Papers

We use the Binary Intrinsic Dimension (BID), a geometrical measure designed for binary data, to analyze the Hopfield model, a paradigmatic spin system from statistical mechanics, machine learning and neuroscience. The BID allows us to characterize the phases and transitions of this system, and moreover it is robust against finite-size effects that interfere with the correct numerical estimation of the spin-glass order parameter ($q$). We observe that the BID scales linearly with system size in the retrieval and paramagnetic phases, where the correlations between spins are small, and exhibits sublinear scaling in the whole spin-glass phase, highlighting its correlated structure. Furthermore, we establish a direct relationship between the BID and the overlap distribution, unveiling a novel connection between the geometry of the state-space and standard spin order parameters.

Transition metal dichalcogenides (TMDs) and other van der Waals (vdW) semiconductors enable transfer-printed, lattice-mismatch-free stacking of many photovoltaic junctions, motivating a re-examination of multijunction detailed-balance limits under realistic material and optical constraints. Here, we develop a unified thermodynamic framework for a multijunction photovoltaic device, which can define a clear set of device-window constraints, optical boundary conditions, and luminescence/entropy penalties and therefore define how closely any realistic multijunction photovoltaic device can approach multicolor limit. By applying it to a conservative TMD bandgap window (1.0-2.1~eV), we show that the accessible bandgap window imposes a large-junction number (N) efficiency limit: under full concentration, unconstrained ladders approach 84.5% at N=50, whereas the TMD window plateaus near 63.4%. This efficiency plateau is set by photons outside the bandgaps, so radiative quality and optics dominate beyond N=5 junctions for realistic transfer-printed device stacks. We identify an experimentally achievable N=5 ladder Eg~(2.10,1.78,1.50,1.24, 1.00)eV and map each rung to candidate vdW/TMD absorbers. Using reciprocity and luminescence thermodynamics, we quantify penalties from finite external radiative efficiency, two-sided emission, and luminescent coupling, and introduce the upward-emitted luminescence power as an indicator of entropy-loss proxy. Incorporating excitonic absorptance and nanophotonic thickness bounds yields practical thickness and light-management targets for transfer-printed stacks. Finally, inserting an idealized nonreciprocal multijunction model into the reciprocity-optimized ladders provides conservative efficiency advantage estimates, which are consistent with negligible benefit for single junctions but measurable efficiency gains for multijunction TMD devices.

The mid-infrared (MIR) spectral regime is central to applications including remote sensing, precision spectroscopy, higher harmonic generation, and free-space optical communication. However, coherent and broadband MIR modulation remains challenging owing to high optical loss, limited bandwidth, and large drive voltages in existing platforms. Here, we overcome these challenges by deploying a suspended thin-film lithium-niobate (TFLN) based electro-optic (EO) platform co-designed with high-performance traveling-wave microwave (MW) electrodes. We demonstrate a record-low Vpi,DC of 2.3 to 4.3 V over a broadband MIR bandwidth from 2.4 to 3.6 um, and a 2.7 dB EO bandwidth of 40 GHz (extracted 3 dB bandwidth of 50 GHz), yielding a figure of merit of 17.4 GHz/V, more than an order of magnitude higher than the state of the art. We demonstrate, for the first time, high-frequency Vpi,MW of 4.5 to 6.5 V in the 25 to 35 GHz range, and frequency-agile MIR EO frequency comb generation with a 10 dB optical bandwidth over 0.8 THz using a suspended phase modulator of 4 cm active modulation length. We further validate the platform in a free-space optical communication link. Our results establish a monolithic MIR photonic platform capable of powerful EO modulation and spectral synthesis, and represent a significant step toward reconfigurable MIR sensing and communication systems on chip.

Reaction-diffusion (RD) systems provide fundamental models for understanding self-organized spatiotemporal patterns across natural and engineered settings, yet reliable parameter estimation remains challenging, particularly when observations are sparse, noisy, and restricted to a subset of state variables. We introduce CLIP (Curriculum Learning Identification via PINNs), a physics-guided framework built on physics-informed neural networks for joint parameter inference and hidden-state reconstruction under partial observability. Leveraging the physical separability of RD systems, the CLIP training progresses from reaction-dominated regimes to full spatiotemporal dynamics using curriculum learning and an anchored widening transfer strategy. Across three canonical reaction-diffusion benchmarks, CLIP achieves more accurate and robust identification than baseline methods. Furthermore, the CLIP framework is successfully applied to infer the dynamics of the Min system in bacteria, where only membrane-bound species are observed and key kinetic rates span multiple orders of magnitude. Ablation experiments and loss-landscape visualizations demonstrate that both the curriculum stages and the anchored transfer are essential for stable convergence.

Photonic time crystals realize a continuum of momentum-resolved SU(1,1) parametric amplifiers. We show that a classical quantity, the Petermann factor of the effective Floquet Bogoliubov de Gennes (BdG) dynamical matrix, sets the scale of their quantum noise. In stable bands it fixes the Bogoliubov mixing and hence the mean bare-photon occupation of the Floquet vacuum, while in momentum gaps it sets the photon-number prefactor and enhances the squeezing dynamics, with the Floquet growth rate setting the time scale. This converts classical measurements of mode nonorthogonality into quantitative predictions for squeezing and photon generation, and offers a compact design parameter for engineering quantum resources in two-mode BdG platforms.

Cellular automata (CA), originally developed as computational models of natural processes, have become a central subject in the study of complex systems and generative visual forms. Among them, the Ulam-Warburton Cellular Automaton (UWCA) exhibits recursive growth and fractal-like characteristics in its spatial evolution. However, exact self-similar fractal structures are typically observable only at specific generations and remain visually obscured in conventional binary renderings. This study introduces a Recursive Gradient Profile Function (RGPF) that assigns grayscale values to newly activated cells according to their generation index, enabling latent self-similar structures to emerge cumulatively in spatial visualizations. Through this gradient-based mapping, recursive geometric patterns become perceptible across scales, revealing fractal properties that are not apparent in standard representations. We further extend this approach to UWCA variants with alternative neighborhood configurations, demonstrating that these rules also produce distinct yet consistently fractal visual patterns when visualized using recursive gradient profile. Beyond computational analysis, the resulting generative forms resonate with optical and cultural phenomena such as infinity mirrors, video feedback, and mise en abyme in European art history, as well as fractal motifs found in religious architecture. These visual correspondences suggest a broader connection between complexity science, computational visualization, and cultural art and design.

We consider the wake flow past a thin two-dimensional flat plate normal to the uniform stream and demonstrate that this flow is turbulent already at a relatively low Reynolds number of $Re = 400$. This is achieved by performing a careful comparison of the results of a DNS of this flow with experimental measurements of wake flows in the same geometric configuration at the Reynolds numbers of $Re=12500, 19700$. This comparison reveals that the distribution of several key quantities, including the mean velocity, Reynolds stresses and different effects contributing to the transport of the turbulent kinetic energy, are, up to measurement uncertainty, the same in these flows. Moreover, the wake flow at $Re = 400$ also features energy spectra characteristic of turbulent flows with intermittency detected in the distributions of the fluctuating strain and rotation rates. In contrast, these features are absent from the results of the DNS of the wake flow at $Re = 150$ where the distribution of the key quantities is also fundamentally different. These results show that the path to transition to turbulence in the wake past a thin flat plate is different from that in the wakes of canonical (i.e., circular or square) cylinders. We also identify possible physical mechanisms that may be responsible for these differences.

Reaction-diffusion processes play an important role in a variety of physical, chemical, and biological systems. Conventionally, the kinetics of these processes are described by the law of mass action. However, there are various cases where these equations are insufficient. A fundamental challenge lies in accurately accounting for the microscopic correlations that inevitably arise in bimolecular reactions. While approaches to describe microscopic correlations in many specific cases exist, no general theory for multistage reactions has been established. In this article, we apply the quantum field theory approach to derive kinetic equations for general multistage reactive systems termed CMET (complete modified encounter theory). CMET can be formulated as a set of coupled partial differential equations that can be easily integrated numerically, thereby serving as a versatile tool for investigating reaction-diffusion processes. Across multiple case studies, we demonstrated that CMET reproduces the kinetics predicted by many other theories within their respective scopes of applicability.

Guidance relating to the safe distance within which overhead conductors (OHCs) should not be built with respect to a wind turbine, and vice versa, varies from country to country. In the United Kingdom the recommendation is that OHCs are not installed within three rotor diameters (3D) of wind turbines due to concerns over wake-induced fatigue. To assess the physical basis for this recommendation, wind-tunnel experiments were conducted using a scaled wind turbine placed upstream of an instrumented conductor, in conditions representative of forested terrain. The conductor was equipped with distributed fibre-optic strain sensing based on Rayleigh backscattering, providing spatially resolved measurements at 2.6 mm resolution. Three conductor heights and four turbine-conductor spacings were tested, together with a no-turbine baseline, while maintaining a constant incident wind speed of 7 m/s. The results identify a critical region near the clamp where mean strain is maximised. Neither mean nor fluctuating strain at this location increased substantially due to the presence of the turbine wake. Strain fluctuations were dominated by aeolian vibration, with the wake increasing vibration amplitude whilst reducing its characteristic frequency. Rainflow counting fatigue analysis shows that damage accumulation depends strongly on conductor height. While fatigue damage rates increase when the conductor is fully immersed in the wake, reduced damage rates are observed for lower heights and closer spacings. These results suggest that, under forested atmospheric conditions, conductor-turbine separations smaller than the current 3D guidance may be feasible provided the conductor is not fully immersed in the turbine wake.

The fidelity of collisional-radiative (CR) models is critical for advancing our understanding of radiative properties and ionization balance in fusion plasmas. In this work, we present and evaluate hybrid CR schemes that combine fine-structure resolution with superconfiguration averaging, offering a practical compromise between accuracy and computational efficiency. Two hybrid CR models are developed for helium, lithium, and beryllium, retaining detailed fine-structure states up to selected principal quantum numbers, while higher-lying states are statistically averaged to form superconfigurations. These models are applied to compute radiative power loss, as well as average and effective charge states, across a wide range of electron temperatures and densities. The results are benchmarked against a fully fine-structure-resolved CR model to assess the accuracy of the hybrid approach. The findings demonstrate the versatility of hybrid CR schemes and their suitability for detailed plasma simulations where predictive fidelity must be balanced with computational cost.

The pedestrian flow is one of the most complex systems, involving large populations of interacting agents. Models at microscopic and macroscopic scales offer different advantages for studying related problems. In general, microscopic models can describe interaction forces at the individual level. Macroscopic models, on the other hand, provide analytical insights into global interactions and long-term overall dynamics, along with efficient numerical simulations and predictions. However, the relationship between models at different scales has rarely been explored. In this study, based on the original microscopic social force model with a reactive optimal route choice strategy, we first derive kinetic equations at the mesoscopic level. By varying the interaction force in different scenarios, we then derive several continuum models at the macroscopic level. Finally, numerical examples are given to evaluate the behaviors of the social force model and our continuum models.

Circular dichroism (CD) sensing plays a pivotal role in probing molecular chirality in biomedical sciences. However, engineering superchiral electromagnetic fields that can reliably amplify the faint signatures of chiral analytes remains profoundly challenging. Central to this difficulty is the need to balance two competing demands: maximizing the enhancement of chiral fields while maintaining a sufficiently large interaction volume for effective molecular interrogation. Here, we introduce a figure of merit (FOM) that captures the enhancement and spatial coverage of superchiral fields to benchmark different chiral-field configurations. We examine the effects of helix-geometry evolution on the FOM, including 2D to 3D chirality induction, winding-number escalation, helical-order enhancement, and transverse dilation. By tuning these structural degrees of freedom, the sensing volume can be enlarged without compromising the distribution and enhancement strength of fields. The optimized triple-strand helix markedly enhanced the analyte CD signal, yielding a FOM of 2.43*10^10 nm3, which surpassed prior 2D and 3D configurations by over an order of magnitude. The proposed FOM exhibits a strong linear correlation (R^2 = 0.9256) with the analyte CD signal. Our findings provide a systematic design framework for 3D chiral structures and a robust metric for assessing their chiroptical sensing performance, particularly in scenarios involving clusters of randomly oriented small molecules or a large chiral molecule.

We calculate the scalar transport rate, as characterized by the Nusselt number\,($Nu$), from a neutrally buoyant spherical drop in an ambient linear flow, in the absence of inertia and in the strong convection limit. This corresponds to the regime $Re \ll 1, Pe \gg 1$, where $Re$ and $Pe$ are the Reynolds and Péclet numbers, and denote the ratios of the diffusive and convective time scales associated with momentum and scalar transport. The focus is on the exterior problem, with the drop-phase transport resistance assumed negligible, and the scalar field being a constant on the drop surface. While $Nu \propto Pe^{\frac{1}{2}}$ for $Pe \gg 1$, owing to the transport occurring across a thin $O(a Pe^{-\frac{1}{2}})$ boundary layer\,($a$ being the drop radius), the proportionality factor in this relation depends sensitively on ambient flow geometry via the surface-streamline topology. Unlike a rigid sphere, a variety of surface-streamline topologies can drive transport across the boundary layer at widely differing rates. In contrast to earlier studies which almost exclusively focus on axisymmetric ambient flows, we calculate $Nu$ for a pair of non-axisymmetric linear flow families: (i) 3D extensional flows with aligned vorticity and (ii) Axisymmetric extensional flows with inclined vorticity, using a non-orthogonal surface-streamline aligned coordinate system. Taken together, the families span the entire gamut of surface-streamline topologies in the space of incompressible linear flows. Independent numerical simulations of the interior problem reveal the emergence of an $O(aPe^{-\frac{1}{2}})$ boundary layer beneath the drop surface, driven by chaotic streamlines, pointing to the possibility of $Nu \propto Pe^{\frac{1}{2}}$ for the conjugate problem, for sufficiently large $Pe$.

Nowcasting the evolution of severe convective storms over the first few hours after observations are collected is critical for issuing timely, accurate warnings. Forecast accuracy is hindered because the variables that matter most, such as rain, hail, and lightning, are imperfectly represented by numerical model physics and are difficult to initialize correctly. Here we introduce StormScope, a computationally efficient generative forecasting system that learns storm-scale evolution directly from satellite, radar, and lightning observations over the continental United States with no numerical model used for training or initialization. StormScope produces coherent, physically plausible storm trajectories with well-calibrated uncertainty. Across more than 900 forecasts, it greatly exceeds the National Oceanic and Atmospheric Administration's state-of-the-art forthcoming operational numerical modeling system in both deterministic and probabilistic skill through the 0-2-h nowcasting window. StormScope's skill for strong radar echoes exceeds the operational state of the art by approximately 75% and 45% at lead times of 30 min and 2 h, a relative improvement several times larger than machine learning has delivered for global medium-range forecasting. Crucially, it also anticipates storms that develop after forecast initialization, establishing its utility beyond extrapolation of existing radar echoes. It likewise surpasses a leading AI nowcasting model for intense precipitation. Together, these results establish learning directly from observations as a route toward skillful, well-calibrated storm-scale forecasts that is unconstrained by the initialization and microphysics bottlenecks limiting numerical models and their emulators.

A non-Hermitian system at an exceptional point (EP), a specific critical point (CP) associated with the parity-time symmetric phase transition, exhibits a sublinear response to perturbation and promise unprecedented sensitivity beyond the linear-response Hermitian sensors, so far operating at the diabolic points (DP). Despite great advancements, its sensitivity enhancement is fundamentally limited by the divergent Petermann factor, intrinsically rooted in the non-Hermitian eigenvector degeneracy, and practically by the system complexity. Here, we report the CP-resulting square-root response to the refractive index change and enhanced sensitivity in a simple chiral Hermitian cavity without phase transitions. Because of the inherent eigenvector orthogonality, this CP-based Hermitian sensor exhibits an EP-like response and enhanced sensitivity, breaking the Petermann-factor limit of sensitivity in non-Hermitian counterparts. This work paves the way towards exploring the Hermitian CPs for ultrasensitive sensing outperforming both the EP- and DP-based sensors.

A suite of classifiers was developed to distinguish experimentally synthesized zeolites from computationally predicted zeolite-like structures. Using convolutional neural networks applied to 3D volumetric grids, these classifiers achieve accuracies more than an order of magnitude higher than previous approaches based on geometric filters or other machine learning methods. The best-performing model differentiates among hypothetical zeolites and those that can be synthesized as silicates, as aluminophosphates, or as both. This four-class classifier attains a false negative rate of 3.4% and a false positive rate of 0.4%, misidentifying only 1,207 of over 330,000 hypothetical structures–even though the hypothetical structures exhibit similar formation energies as real zeolites and chemically reasonable bond lengths and angles. We hypothesize that the ZeoNet representation captures essential structural features correlated with synthetic feasibility. In the absence of comprehensive physics-based criteria for synthesizability, the small subset of misclassified hypothetical structures likely represents promising candidates for future experimental synthesis.

We propose a structural framework for the geometry of financial order books in which liquidity, supply, and demand are treated as emergent observables rather than primitive economic variables. The market is modeled as an inflationary relational system without assumed metric, temporal, or price coordinates. Observable quantities arise only through projection, implemented here via spectral embeddings of the graph Laplacian. A one-dimensional projection induces a price-like coordinate, while the projected density defines liquidity profiles around the mid price. Under a minimal single-scale hypothesis – excluding intrinsic length scales beyond distance to the mid and finite visibility – we show that projected supply and demand are constrained to gamma-like functional forms. In discrete data, this prediction translates into integrated-gamma cumulative profiles. We test these results using high-frequency Level~II data for several U.S. equities and find robust agreement across assets and intraday windows. Explicit comparison with alternative cumulative models using information criteria demonstrates a systematic preference for the integrated-gamma geometry. A minimal simulation of inflationary relational dynamics reproduces the same structure without invoking agent behavior or price formation mechanisms. These results indicate that key regularities of order-book liquidity reflect geometric constraints induced by observation rather than detailed microstructural dynamics. Supplementary Material is available at the arXiv submission.

Understanding concentrated electrolytes requires a theory that spans local hydration and mesoscale interfacial assembly. We present an integrated workflow-SCOPE-that combines (i) enhanced sampling focused on a single Li+ ion, (ii) reweighting of biased trajectories to recover equilibrium microstate probabilities, and (iii) a chemical-potential correction that accounts for the limited reservoir of free water in finite simulation boxes. Applied to LiCl(aq) across 0.5-26 M and 283-313 K, this approach reveals a simple organizing principle: solvated ions dominate at low concentration; contact ion pairs emerge at intermediate strength; and aggregated Li-xCl clusters become most stable at the solubility limit. The resulting free-energy trends predict temperature-dependent solubility in close agreement with experiment and clarify the role of interfacial nucleation in precipitation. Beyond the simple LiCl(aq) salt considered here, SCOPE offers a transferable strategy for characterizing speciation and phase behavior in concentrated liquid systems where collective coordinates and rare events dominate.

Extreme events pose significant risks and are challenging to predict. Assessing climate hazards requires placing quantitative constraints on geophysical fields under observable but fluctuating conditions. We propose a framework for estimating uncertainties – a ReLU-bias loss quantile neural network (RBLQNN) – with two novel modifications to the loss function to enforce uniform quantile accuracy and reduce degenerate predicted probability distributions. We evaluate the RBLQNN against other probabilistic baselines on a suite of datasets: synthetic datasets, observed daily temperature maxima from 1,501 NOAA Global Surface Summary of the Day (GSOD) weather stations, and altimetry-observed precipitation from the Tropical Rainfall Measuring Mission (TRMM). On synthetic datasets, the RBLQNN accurately predicts conditional distributions where more restrictive methods like linear quantile regression (LQR) or mean-variance estimation (MVE) neural networks fail, mitigates shortcomings of some other quantile neural networks, and converges stably under a range of hyperparameters. When applied to daily temperature maxima, the RBLQNN reveals that temperature distributions are relatively well described by Gaussian statistics, though nonlinear dependencies on local sea level pressure and geopotential heights appear important. For precipitation statistics, the RBLQNN strongly outperforms both LQR and MVE baselines, demonstrating its capacity to capture highly nonlinear and non-Gaussian conditional distributions. The RBLQNN's performance across varied datasets demonstrates it is a flexible and general approach for constraining uncertainties in geophysical quantities with nonlinear or non-Gaussian conditional dependencies.

Various extensions to Riemann geometry have been proposed since the inception of general relativity (GR). The aim has been and continues to be to construct a quantum and dynamic spacetime that incorporates the well-known classical (static) spacetime. Apparently, this seems to enable the principles of GR and quantum mechanics (QM) to be reconciled into a coherent relativity and quantum theory. A canonical geometric quantization approach that presents kinematics of free-falling quantum particles within a tangent bundle, expands QM to incorporate relativistic gravitational fields, and generalizes the four-dimensional Riemann manifold into an eight-dimensional one likely discretizes, if not fully quantizes, the Finsler and Hamilton structures. The Finsler and Hamilton metrics can be directly derived from the Hessian matrix. As introduced in [Physics, 7 (2025) 52], the quantized four-dimensional metric tensor can be deduced by means of approximations including proper parameterization of coordinates and the equating line elements on all these manifolds including Riemann manifold. This research, on the contrary, goes beyond all these approximations and proposes the incorporation of a phase-space metric tensor into GR. The derivation of a quantized eight-dimensional metric tensor is not only presented, but also the implications of it and the corresponding relative spacetime are examined.

We provide a numerical method for computing solutions to a free boundary problem arising from the equilibrium state of a floating drop. This numerical method is based on a Newton's method for the underlying nonlinear boundary value problems, and at each iterative step a Chebyshev spectral collocation method is employed. The problems considered here are those that can be described by using generating curves, and include problems in $\mathbb{R}^2$ and $\mathbb{R}^3$. The resulting nine-dimensional space of physical parameters is explored, and examples are given that highlight the potential energy of centrally located drops, wall-bound drops, and asymmetrical configurations in $\mathbb{R}^2$. Non-uniqueness of solutions to the corresponding Euler-Lagrange equations is displayed, and also strong evidence of non-uniqueness of energy minimizers is given.

We study a one-dimensional system of cold plasma equations taking into account electron-ion collisions in both relativistic and nonrelativistic cases. It is known that for a constant collision coefficient $\nu$, the solution to the Cauchy problem for such a system can lose smoothness. However, if the dependence of $\nu$ on the electron density $N$ is more than linear, then the solution remains globally smooth for any initial data. However, the appearance of the dependence $\nu(N)$ leads to a change in the type of the system, it loses hyperbolicity, which leads to computational problems. In this paper, we propose a new implicit solution method in Euler variables that overcomes these difficulties. It can be used in both nonrelativistic and relativistic cases and is tested for the threshold case of a linear dependence $\nu(N)=\nu_1+\nu_0 N$, when smoothness can still be lost. The computational experiments carried out are in full agreement with the available theoretical results.

This paper presents a high-performance framework for three-dimensional gravity modeling and inversion implemented in Julia, addressing key computational challenges associated with gravity inversion, including large scale problem, ill-posedness, and non-uniqueness. The framework employs a data-space inversion formulation that reduces the dimensionality of the inverse problem, resulting in lower memory requirements and improved computational efficiency. Forward modeling and inversion operators are implemented using a backend-agnostic kernel abstraction, allowing the same computational code to run on multicore CPUs and GPU accelerators. Performance evaluations using NVIDIA CUDA GPUs show significant reductions in computational time relative to CPU execution, particularly for large-scale problems involving up to approximately 3.3 million rectangular prisms and 178,797 gravity observations. The inversion incorporates implicit model constraints through the data-space formulation and depth-weighted sensitivity, which reduces the effect of depth-dependent amplitude decay and promotes geologically coherent density models. Synthetic experiments demonstrate the capability of the framework to recover complex subsurface structures, including vertical and dipping dykes. Application to field gravity data further demonstrates the practical applicability of the proposed approach, with the recovered density distributions showing good agreement with independent geological constraints and previous interpretations. The results demonstrate that GPU-accelerated Julia provides an efficient and extensible platform for large-scale three-dimensional gravity modeling and inversion, enabling high-resolution geophysical investigations with reduced computational and memory requirements.

The significance of wettability between solid and liquid substances in different fields encourages scientists to develop accurate models to estimate the resultant apparent contact angles. Surface free energy (SFE), which is principally defined for ideal (flat) surfaces, is not applicable to predict the wettability of real (rough) surfaces. This paper introduces a new parameter, namely normalized surface free energy (NSFE) as a combination of SFE and roughness, to predict the contact angle of liquids on non-ideal low-energy surfaces. The remarkable consistency of the predicted and measured contact angles of liquids on some rough surfaces also confirm the validity of the approach.

This study presents a hybrid reduced-order modeling (ROM) framework for incompressible flows on collocated finite-volume grids, combining a discretize-then-project consistent-flux formulation for velocity and pressure with a non-intrusive neural-network closure for turbulent viscosity. The intrusive formulation preserves discrete mass conservation and pressure-velocity coupling, while a reduced pressure reference-cell constraint fixes pressure gauge ambiguity. We evaluate Multilayer Perceptron (MLP), Transformer, and Long Short-Term Memory (LSTM) closures. For a three-dimensional lid-driven cavity at $Re=100$, the LSTM-based ROM achieves relative errors of 0.7% in velocity and 4% in turbulent viscosity. At $Re=3200$, a mode-sensitivity study identifies $N=15$ POD modes as the best overall configuration, balancing accuracy, dimension, robustness, and cost. It yields a final relative velocity error of approximately 12.3% and an online wall-clock speedup of approximately $50\times$ over the full-order model; energy and enstrophy errors remain below 11% for all three architectures. This regime requires case-specific neural-network retraining and pressure reference-cell parameter retuning. In a time-extrapolation test trained on $t\in[0,3]$,s and rolled out to $t=6$,s, the ROM remains bounded, although velocity and pressure errors increase beyond the training window. The LSTM turbulent-viscosity closure remains robust, identifying long-horizon pressure accuracy as the main limitation. These results demonstrate the potential of consistent projection-based modeling combined with data-driven turbulence closure for efficient reduced-order simulation.

Atom interferometers are used in a variety of applications, from measuring gravity and gravity gradients in the field to performing tests of fundamental physics in the lab. One method of increasing interferometer sensitivity is to produce a larger momentum difference between interferometer arms through the use of large momentum transfer methods, such as Bragg diffraction. However, Bragg diffraction introduces systematic effects in the accumulated interferometer phase that are challenging to characterize. A Bragg atom interferometer is described by the one-dimensional time-dependent Schrödinger equation (1D-TDSE). In this paper we show that for the case of Bragg diffraction the 1D-TDSE partial differential equation can be separated into several systems of ordinary differential equations, allowing for the use of adaptive step size Runge-Kutta methods. We compare the convergence of this method to the split-step and Crank-Nicolson methods, and present a method for further computational speed-ups using a lookup table.

Emergent quasiparticles in solids often exhibit unique topological properties as a result of the complex interplay between charge, orbital, spin and lattice degrees of freedom. Among these quasiparticles, the polaron occupies a special place as the first known manifestation of the interaction between a fermion and a boson field. While polarons have been investigated for almost a century, whether these quasiparticles exhibit topological properties and why remain open questions. Here, we establish the universal symmetry principles governing the topology of polar textures in large polarons. Using a group-theoretic analysis, we identify four distinct classes of polar textures in time-reversal-invariant systems, and we show that they carry integer topological charges. We validate this classfication by performing state-of-the-art first-principles calculations of materials representative of each class. For these materials, we compute the fingerprints of polaron topology in Huang diffuse scattering, and propose ultrafast electron and X-ray scattering experiments to detect these quasiparticles.

The Polarized Atomic Hydrogen Gas Jet Target (HJET) has played a central role in the absolute calibration of proton beam polarization at RHIC and is foreseen as a key component of the hadron polarimetry program at the future Electron–Ion Collider (EIC). The substantially higher beam current, reduced bunch spacing, and shorter bunch length planned for EIC operation motivate a careful reassessment of possible beam-induced depolarization of the jet target. In this paper, the depolarization of ground-state hydrogen atoms caused by the time-dependent magnetic field of the circulating polarized proton beam is quantitatively evaluated. The hydrogen atom is treated as a four-level hyperfine system in a holding magnetic field, and transitions driven by harmonic components of the bunch-induced magnetic field are analyzed using time-dependent quantum-mechanical evolution along atomic trajectories. Numerical tracking of hydrogen atoms through the beam region is performed using nominal EIC beam parameters. It is shown that, for a holding field of $120 \mathrm{mT}$ (as used at RHIC), the resulting depolarization of the jet target at the EIC is negligibly small, $\lesssim 0.01\%$, and well below the level relevant for EIC polarization accuracy requirements. The stability of this result with respect to plausible variations of the EIC proton beam parameters is also evaluated. In addition, possible effects under alternative experimental conditions are also examined.

Heterogeneous nucleation from defects dominates the electric field required for polarization switching of ferroelectrics. Here, we consider the switching of a nominally non-switchable polar thin film of AlN due to the proximity effect arising from embedded ferroelectric nanoclusters of Al1-xScxN. Using a Landau-Ginzburg-Devonshire thermodynamic approach and finite element modeling, we study the influence of nanocluster shape on polarization switching and domain nucleation emerging in AlN. The ferroelectric nanocluster boundary is modeled as a thin layer transitioning from Al1-xScxN to AlN. We analyze the conditions under which polarization switching in the AlN film occurs at coercive fields significantly lower than its dielectric breakdown field. In the presence of spike-like Al1-xScxN nanoclusters, the proximity effect enables switching of the spontaneous polarization in AlN and significantly reduces the corresponding coercive field. The internal field, which is depolarizing inside the AlN (due to its larger spontaneous polarization) and polarizing within the ferroelectric Al1-xScxN nanoclusters (due to its smaller spontaneous polarization), lowers the potential barrier in the clusters and nucleates nanodomains at the Al1-xScxN-AlN interface, forming localized regions of reversed polarization. Proximity effect can thus provide a pathway towards "thawing" previously "frozen" ferroelectrics through engineered nucleation for memory, actuation and optical technologies.

This work presents a pedagogical and self-contained derivation of the first-order effective Hamiltonian for the two-mode Jaynes-Cummings model in the dispersive regime. A perturbative unitary transformation removes nonresonant atom-field terms, revealing dispersive frequency shifts leading to an atom-induced effective beam-splitter interaction between the field modes. The resulting Hamiltonian is diagonalized through a simple geometric rotation in the two-mode bosonic space, providing a transparent interpretation of the underlying dynamics. The exposition emphasized clarity and physical insight, making effective Hamiltonian methods accessible for teaching and learning in multimode light-matter interactions.