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, sensor-induced correlations, and strong intersystem variability can render data-driven models nonsensical or physically inconsistent without additional constraints on the model structure. Hence, it is important to leverage prior knowledge from physical principles to learn biologically plausible and physically consistent models rather than models that simply fit the data best. We present the group iterative hard thresholding algorithm and use stability selection to infer physically consistent models with minimal parameter tuning. We show several applications from systems biology that demonstrate the benefits of enforcing priors in data-driven modeling.

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 $\left(N_{\mathrm{TB}}\right)$ droplets in a nematic matrix act as colloidal particles that lead to the formation of elastic dipoles, quadrupoles, and their ordered clusters. In this study, we examine the effect of low-frequency $(f\sim \mathrm{mHz})$ 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 $N_{\mathrm{TB}}$ drops in lead; (iii) the translational velocity $v_d$ increases linearly with both $f$ and the diameter of $N_{\mathrm{TB}}$ drops; and (iv) with increasing applied voltage U, $v_d\left(U\right)$ exhibits a monotonic, slightly nonlinear variation at $f\le 200\mathrm{mHz}$, 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 investigate the relaxation dynamics of open nonintegrable quantum many-body systems in the thermodynamic limit by using a tensor-network formalism. We simulate the Lindblad quantum master equation (LQME) of infinite systems by making use of the uniform matrix product operators (MPO) as the ansatz of their density matrices. Furthermore, we establish a method to measure the thermodynamic equivalence between two states described by the uniform MPOs. We numerically show that when an initial state of the LQME is a thermal Gibbs state, a time evolved state is always indistinguishable from a Gibbs state with a time-dependent effective temperature in the weak-dissipation and thermodynamic limit.

The standard short-range two-dimensional Ising spin glass is numerically well accessible, in particular, because there are polynomial-time ground-state algorithms. On the other hand, in contrast to higher dimensional spin glasses, it does not exhibit a rich behavior, i.e., no ordered phase at finite temperature. Here, we investigate whether long-range correlated bonds change this behavior. This would still keep the model numerically well accessible while exhibiting a more interesting behavior. The bonds are drawn from a Gaussian distribution with a two-point correlation for bonds at distance $r$ that decays as ${(1+r^2)}^{-a/2}$, $a\ge 0$. We study numerically with exact algorithms the ground-state and domain-wall excitations. Our results indicate that the inclusion of bond correlations still does not lead to a spin-glass order at any finite temperature. A further analysis reveals that bond correlations have a strong effect at local length scales, inducing ferro- and antiferromagnetic domains into the system. The length scale of ferro- and antiferromagnetic order diverges exponentially as the correlation exponent approaches a critical value, $a\to a_{\text{crit}}=0$. Thus, our results suggest that the system becomes a ferro- or antiferromagnet only in the limit $a\to 0$.

Devices that use quantum advantages for storing energy in the degree of freedom of quantum systems have drawn attention due to their properties of working as quantum batteries (QBs). However, one can identify a number of problems that need to be adequately solved before the start of a real manufacturing process of these devices. In particular, it is important to pay attention to the ability of quantum batteries in storing energy when no consumption center is connected to them. In this paper, by considering quantum batteries disconnected from external charging fields and consumption center, we study the dissipative effects that lead to charge leakage to the surrounding environment. We identify this phenomena as a self-discharging of QBs, in analogy to the inherent decay of the stored charge of conventional classical batteries in a open-circuit configuration. The performance of QBs compared to the classical counterpart is highlighted for single- and multicell quantum batteries.

We consider the laser rate equations describing the evolution of a semiconductor laser subject to an optoelectronic feedback. We concentrate on the first Hopf bifurcation induced by a short delay and develop an asymptotic theory where the delayed variable is Taylor expanded. We determine a nearly vertical branch of strongly nonlinear oscillations and derive ordinary differential equations that capture the bifurcation properties of the original delay differential equations. An unexpected result is the need for Taylor expanding the delayed variable up to third order rather than first order. We discuss recent laser experiments where sustained oscillations have been clearly observed with a short-delayed feedback.

We perform an experimental parametric study of the chaos generated by a laser diode subjected to phase-conjugate feedback. In addition to the typical figure of merit, i.e., chaos bandwidth, the corresponding spectral flatness and permutation entropy at delay is analyzed. Our experimental observations reveal that the chaos can be generated with a bandwidth of $\approx 29$ GHz, a spectral flatness up to 0.75, and a permutation entropy at delay of up to 0.99. These optimized performances are maintained over a large range of parameters and have not been achieved in the conventional optical feedback configuration. Interestingly, reducing the pump current reduces the chaos bandwidth while keeping the spectral flatness and the permutation entropy at delay the same as observed for increased pump current. Our experimental findings are consistent with the presented numerical simulations produced using the Lang-Kobayashi model.

In 4D symplectic maps complex instability of periodic orbits is possible, which cannot occur in the 2D case. We investigate the transition from stable to complex unstable dynamics of a fixed point under parameter variation. The change in the geometry of regular structures is visualized using 3D phase-space slices and in frequency space using the example of two coupled standard maps. The chaotic dynamics is studied using escape time plots and by computations of the 2D invariant manifolds associated with the complex unstable fixed point. Based on a normal-form description, we investigate the underlying transport mechanism by visualizing the escape paths and the long-time confinement in the surrounding of the complex unstable fixed point. We find that the slow escape is governed by the transport along the unstable manifold while going across the approximately invariant planes defined by the corresponding normal form.

Neural populations show patterns of synchronous activity, as they share common correlated inputs. Neurons in the cortex that are connected by strong synapses cause rapid firing explosions. In addition, areas that are connected by weaker synapses have a slower dynamics and they can contribute to asymmetries in the input distributions. The aim of this work is to develop a neural model to investigate how the heterogeneities in the synaptic input distributions affect different levels of organizational activity in the brain dynamics. We analytically show how small changes in the correlation inputs can cause large changes in the interactions of the outputs that lead to a phase transition, demonstrating that a simple variation in the direction of a biased skewed distribution in the neuronal inputs can generate a transition of states in the firing rate, passing from spontaneous silence (“down state”) to an absolute spiking activity (“up state”). We present an exact quantification of the dynamics of the output variables, showing that when considering a biased skewed distribution in the inputs of neuronal population, the critical point is not in an asynchronous or synchronous state but rather at an intermediate value.

Despite the recognition of the layered structure and evident criticality in the cortex, how the specification of input, output, and computational layers affects the self-organized criticality has not been much explored. By constructing heterogeneous structures with a well-accepted model of leaky neurons, we find that the specification can lead to robust criticality rather insensitive to the strength of external stimuli. This naturally unifies the adaptation to strong inputs without extra synaptic plasticity mechanisms. Low degree of recurrence constitutes an alternative explanation to subcriticality other than the high-frequency inputs. Unlike fully recurrent networks where external stimuli always render subcriticality, the dynamics of networks with sufficient feedforward connections can be driven to criticality and supercriticality. These findings indicate that functional and structural specification and their interplay with external stimuli are of crucial importance for the network dynamics. The robust criticality puts forward networks of the leaky neurons as promising platforms for realizing artificial neural networks that work in the vicinity of critical points.

We investigate the influence of nonlocal couplings on the torsional and bending elasticities of DNA. Such couplings have been observed in the past by several simulation studies. Here, we use a description of DNA conformations based on the variables tilt, roll, and twist. Our analysis of both coarse-grained (oxDNA) and all-atom models indicates that these share strikingly similar features: there are strong off-site couplings for tilt-tilt and twist-twist, while they are much weaker in the roll-roll case. By developing an analytical framework to estimate bending and torsional persistence lengths in nonlocal DNA models, we show how off-site interactions generate a length-scale-dependent elasticity. Based on the simulation-generated elasticity data, the theory predicts a significant length-scale-dependent effect on torsional fluctuations but only a modest effect on bending fluctuations. These results are in agreement with experiments probing DNA mechanics from single base pair to kilobase pair scales.

The adsorption of colloidal particles at liquid interfaces is of great importance scientifically and industrially, but the dynamics of the adsorption process is still poorly understood. In this paper we use a Langevin model to study the adsorption dynamics of ellipsoidal colloids at a liquid interface. Interfacial deformations are included by coupling our Langevin dynamics to a finite element model while transient contact line pinning due to nanoscale defects on the particle surface is encoded into our model by renormalizing particle friction coefficients and using dynamic contact angles relevant to the adsorption timescale. Our simple model reproduces the monotonic variation of particle orientation with time that is observed experimentally and is also able to quantitatively model the adsorption dynamics for some experimental ellipsoidal systems but not others. However, even for the latter case, our model accurately captures the adsorption trajectory (i.e., particle orientation versus height) of the particles. Our study clarifies the subtle interplay between capillary, viscous, and contact line forces in determining the wetting dynamics of micron-scale objects, allowing us to design more efficient assembly processes for complex particles at liquid interfaces.

We numerically analyze the tensile strength of a single wet agglomerate modeled as a viscocohesive aggregate impacting a flat surface by using the discrete-element simulations. The viscocohesive agglomerate composed of primary spherical particles with the inclusion of the interstitial liquid in the form of the capillary bridges characterized by the cohesive and viscous forces between particles is extracted from a cuboidal sample of granular materials by applying a spherical probe. The tensile strength is measured from the impact test of a wet agglomerate by systematically varying different values of the surface tension of the interstitial liquid, the liquid viscosity, and the impact speed. We show that the tensile stress increases immediately when the collision occurs between the agglomerate and the flat surface. The peak of the tensile stress obtained after the collision, then decreases smoothly with increasing the particle movement. The maximum tensile stress is defined to be the tensile strength of such agglomerate. It is remarkable that the normalized tensile strength of such agglomerate can be well described as a function of a dimensionless impact number that incorporates the capillary number and Stokes number (calculated from the surface tension and the viscosity of the liquid and the impact rate of the agglomerate), thus providing the confirmation for the unified representation of the liquid properties and the impact rate of wet granular media.

We study a three-dimensional articulated rigid-body biped model that possesses zero cost of transport walking gaits. Energy losses are avoided due to the complete elimination of the foot-ground collisions by the concerted oscillatory motion of the model's parts. The model consists of two parts connected via a universal joint. It does not rely on any geometry-altering mechanisms, massless parts, or springs. Despite the model's simplicity, its collisionless gaits feature walking with finite speed, foot clearance, and ground friction. The collisionless spectrum can be studied analytically in the small movement limit, revealing infinitely many periodic modes. The modes differ in the number of sagittal and coronal plane oscillations at different stages of the walking cycle. We focus on the mode with the minimal number of such oscillations, presenting its complete analytical solution. We then numerically evolve it toward a general nonsmall movement solution. A general collisionless mode can be tuned by adjusting a single model parameter. Some of the presented results display a surprising degree of generality and universality.

We consider the problem of an inextensible but flexible fiber advected by a steady chaotic flow, and ask the simple question of whether the fiber can spontaneously knot itself. Using a one-dimensional Cosserat model, a simple local viscous drag model and discrete contact forces, we explore the probability of finding knots at any given time when the fiber is interacting with the ABC class of flows. The bending rigidity is shown to have a marginal effect compared to that of increasing the fiber length. Complex knots are formed up to 11 crossings, but some knots are more probable than others. The finite-time Lyapunov exponent of the flow is shown to have a positive effect on the knot probability. Finally, contact forces appear to be crucial since knotted configurations can remain stable for times much longer than the turnover time of the flow, something that is not observed when the fiber can freely cross itself.

Vertically vibrating a liquid bath at two frequencies, $f$ and $f/2$, having a constant relative phase difference can give rise to self-propelled superwalking droplets on the liquid surface. We have numerically investigated such superwalking droplets in the regime where the phase difference varies slowly with time. We predict the emergence of stop-and-go motion of droplets, consistent with experimental observations [Valani et al. Phys. Rev. Lett. 123, 024503 (2019)]. Our simulations in the parameter space spanned by the droplet size and the rate of traversal of the phase difference uncover three different types of droplet motion: back-and-forth, forth-and-forth, and irregular stop-and-go motion, which we explore in detail. Our findings lay a foundation for further studies of dynamically driven droplets, whereby the droplet's motion may be guided by engineering arbitrary time-dependent phase difference functions.

We present our analysis on microrheology of a bench-scale pulsed fluidized bed, which represents a weakly confined system. Nonlinear gas-particle and particle-particle interactions resulting from pulsed flow are associated with harmonic and subharmonic modes. While periodic structured bubble patterns are observed at the mesoscale, particle-scale measurements reveal anomalous diffusion in the driven granular medium. We use single-particle tracks to analyze ergodicity and ageing properties at two pulsing frequencies having remarkably different mesoscale features. The scaling of ensemble-averaged mean-squared displacement is not unique. The distribution of time-averaged mean-squared displacements is non-Gaussian, asymmetric, and has a finite trivial contribution from particles in crowded quasistatic surroundings. Results indicate weak ergodicity breaking, which along with ageing characterizes the nonstationary and out-of-equilibrium dynamics.

A lattice Boltzmann (LB) model with an efficient and accurate interface treatment for conjugate heat transfer across a thin wall between two different media is developed. The proposed interface treatment avoids fine meshing and computation within the thin layer; instead, the energy balance within the thin layer and the conjugate conditions on each interface are utilized to construct explicit updating schemes for the microscopic distribution functions of the LB model at the interior lattice nodes of the two media next to the thin layer. The proposed interface schemes reduce to the standard interface scheme for conjugate conditions in the literature in the limit of zero thickness of the thin layer, and thus it can be considered a more general interface treatment. A simplified version of the interface treatment is also proposed when the heat flux variation along the tangential direction of the thin layer is negligible. Three representative numerical tests are conducted to verify the applicability and accuracy of the proposed interface schemes. The results demonstrate that the intrinsic second-order accuracy of the LB model is preserved with the proposed interface schemes for thin layers with constant tangential fluxes, while for general situations with varying tangential fluxes, first-order accuracy is obtained. This interface treatment within the LB framework is attractive in conjugate heat transfer modeling involving thin layers for its simplicity, accuracy, and significant reduction in computational resources.

In aggregation-fragmentation processes, a steady state is usually reached. This indicates the existence of an attractive fixed point in the underlying infinite system of coupled ordinary differential equations. The next simplest possibility is an asymptotically periodic motion. Never-ending oscillations have not been rigorously established so far, although oscillations have been recently numerically detected in a few systems. For a class of addition-shattering processes, we provide convincing numerical evidence for never-ending oscillations in a certain region $\mathcal{U}$ of the parameter space. The processes which we investigate admit a fixed point that becomes unstable when parameters belong to $\mathcal{U}$ and never-ending oscillations effectively emerge through a Hopf bifurcation.