While modern physics and biology satisfactorily explain the passage from the Big Bang to the formation of Earth and the first cells to present-day life, respectively, the origins of biochemical life still remain an open question. Since life, as we know it, requires extremely long genetic polymers, any answer to the question must explain how an evolving system of polymers of ever-increasing length could come about on a planet that otherwise consisted only of small molecular building blocks. In this work we show that, under realistic constraints, an abstract polymer model can exhibit dynamics such that attractors in the polymer population space with a higher average polymer length are also more probable. We generalize from the model and formalize the notions of complexity and evolution for chemical reaction networks with multiple attractors. The complexity of a species is defined as the minimum number of reactions needed to produce it from a set of building blocks, which in turn is used to define a measure of complexity for an attractor. A transition between attractors is considered to be a progressive evolution if the attractor with the higher probability also has a higher complexity. In an environment where only monomers are readily available, the attractor with a higher average polymer length is more complex. Thus, by this criterion, our abstract polymer model can exhibit progressive evolution for a range of thermodynamically plausible rate constants. We also formalize criteria for open-ended and historically contingent evolution and explain the role of autocatalysis in obtaining them. Our work provides a basis for searching for prebiotically plausible scenarios in which long polymers can emerge and yield populations with even longer polymers. Additionally, the existence of features like history dependence and open endedness support the view that the path from chemistry to biology was one of gradual complexification rather than an instantaneous origin of life.

An investigation of the characteristics of the turbulent-nonturbulent interface (TNTI) in spatially developing compressible turbulent boundary layers is performed by using a direct numerical simulation. The mean thicknesses of the TNTI layer ${\delta }_{\mathrm{TNTI}}$ is approximately $13{\eta }_{\mathrm{ref}}$ (${\eta }_{\mathrm{ref}}$ is the Kolmogorov scale taken from a location of $0.3{\delta }_{\mathrm{ref}}$ from the outer edge of the TNTI layer, where ${\delta }_{\mathrm{ref}}$ is the nominal thickness of the boundary layer) and the two components of the TNTI layer, viscous superlayer ${\delta }_{\mathrm{VSL}}$ and turbulent sublayer ${\delta }_{\mathrm{TSL}}$, are approximately $3.6{\eta }_{\mathrm{ref}}$ and $9.4{\eta }_{\mathrm{ref}}$, respectively. The multiscale analyses of the TNTI shows that the interscale energy transfer ${\mathcal{T}}_{r_1}$ in the direction normal to the TNTI layer are from large to small scales, while ${\mathcal{T}}_{r_1}$ is expressed as energy input in the entire range of scales in the direction close to the interface's tangent plane. However, this is the opposite for that in ${\mathcal{T}}_{r_2}$, as the interscale energy transfer ${\mathcal{T}}_{r_2}$ in the direction normal to the TNTI layer are almost from small to large scales, while ${\mathcal{T}}_{r_2}$ is expressed as energy output in the entire range of scales in the direction close to the interface's tangent plane. The numerical results reported are expected to shed light on the accurate modeling and predicting of the spatially developing turbulent flows.

We compare model solutions of a forced Kadomtsev-Petviashvili (fKP) equation with experimental observations of dust acoustic precursor solitons excited by a supersonically moving charged cylindrical object in a dusty plasma medium. The fKP equation is derived from a three-fluid-Poisson model of the dusty plasma using the reductive perturbation technique and numerically solved for parameters close to the experimental investigations of cylindrical precursor solitons. The fKP model solutions show excellent agreement with the experimental results in reproducing the prominent geometric features of the two-dimensional solitons and closely matching the quantitative values of their velocities, amplitudes, and temporal evolutions. Our findings suggest that the fKP equation can serve as a very realistic model to investigate the dynamics of precursor solitons and can be usefully employed in practical applications such as space debris detection and tracking techniques that are based on observing/predicting nonlinear plasma excitations induced by the debris in the ionosphere.

We consider the problem of optimizing a set of passive superconducting coils (PSCs) with currents induced by a background magnetic field rather than power supplies. In the nuclear fusion literature, such coils have been proposed to partially produce the 3D magnetic fields for stellarators and provide passive stabilization. We perform the first optimizations of PSC arrays with respect to the orientation, shape, and location of each coil, jointly minimized with the background fields. We conclude by generating passive coil array solutions for four stellarators.

An advanced moving boundary truncated grid method is presented as an efficient finite-difference solver for the Boltzmann–Bhatnagar-Gross-Krook equation, specifically tailored for semiconductor device simulations. By dynamically adjusting the computational domain to include only the essential portions of phase space, the truncated grid method significantly reduces computational costs without compromising accuracy. Through this adaptive boundary approach, we integrate the Boltzmann-Poisson system in a GaAs homostructure under various scattering models, including constant relaxation times and polar-optical-phonon scattering, achieving quasiballistic transport simulations at a fraction of the computational expense of conventional methods. Computational results demonstrate that the truncated grid method achieves excellent agreement with full grid solutions, as well as substantial reductions in runtime, and faithfully captures key transport phenomena, such as quasiballistic effects, even in low-density conditions. Adaptability of the truncated grid method across different scattering scenarios, along with its general-purpose applicability, positions it as a robust tool for extending to the full Boltzmann transport equation and quantum kinetic models in future studies.

Neural networks have emerged as powerful tools across various disciplines, yet their application in deciphering complex physical laws remains underexplored. Addressing this, we introduce augmented physics-informed Hamiltonian networks (A-PIHNs), designed to adeptly learn the physical laws in Hamiltonian systems under complex perturbations. Utilizing Helmholtz-Hodge decomposition, A-PIHNs excel in identifying Hamiltonian dynamical systems and perturbed dynamical systems, thereby capturing the underlying physical laws more effectively than contemporary models like dissipative Hamiltonian neural networks (DHNNs). Our empirical results, spanning diverse strong perturbation scenarios, attest to A-PIHNs' superior accuracy and generalization capabilities. Furthermore, we regard the A-PIHNs model as a dynamical system. Specifically, we regard the error between the recovered dynamical systems and the true dynamical systems as a small perturbation of the proposed model, and conclude that the model can well approximate the Kolmogorov-Arnold-Moser (KAM) theory. This perspective highlights the untapped potential of neural networks for uncovering complex physical phenomena.

Emulsions are paramount in various interdisciplinary topical areas, yet a satisfactory understanding of their behavior in buoyancy-driven thermal flows has not been established. In the present work, we unravel the dynamical regimes of thermal convection in emulsions by leveraging a large set of mesoscale numerical simulations. Emulsions are prepared with a given volume fraction of the initially dispersed phase, $\varphi$, ranging from dilute (low values of $\varphi$) to jammed emulsions (high values of $\varphi$), resulting in different rheological responses of the emulsion, i.e., from Newtonian to non-Newtonian yield-stress behaviors, respectively. We then characterize the dynamics of the emulsions in the paradigmatic setup of the Rayleigh-Bénard convection, i.e., when confined between two parallel walls at different temperatures under the effect of buoyancy forces, the latter encoded in the dimensionless Rayleigh number $Ra$. We thoroughly investigated the dynamics of the emulsion in the changing of $\varphi$ and $Ra$. For a given $\varphi$, at increasing $Ra$, we observe that the emulsion exhibits convection states, where structural changes may appear (i.e., droplet breakup, coalescence, or phase inversion), which inevitably impact the emulsion rheology. For sufficiently high values of $Ra$, two states of convection are observed: for low/moderate values of $\varphi$ (Newtonian emulsions), we observe breakup-dominated dynamics, whereas for high values of $\varphi$ (non-Newtonian emulsions), we observe phase-inverted states. For both scenarios, the droplet size distribution depends on $Ra$, and scaling laws for the average droplet size are analyzed and quantified. Our results offer insights into the rich dynamics of emulsions under thermal convection, offering a detailed characterization of the various dynamical regimes to be expected and their relation with structural changes occurring in such complex fluids.

Microswimmers are often found in heterogeneous or crowded environments and show interesting transport behaviors like trapping or escaping in the presence of hydrodynamic interactions and thermal fluctuations. Studying such behaviors helps us to understand infection spreading and plant-microbial interactions and builds applications like targeted drug delivery. To explain the impact of thermal fluctuations, in terms of Péclet number and packing fraction, on the transport behavior of microswimmers in the presence of obstacles, we consider a rigid spherical squirmer swimming in an infinite matrix of rigid spheres arranged in a body-centered-cubic lattice structure with a multiparticle collision dynamics model for the background quiescent fluid. At a constant Péclet number, we observe ballistic or escape behavior for weak pusher, neutral, and puller types of microswimmers. A confined or trapped behavior is observed for strong pushers. The transition from trap to escape is more sensitive to the packing fraction in the case of low Péclet numbers compared to high Péclet numbers. At constant packing fraction, we observe an increase in the effective diffusion coefficient of the squirmers, which shows pure trap or escape behaviors, as the Péclet number increases. Interestingly, the superdiffusive behavior of strong pullers in the presence of obstacles gives the diffusion coefficient close to the squirmer in the absence of obstacles. For the squirmers transitioning between escape and trapped states, the diffusion coefficient increases when they are in the escape state and decreases when they are in the trapped state with an increase in the Péclet number.

Comparison of a few simple models of fluid and solid membranes illustrates how shear stresses can arise from a bending energy through a coupling between curvature and surface stresses, a feature incidental to the fluid or solid nature of the material. In particular, it is shown how a fluidlike Helfrich bending energy contributes shear stresses, while a related solidlike energy, the correct continuum limit of a Seung-Nelson discrete bending term, produces a stress tensor with a purely isotropic tangential part. A distinction is noted between the resistance to tangential flows on the surface and the ability to support tangential stresses. The tangential projection of the divergence of the stress, or the pseudomomentum balance, is viewed in the light of some statements and observations in the literature. Different types of constraints, invariance properties, and shape equations are briefly discussed. The state of a patch of unidirectionally bent material is connected with prior analyses of the pulling of membrane tethers.

In fluids under temperature gradients, long-range correlations (LRCs) emerge generically, leading to enhanced density fluctuations. This phenomenon, characterized by the ${\mathit{q}}^{-4}$ divergence in the static structure factor (where $\mathit{q}$ is the wavenumber), has been extensively studied both theoretically and experimentally. However, they remain unexplored in Hamiltonian particle systems using molecular dynamics (MD) simulations. This Letter reports the first MD study to provide unambiguous observations of the LRCs. We demonstrate this by three distinct approaches: (i) measuring the static structure factor and directly observing the ${\mathit{q}}^{-4}$ divergence characterizing the LRCs; (ii) detecting the corresponding ${\mathit{q}}^{-4}$ divergence in the dynamic structure factor; and (iii) establishing a quantitative agreement between MD results and predictions from fluctuating hydrodynamics, the phenomenological theory that predicts the LRCs. Our findings demonstrate that MD simulations offer a powerful complementary tool to theoretical and experimental investigations of LRCs.

Understanding diffusive mass transport in fluid flows is fundamental in several fields, ranging from virus particles in chromatographic test kits to nutrients in ocean currents. However, experimentally resolving diffusion processes in flowing fluids is challenging due to local sample deformation caused by advective displacements under varying shear rates. Here we demonstrate that differential dynamic microscopy, combined with image velocimetry, provides a robust method to quantitatively measure molecular diffusion coefficients across a laminar flow profile within a rectangular microfluidic channel. By using subresolution tracer particles homogeneously distributed along the flow profile, we capture the effects of differing shear rates across the flow profile and measure the local diffusion coefficient by scanning through different focal planes with a microscope. While demonstrated with water, this approach can be easily adapted to more complex fluids with shear-dependent viscosity, offering broad applicability for studying diffusion in dynamic environments.

Recent experimental demonstrations of ignition and target gain in inertial confinement fusion (ICF) have stimulated interest in exploring the fundamental physics of violent deuterium-tritium (DT) burn in high-gain ICF targets. A significant DT-burn fraction is a necessary condition for high energy gain and large neutron yields ($>100\mathrm{MJ}$). Using classical molecular-dynamics (MD) simulations and a hybrid fluid-kinetic model, we examine how a large fraction of low-energy $\alpha$ particles can kick D and T ions out of equilibrium in high-gain ICF targets. The MD results suggest that (1) temperatures of $T_D$ and $T_T$ can differ by as much as $\sim 20\%$ of their mean temperature and (2) the deviation of the DT energy distribution from the Maxwell-Boltzmann function can exceed $\sim 30\%$. Some of these MD observations, such as the preferential heating of D ions by low-energy $\alpha$ particles and the temperature separation, can be explained by a proposed hybrid fluid-kinetic model. The implication of such nonequilibrium effects on the DT reactivity is also discussed.

We make Kadanoff's block idea into a reliable three-dimensional (3D) real-space renormalization group (RG) method. Kadanoff's idea, expressed in spin representation, offers a qualitative intuition for clarifying scaling behavior in criticality, but has difficulty as a quantitative tool due to uncontrolled approximations. A tensor-network reformulation equips the block idea with a measure of RG errors. In 3D, we propose an entanglement filtering scheme to enhance such a block-tensor map, with the lattice reflection symmetry exploited. When the proposed RG is applied to the cubic-lattice Ising model, the RG errors are reduced to about 2% by retaining more couplings. The estimated scaling dimensions of the two relevant fields have errors 0.4% and 0.1% in the best case, compared with the accepted values. The proposed RG is promising as a systematically improvable real-space RG method in 3D. The unique feature of our method is its ability to numerically obtain a 3D critical fixed point in a high-dimensional tensor space. A fixed-point tensor contains much more information than a handful of observables estimated in conventional techniques for analyzing critical systems.

Activity-mediated unjamming of a confluent glassy system is crucial for several biological processes, such as embryogenesis and cancer metastasis. During these processes, the cells progressively change their junction properties, characterized by an interaction parameter $p_0$, and become motile. Here, we study the effect of nonequilibrium active fluctuations, in the form of self-propulsion, on the glassy dynamics in a confluent system. We simulate the active vertex model and use the analytical mode-coupling theory (MCT) to show that the nature of the transition in the presence of activity remains similar to that in a thermal system where the fluctuations are temperaturelike. The agreement of the simulation results with the MCT predictions demonstrates that the structure-dynamics feedback mechanism controls the relaxation dynamics. In addition, we present the first computation of a dynamic length scale, ${\xi }_d$, in confluent systems using finite-size scaling, and show that the growing relaxation time exhibit a power-law dependence on ${\xi }_d$. Furthermore, unlike particulate glasses, the static length that governs the finite-size scaling of the relaxation time is proportional to ${\xi }_d$, revealing the unique nature of the glassy dynamics in confluent systems.

Liouvillian dynamics describes the evolution of a density operator in closed quantum systems. One extension toward open quantum systems is provided by the Lindblad equation. It is applied to various systems and energy regimes in solid-state physics as well as in nuclear physics. A main challenge is that analytical solutions for the Lindblad equation are only obtained for harmonic system potentials or two-level systems. For other setups one has to rely on numerical methods. In this work, we propose to use a method from computational fluid dynamics, the Kurganov-Tadmor central (finite-volume) scheme, to numerically solve the Lindblad equation in position-space representation. We will argue that this method is advantageous in terms of the efficiency concerning initial conditions, discretization, and stability. On the one hand, we study the applicability of this scheme by performing benchmark tests. Thereby we compare numerical results to analytical solutions and discuss aspects like boundary conditions, initial values, conserved quantities, and computational efficiency. On the other hand, we also comment on new qualitative insights into the Lindblad equation from its reformulation in terms of an advection-diffusion equation with source and sink terms.

In Bild, Deckert, and Ruhl [Phys. Rev. D 99, 096001 (2019)] we presented an explicit and nonperturbative derivation of the classical radiation reaction force for a cutoff modeled by a special choice of tube of finite radius around the charge trajectories. In this paper, we provide a further, simpler, and so-called reduced radiation reaction model together with a systematic numerical comparison between both the respective radiation reaction forces and the one of Landau-Lifschitz as a reference. We explicitly construct the numerical flow for the new forces and present the numerical integrator used in the simulations, a Gauss-Legendre method adapted for delay equations. As comparison, we consider the cases of a constant electric field, a constant magnetic field, and a plane wave. In all these cases, the deviations between the three force laws are shown to be small. This excellent agreement is an argument for plausibility of both new equations but also means that an experimental differentiation remains hard. Furthermore, we discuss the effect of the tube radius on the trajectories, which turns out to be small in the regarded regimes. We conclude with a comparison of the numerical cost of the three corresponding integrators and argue that, numerically, the integrator of the reduced radiation reaction seems most efficient and the integrator of Landau-Lifschitz least efficient.

In this paper, a bond-based peridynamic approach is used to simulate the rupture of cellular materials, such as plant tissues, considered as aggregates of cells bound together by a cohesive matrix. The main objective is to clarify how mechanical properties and fracture are affected by the toughness ratio between cell walls and matrix. Cell microstructures are generated using an approach combining the discrete element method to define a spatial distribution of sites (particle centers) and a Voronoi-Laguerre tessellation built on these sites to generate the cell topology. A parametric study is then carried out by varying the toughness ratio $\beta$ by a factor of five, with ten repetitions per $\beta$ value. The modulus of elasticity evolves as a power-law function of $\beta$. Stress distributions and maps show that stress concentrations are strongly influenced by pore distribution and only weakly by $\beta$. An important point was to highlight the dependence of fracture regimes on the $\beta$ parameter, with a value of $\beta \simeq 3$ separating a regime where the fracture passes through the walls and a regime where it bypasses the cells and remains confined within the cohesive matrix, as has been observed in the literature.