arXiv Physics @arxiv_physics@qoto.org

In this article, we present a multiscale characterization of the streamwise velocity of a turbulent channel flow. We study the 2nd and 4th order structure functions and the flatness for scales ranging from the dissipative to the integral domains and for a wide range of distances to the walls spanning four distinct regions of the channel. We characterize the impact of the shear stress induced by the walls on these statistics. Far from the walls, in the outer layer, the impact of the boundaries on the flow is negligible and the flow statistics follow the Kolmogorov-Obukhov theory. In the viscous, buffer and logarithmic regions, the inertial domain can be split in two subdomains of scales with two different statistical behaviors. In the logarithmic region, the scaling of the structure functions agrees with the model of Davidson et al. 2006 but the scaling of the flatness seems to better correspond to the characterization of intermittency proposed by Kolmogorov and Obukhov in 1962. The structure functions and flatness of the streamwise velocity in the buffer and viscous regions are studied for the first time. We show the strong non-Gaussianity of the velocity flow at any scale in the viscous layer with strong intermittent events that may correspond to high shear-induced dissipation.

Numerical simulations of turbulent fluids are paramount to real-life applications, from predicting and modeling flows to diagnostic purposes in engineering. However, they are also computationally challenging due to their intrinsically non-linear dynamics, which requires a very high spatial resolution to accurately describe them. A promising idea is to represent flows on a discrete mesh using tensor trains (TTs), featuring a convenient scaling of the number of parameters with the mesh size. However, it is yet not clear how the compression power of TTs is affected by the complexity of the flows, measured by the Reynolds number. In fact, no TT fluid solver has been extensively validated in a fully developed turbulent regime yet. We fill this gap. We conduct a comprehensive analysis of TTs as an Ansatz to compress, simulate, and synthetically generate fiducial turbulent snapshots in 3D. Specifically, first, we exhaustively investigate the effect of TT compression of given snapshots on key turbulence signatures, including the energy spectrum and different accuracy metrics. Second, we present a TT solver to simulate time evolution of 3D fluid fields according to the incompressible Navier-Stokes equations entirely within the compressed representation. Third, we develop a TT algorithm to generate artificial snapshots displaying all the signatures of turbulence. In all three cases, a number of parameters scaling polylogarithmically with the mesh size is enough for accurate descriptions. Our findings confirm that fluids in truly turbulent regimes admit an efficient TT description and offer a powerful, quantum-inspired toolkit for their computational treatment.

High-fidelity simulation of nonequilibrium plasmas -- crucial to applications in electric propulsion, hypersonic re-entry, and astrophysical flows--requires state-specific collisional-radiative (CR) kinetic models, but these come at a prohibitive computational cost. Traditionally, this cost has been mitigated through empirical or physics-based simplifications of the governing equations. However, such approaches often fail to retain the essential features of the original dynamics, particularly under strong nonequilibrium conditions. To address these limitations, we develop a Petrov-Galerkin reduced-order model (ROM) for CR argon plasma based on oblique projections that optimally balance the covariance of full-order state trajectories with that of the system's output sensitivities. This construction ensures that the ROM captures both the dominant energetic modes and the directions most relevant to input-output behavior. After offline training in a zero-dimensional setting using nonlinear forward and adjoint simulations, the ROM is coupled to a finite-volume solver and applied to one- (1D) and two-dimensional (2D) ionizing shock-tube problems. The ROM achieves a 3$\times$ reduction in state dimension and more than one order of magnitude savings in floating-point operations, while maintaining errors below 1% for macroscopic quantities. In both 1D and 2D, it robustly reproduces complex unsteady plasma features--such as periodic fluctuations, electron avalanches, triple points, and cellular ionization patterns--in contrast to standard ROM strategies, which become unstable or inaccurate under these challenging conditions. These results demonstrate that the proposed projection-based ROM enables substantial model compression while preserving key physical mechanisms in nonequilibrium plasma physics, paving the way for fast, reliable simulation of high-speed plasma flows.

Three-dimensional magnetic imaging with high spatio-temporal resolution is critical for probing current paths in various systems, from biosensing to microelectronics. Conventional 2D Fourier-based current source localization methods are ill-posed in multilayer or dynamic systems due to signal overlap and noise. In this work, we demonstrate an innovative nitrogen-vacancy (NV) center-based wide-field magnetic microscopy technique for dynamic three-dimensional imaging and localization of current sources. Using custom-fabricated multilayer micro-coil platform to emulate localized, time-varying currents similar to neuronal activity, we acquire magnetic field maps with micrometre-scale spatial and millisecond-scale temporal resolution using per-pixel lock-in-based detection. Source localization and image reconstruction are achieved using a Least Absolute Shrinkage and Selection Operator (LASSO)-based reconstruction framework that incorporates experimentally measured basis maps as spatial priors. Our method enables robust identification of active current sources across space and time, and significantly advances the accuracy of dynamic 3D current imaging and NV-based magnetometry for complex systems.

We present an isotropic ab initio (para-H$_2$)$_4$ four-body interaction potential energy surface (PES). The electronic structure calculations are performed at the correlated coupled-cluster theory level, with single, double, and perturbative triple excitations. They use an atom-centred augmented correlation-consistent double zeta basis set, supplemented by a $(3s3p2d)$ midbond function. We use a multilayer perceptron to construct the PES. We apply a rescaling transformation to the output energies during training to improve the prediction of weaker energies in the sample data. At long distances, the interaction energies are adjusted to match the empirically-derived four-body dispersion interaction. The four-body interaction energy at short intermolecular separations is net repulsive. The use of this four-body PES, in combination with a first principles pair potential for para-H$_2$ [J. Chem. Phys. 119, 12551 (2015)], and an isotropic ab initio three-body potential for para-H$_2$ [J. Chem. Phys. 156, 044301 (2022)], is expected to provide closer agreement with experimental results.

The TJ toroidal tearing mode code is used to make realistic predictions of the electron cyclotron emission (ECE) signals generated by neoclassical tearing modes (NTMs) in an ITER-like tokamak plasma equilibrium. In the so-called "outer region'', which comprises the bulk of the plasma, helical harmonics of the magnetic field with the same toroidal mode number as the NTM, but different poloidal mode numbers, are coupled together by the Shafranov shifts and shaping of the equilibrium magnetic flux-surfaces. In the "inner region'', which is localized in the vicinity of the NTM rational surface, helical harmonics whose poloidal and toroidal mode numbers are in the same ratio as those of the NTM are coupled together nonlinearly to produce a radially asymmetric magnetic island chain. The solutions in the inner and outer regions are asymptotically matched to one another. The asymptotic matching process determines the overall magnetic structure of the NTM, as well as the global perturbation to the electron temperature caused by the mode. A simulated ECE diagnostic is developed that accounts for the downshifting and broadening in frequency of the signal due to the relativistic mass increase of the emitting electrons.

We present path integral Monte Carlo simulation results for the equation of state of solid parahydrogen between $ 0.024 \, {Å}^{-3} $ and $ 0.1 \, {Å}^{-3} $ at $ T = 4.2 \, $ K. The simulations are performed using non-additive isotropic ab initio two-body, three-body, and four-body potential energy surfaces (PES). We apply corrections to account for both the finite size simulation errors and the Trotter factorization errors. Simulations that use only the two-body PES during sampling yield an equation of state similar to that of simulations that use both the two-body and three-body PESs during sampling. With the four-body interaction energy, we predict an equilibrium density of $ 0.02608 \, {Å}^{-3} $, very close to the experimental result of $ 0.0261 \, {Å}^{-3} $. The inclusion of the four-body interaction energy also brings the simulation results in excellent agreement with the experimental pressure-density data until around $ 0.065 \, {Å}^{-3} $, beyond which the simulation results overestimate the pressure. These PESs overestimate the average kinetic energy per molecule at the equilibrium density by about $ 7 \% $ compared to the experimental result. Our findings suggest that, at higher densities, we require five-body and higher-order many-body interactions to quantitatively improve the agreement between the pressure-density curve produced by simulations, and that of experiment. Using the four-body PES during sampling at excessively high densities, where such higher-order many-body interactions are likely to be significant, causes an artificial symmetry breaking in the hcp lattice structure of the solid.

Networks of physical units can vary from a stationary set of spatially-embedded links to a collection of mobile agents that undergo transient social interactions. In living cells, mitochondria form architectures that span across these regimes, transitioning between fragmented, partly connected, and highly fused structures depending on cell type and state. Diffusive transport of biomolecular components through these networks helps to homogenize the mitochondrial population. Here we address the connection between dynamic network architecture and the rate of diffusive mixing through simulations and analytic models that incorporate fusion, fission, and rearrangement. We find that the material delivered from a source to the rest of the network depends on the network dimensionality and a balance of competing timescales for encounter, fusion, and diffusive dispersion. These results provide a quantitative basis for predicting the homogenization of proteins, lipids, ions, or genetic material through the mitochondrial population. The general principles identified in this work capture diffusive spreading through both social and physical networks, unifying a continuum of spatial network architectures.

Machine-learned interatomic potentials (MLIPs) are revolutionizing computational materials science and chemistry by offering an efficient alternative to {\em ab initio} molecular dynamics (MD) simulations. However, fitting high-quality MLIPs remains a challenging, time-consuming, and computationally intensive task where numerous trade-offs have to be considered, e.g. How much and what kind of atomic configurations should be included in the training set? Which level of {\em ab initio} convergence should be used to generate the training set? Which loss function should be used for fitting the MLIP? Which machine learning architecture should be used to train the MLIP? The answers to these questions significantly impact both the computational cost of MLIP training and the accuracy and computational cost of subsequent MLIP MD simulations. In this study, we highlight that simultaneously considering training set selection strategies, energy versus force weighting, precision of the {\em ab initio} reference simulations, as well as model complexity and computational cost of MLIPs can lead to a significant reduction in the overall computational cost associated with training and evaluating MLIPs. This opens the door to computationally efficient generation of high-quality MLIPs for a range of applications which demand different accuracy versus training and evaluation cost trade-offs.

Flow-induced vibration (FIV) commonly occurs in rigidly coupled twin-pipe structures. However, the limited understanding of their FIV responses and hydrodynamic features presents a major challenge to the development of reliable engineering designs. To bridge this gap, the present study systematically investigates the FIV characteristics of a rigidly coupled twin-pipe model with elastic support using a virtual physical framework (VPF), which enables flexible control of structural parameters during physical testing. A distinctive feature of twin-pipe structures is the presence of in-line hydrodynamic interactions and torsional moments arising from the rigid coupling. The in-line interaction is primarily compressive and becomes more pronounced as the mass ratio increases. The torsional moment coefficient exhibits a rise-fall trend with increasing reduced velocity $U_R$ and stabilizes around 0.46 at low mass ratios. In addition, an "amplitude drop" phenomenon is observed at $U_R=6$, attributed to energy dissipation from the downstream pipe. The mass ratio significantly affects FIV amplitude, frequency, and hydrodynamic coefficients. As the mass ratio decreases, the synchronization region broadens and the hydrodynamic coefficients become more stable. At mass ratio of 1.0, a "resonance forever" behavior is observed. Damping primarily suppresses FIV amplitude, with minimal impact on dominant frequency and hydrodynamic coefficients. These findings provide valuable insights into twin-pipe FIV mechanisms and support a scientific basis for future structural design optimization.

Non-Hermitian systems have recently shown new possibilities to manipulate wave scattering by exploiting loss, yet coherent perfect absorption at an exceptional point (CPA EP) remains elusive in acoustics. Here we demonstrate it based on a two-channel waveguide with compact lossy resonators. We realize imbalanced losses crucial for CPA EP by using active components to independently modulate the non-Hermiticity. The CPA EP experimentally manifests as full absorption at a unique real frequency and shows high sensitivity to the incident phase variations.Our findings open an avenue to explore novel non-Hermitian physics for classical waves and develop innovative acoustic singularity-based devices.

Optical Diffraction Tomography (ODT) is a powerful non-invasive imaging technique widely used in biological and medical applications. While significant progress has been made in transmission configuration, reflection ODT remains challenging due to the ill-posed nature of the inverse problem. We present a novel optimization algorithm for 3D refractive index (RI) reconstruction in reflection-mode microscopy. Our method takes advantage of the multiply-scattered waves that are reflected by uncontrolled background structures and that illuminate the foreground RI from behind. It tackles the ill-posed nature of the problem using weighted time loss, positivity constraints and Total Variation regularization. We have validated our method with data generated by detailed 2D and 3D simulations, demonstrating its performance under weak scattering conditions and with simplified forward models used in the optimization routine for computational efficiency. In addition, we highlight the need for multi-wavelength analysis and the use of regularization to ensure the reconstruction of the low spatial frequencies of the foreground RI.

The presented approach aims at estimating the lateral variation of seismic velocities for a seismic timelapse survey. The monitor survey is depth migrated with the known velocity model of the base survey. An analysis is performed to determine whether the moveout to be expected for the monitor survey after residual prestack depth migration (RPSM) can be used for the velocity determination of the formation of interest. In that case the migrated response for the base horizon of the monitor survey is analyzed in order to determine the velocity distribution by employing residual migration concepts. With the use of Fresnel apertures a rather local application is possible at comparatively low signal to noise ratios. The approach is successfully demonstrated for two isotropic depth models both for a lens-like and for a faulted reservoir structures. In both cases the velocities of the monitor survey is recovered accurately. In addition it is demonstrated that an iterative application is possible and that for these two models even severe compaction of the model has little influence on the velocity estimation.

Poroelasticity can be classified with geophysics and describes the interaction between solids deformation and the pore pressure in a porous medium. The investigation of this effect is anywhere interesting where a porous medium and a fluid come together into play, for example this is the case in geothermics. More precisely, it is an important aspect in reservoir management since the replacement of the water in the reservoir some kilometers below the Earth's surface has an effect on the surrounding material and of course displacement of the solid increases or decreases the pore pressure. The underlying physical processes are deduced with the help of linear elasticity, conservation of linear momentum, conservation of mass and Darcy's law. They result in partial differential equations, called the quasistatic equations of poroelasticity (QEP). In this paper, we want to do a multiscale decomposition of the components displacement and pore pressure. This should provide us with more information about the data that means visualize underlying structures in the different decomposition scales that cannot be seen in the whole data. The aim is to detect interfaces and extract more details of the data. For this purpose, we construct physically motivated scaling functions by mollifying the appropriate fundamental solutions. Here we have a closer look at the scaling functions fulfilling the necessary theoretical requirements of an approximate identity. The corresponding wavelets are constructed by subtraction of two consecutive scaling functions.

In this short article, I leverage the National Crime Victimization Survey from 1992 to 2022 to examine how income, education, employment, and key demographic factors shape the type of crime victims experience (violent vs property). Using balanced classification splits and logistic regression models evaluated by F1-score, there is an isolation of the socioeconomic drivers of victimization "Group A" models and then an introduction of demographic factors such as age, gender, race, and marital status controls called "Group B" models. The results consistently proves that higher income and education lower the odds of violent relative to property crime, while men younger individuals and racial minorities face disproportionately higher violentcrime risks. On the geographic spectrum, the suburban models achieve the strongest predictive performance with an accuracy of 0.607 and F1 of 0.590, urban areas benefit from adding education and employment predictors and crime in rural areas are still unpredictable using these current factors. The patterns found in this study shows the need for specific interventions like educational investments in metropolitan settings economic support in rural communities and demographicaware prevention strategies.

It is shown that linear instability of plane Couette flow can take place even at finite Reynolds numbers which meets with known experimental data. This new result of the linear theory of hydrodynamic stability is obtained only due by abandoning traditional assumption of the longitudinal periodicity of disturbances in the flow direction.

This work focuses on estimating soil properties from water moisture measurements. We consider simulated data generated by solving the initial-boundary value problem governing vertical infiltration in a homogeneous, bounded soil profile, with the usage of the Fokas method. To address the parameter identification problem, which is formulated as a two-output regression task, we explore various machine learning models. The performance of each model is assessed under different data conditions: full, noisy, and limited. Overall, the prediction of diffusivity $D$ tends to be more accurate than that of hydraulic conductivity $K.$ Among the models considered, Support Vector Machines (SVMs) and Neural Networks (NNs) demonstrate the highest robustness, achieving near-perfect accuracy and minimal errors.

In seismically active regions, the accumulation of geophysical stress during the aseismic period and its impact on faults is crucial for identifying which faults are more likely to undergo future fault movement. In this model, we consider an infinite non-planar fault located in a viscoelastic half-space of a fractional Maxwell medium representing the lithosphere-asthenosphere system comprising three interconnected planar sections. The problem is formulated as a two-dimensional boundary value problem with discontinuities along the fault plane. A numerical solution is obtained using a Laplace transformation, fractional derivative and the correspondence principle. The results have been illustrated graphically using suitable model parameters. The computational findings highlight the significant influence of fault movement and geometry on displacement, stress and strain components in the region surrounding the fault plane. A study has been carried out to investigate how non-planar faults influence displacement and the accumulation of stress and strain. Analysis of these results can provide insights into subsurface deformation and its impact on fault movement, which may contribute to the study of earthquake activity.

The inaugural edition of the MIT Quantum Index Report (QIR). Quantum technologies are evolving from theoretical concepts into tangible technologies with commercial promise. Their rapid progress is capturing global attention and suggests we stand on the cusp of a second quantum revolution. Unlocking the quantum opportunity is not simple. One challenge is that quantum technologies can present a high barrier to understanding for nonexperts because they often rely on complex principles and concepts from a variety of specialist fields. This can lead to confusion and intimidation for business leaders, educators, policymakers and others. The Quantum Index Report aims to reduce the complexity and make it possible for a wider audience to have a deeper understanding of the quantum landscape. The Quantum Index Report provides a comprehensive, data-driven assessment of the state of quantum technologies. For this inaugural edition we have focused on quantum computing and networking. The report tracks, measures, and visualizes trends across research, development, education and public acceptance. It aggregates data from academia, industry and policy sources and aims to provide nonpartisan insights.

Boltzmann's work, ``Ueber die sogenannte H-Curve," discusses his demonstration of the essential characteristics of the H-curve in a clear, concise, and precise style, showcasing his efforts to persuade his peers. To make these findings more widely accessible, the author aims to provide a translated version of the original article, while also correcting some typographical errors in the mathematical expressions with explanatory footnotes. The final section offers concluding remarks with graphs and relevant references for interested readers.