Brandstrup Lundgren (badgerrotate3)

Nonlinear oscillator systems are ubiquitous in biology and physics, and their control is a practical problem in many experimental systems. Here we study this problem in the context of the two models of spatially coupled oscillators the complex Ginzburg-Landau equation (CGLE) and a generalization of the CGLE in which oscillators are coupled through an external medium (emCGLE). We focus on external control drives that vary in both space and time. We find that the spatial distribution of the drive signal controls the frequency ranges over which oscillators synchronize to the drive and that boundary conditions strongly influence synchronization to external drives for the CGLE. Our calculations also show that the emCGLE has a low density regime in which a broad range of frequencies can be synchronized for low drive amplitudes. We study the bifurcation structure of these models and find that they are very similar to results for the driven Kuramoto model, a system with no spatial structure. We conclude by discussing qualitative implications of our results for controlling coupled oscillator systems such as the social amoebae Dictyostelium and populations of Belousov Zhabotinsky (BZ) catalytic particles using spatially structured external drives.Colloidal systems comprising solid or fluid particles dispersed in nematic monodomains are known to be a convenient means to study topological defects. Recent experiments have shown that twist-bend nematic (N_TB) droplets in a nematic matrix act as colloidal particles that lead to the formation of elastic dipoles, quadrupoles, and their ordered clusters. selleck In this study, we examine the effect of low-frequency (f∼mHz) electric fields on such defect configurations. We find that (i) the hyperbolic hedgehogs of elastic dipoles shift toward the negative electrode in static fields and perform oscillatory motion in AC fields, indicating the presence of nonvanishing flexoelectric polarization in the field-free state; (ii) the elastic dipoles, propelled by forces of backflow due to coupled flexoelectric and dielectric distortions, drift uniformly along their axes with the N_TB drops in lead; (iii) the translational velocity v_d increases linearly with both f and the diameter of N_TB drops; and (iv) with increasing applied voltage U, v_d(U) exhibits a monotonic, slightly nonlinear variation at f≤200mHz, tending toward linearity at higher frequencies.Magnetohydrodynamic (MHD) turbulence affects both terrestrial and astrophysical plasmas. The properties of magnetized turbulence must be better understood to more accurately characterize these systems. This work presents ideal MHD simulations of the compressible Taylor-Green vortex under a range of initial subsonic Mach numbers and magnetic field strengths. We find that regardless of the initial field strength, the magnetic energy becomes dominant over the kinetic energy on all scales after at most several dynamical times. The spectral indices of the kinetic and magnetic energy spectra become shallower than k^-5/3 over time and generally fluctuate. Using a shell-to-shell energy transfer analysis framework, we find that the magnetic fields facilitate a significant amount of the energy flux and that the kinetic energy cascade is suppressed. Moreover, we observe nonlocal energy transfer from the large-scale kinetic energy to intermediate and small-scale magnetic energy via magnetic tension. We conclude that even in intermittently or singularly driven weakly magnetized systems, the dynamical effects of magnetic fields cannot be neglected.We propose a statistical learning framework based on group-sparse regression that can be used to (i) enforce conservation laws, (ii) ensure model equivalence, and (iii) guarantee symmetries when learning or inferring differential-equation models from data. Directly learning interpretable mathematical models from data has emerged as a valuable modeling approach. However, in areas such as biology, high noise levels, senso