude of the elastic displacements of curved rods is influenced by the shape and magnitude of the angle, the direction of the external force, the radius of curvature, the rigidity of the cross section. The considered schemes of thin rods with curvilinear sections can find practical application in ultrasonic oscillatory systems for processing small-diameter holes in fragile materials. This increases the intensity of tool oscillations and improves the process performance.

Diffusion approximation of multi-class Hawkes processes: Theoretical and numerical analysis

Oscillatory systems of interacting Hawkes processes with Erlang memory kernels were introduced by Ditlevsen and Löcherbach (Stoch. Process. Appl., 2017). They are piecewise deterministic Markov processes (PDMP) and can be approximated by a stochastic diffusion. In this paper, first, a strong error bound between the PDMP and the diffusion is proved. Second, moment bounds for the resulting diffusion are derived. Third, approximation schemes for the diffusion, based on the numerical splitting approach, are proposed. These schemes are proved to converge with mean-square order 1 and to preserve the properties of the diffusion, in particular the hypoellipticity, the ergodicity, and the moment bounds. Finally, the PDMP and the diffusion are compared through numerical experiments, where the PDMP is simulated with an adapted thinning procedure.

Inferring causality in biological oscillators

Motivation Fundamental to biological study is identifying regulatory interactions. The recent surge in time-series data collection in biology provides a unique opportunity to infer regulations computationally. However, when components oscillate, model-free inference methods, while easily implemented, struggle to distinguish periodic synchrony and causality. Alternatively, model-based methods test the reproducibility of time series given a specific model but require inefficient simulations and have limited applicability. Results We develop an inference method based on a general model of molecular, neuronal, and ecological oscillatory systems that merges the advantages of both model-based and model-free methods, namely accuracy, broad applicability, and usability. Our method successfully infers the positive and negative regulations within various oscillatory networks, e.g., the repressilator and a network of cofactors at the pS2 promoter, outperforming popular inference methods. Availability We provide a computational package, ION (Inferring Oscillatory Networks), that users can easily apply to noisy, oscillatory time series to uncover the mechanisms by which diverse systems generate oscillations. Accompanying MATLAB code under a BSD-style license and examples are available at ttps://github.com/Mathbiomed/ION. Additionally, the code is available under a CC-BY 4.0 License at https://doi.org/10.6084/m9.figshare.16431408.v1. Supplementary information Supplementary data are available at Bioinformatics online.

PARAMETER ESTIMATION FOR OSCILLATORY SYSTEMS

Simple harmonic motion was investigated of a rotational oscillating system. The effect of dumping and forcing on motion of the system was examined and measurements were taken. Resonance in a oscillating system was investigated and quality factor of the dumping system was measured at different damping forces using three different methods. Resonance curves were constructed at two different damping forces. A probabilistic model was built and system parameters were estimated from the resonance curves using Stan sampling platform. The quality factor of the oscillating system when the additional dumping was turned off was estimated to be $Q = \num{71 \pm 1}$ and natural frequency $\omega_0 = \num{3.105 \pm 0.008}\, \si{\per\second}$.