Papers

Deep learning surrogates have become powerful tools for simulating and forecasting complex dynamical systems, yet their utility remains limited by catastrophic error accumulation during long-term autoregressive rollouts. This behavior is partly tied to the nature of the underlying systems: chaotic spatiotemporal systems visit phase space unevenly, with dynamics dominated by recurrent, low-dimensional quiescent states and characterized by rare and dynamically complex regime transitions. Trained under a sample-wise uniform objective, standard neural surrogates allocate their finite capacity to the statistically more numerous low-dimensional quiescent states, systematically under-representing the transient regimes that trigger disproportionate, localized errors. Existing imbalanced-regression methods tackle this issue by reweighting samples according to target-space density. However, statistical target-space rarity does not coincide with the intrinsic dynamical rarity encoded in the recurrence geometry of the attractor. To address this, we introduce Dynamics-Aware Weighting (DAW), a data-centric objective reweighting framework. Using the local dimension $d$ from dynamical systems theory as an a priori measure of a state's dynamical complexity, DAW reshapes the loss landscape to allocate representational capacity toward the sparse, high-$d$ regimes where forecast errors are systematically large. On the chaotic KS equation, DAW consistently outperforms uniform training as well as weighting based on target-space rarity, and its randomly permuted ablation, reducing long-term autoregressive error relative to all baselines. Event-level analysis shows that DAW achieves this by suppressing the localized error amplifications incurred during sharp jumps in the local dimension $d$, which typically accompany complex physical processes such as wave-merging in the KS system.

Vibrational spectroscopy reports on molecular structure with chemical-bond specificity, but in biological systems the vibrational signals of a target chromophore are typically buried under contributions from the surrounding matrix. Resonance Raman (RR) spectroscopy addresses this problem by matching the excitation wavelength to an electronic transition of the chromophore of interest, which increases Raman cross sections by up to six orders of magnitude and restricts the enhanced modes to those coupled to the resonant electronic state. This chapter introduces the physical basis of resonance enhancement and shows how RR isolates chromophore-specific vibrational markers in complex biological systems. We then extend the same principle into the time domain with femtosecond stimulated resonance Raman spectroscopy (FSRRS). In FSRRS, the Raman pump is tuned across the visible range and can be placed in resonance with the transient absorption of a chosen excited-state species, which makes the Raman pump wavelength an additional experimental variable. Sampling this variable across the excited-state absorption manifold provides a selection criterion that separates coexisting transient species sharing a common vibrational window.

This study proposes a well-balanced formulation of weakly compressible smoothed particle hydrodynamics (WCSPH) for free-surface flows, which preserves hydrostatic equilibrium exactly at the discrete level–a property essential for reliable long-term simulations. Although well-balanced schemes are well established for mesh-based methods, the property remains largely unaddressed in WCSPH, where the particle approximation of the pressure gradient fails to balance the gravitational force exactly. The imbalance stems from two difficulties: the nonlinearity of the pressure-gradient-over-density term, and the approximation error of gradients evaluated by particle summation. The first is resolved by introducing an auxiliary potential variable that recasts the nonlinear term as the gradient of a single scalar, which reduces to a linear function of position under hydrostatic conditions. The second is resolved by a Riemann-based gradient approximation with kernel correction, which is first-order consistent and recovers linear fields exactly. These two ingredients ensure that the discrete potential gradient balances gravitational force exactly. Widely used techniques, including $\delta-$SPH, particle shifting and tensile instability control, are readily incorporated. The formulation is further extended to three dimensions and implemented on GPU with architecture-tailored optimizations. Hydrostatic tests with rectangular, triangular and Gaussian bottom topographies show that the proposed formulation attains the well-balanced property to machine precision, reducing the spurious velocity error of conventional SPH from $10^{-3}$ to the order of $10^{-13}$. More complex benchmarks confirm its robustness, accuracy and low pressure oscillation, with simulations of up to 17.53 million particles performed on a single consumer-grade GPU.

Core ionization is spatially localized, but the correlated valence response that screens the resulting hole need not be. Using the water dimer as a minimal hydrogen-bonded system, we ask whether acceptor-site O~1s ionization recruits valence channels on the neighboring donor molecule and how proton displacement redistributes that response. We introduce correlated shifted-start real-time $\Lambda$-coupled-cluster theory to connect the core-hole spectrum with orbital- and fragment-resolved screening pathways. At equilibrium, the satellite response contains donor-local and intermolecular charge-transfer-like contributions. Their intermolecular origin is supported by the strong suppression of both contributions when the monomers are separated. In a fixed-nuclei geometry scan, elongating the hydrogen-bonded donor O–H bond nearly doubles their combined share of the satellite response, while analysis of the first approximately 10~fs of the fixed-nuclei electronic response reveals stronger modulation involving a donor-localized valence orbital. More broadly, this work shows how localized core ionization can serve as a site-selective probe of electronic communication, with satellite structure revealing how nuclear geometry redirects many-electron screening through molecular environments.

We give in depth overview of charge-carrier mobility in p-type silicon double lateral-gate junctionless transistors (DLGJLT) through a mix of computer simulations and experimental results. The devices were built on low-doped silicon-on-insulator substrates using atomic force microscope nanolithography. Because the design uses air-gap side gates instead of a gate-oxide layer or junction, it allows electrical current to flow through a lightly doped silicon channel. This setup reduces impurity scattering and protects transport quality from interface damage. We used three-dimensional TCAD Sentaurus simulation tool to model the device's electrical behavior and confirm our experimental data for transconductance and drain conductance. We attempt to give a comprehensive view on the charge transition within the device.

Transformation method provides an efficient tool to control wave propagation inside the materials. Using the coordinate transformation approach, we study invisibility cloaks with sphere, cylinder and ellipsoid structures for electronic waves propagation. The underlying physics behind this investigation is the fact that the Schrödinger equation with position-dependent mass tensor and potentials has a covariant form that follows the coordinate transformation. Using this technique we obtain the exact spatial form of the mass tensor and potentials for a variety of cloaks with different shapes.

We investigate electron injection into direct laser acceleration (DLA) in relativistic laser-driven plasma channels using particle-in-cell simulations. We identify and characterize a wavefront-guided injection mechanism, in which electrons are continuously fed into the plasma channel through the density pile-up layer at the laser-pulse front. Phase-space analysis reveals a localized injectable region within the pile-up layer, indicating that only a selected subset of electrons satisfies the conditions required for the subsequent direct laser acceleration. This mechanism provides a physical interpretation for the high-charge capability of DLA by explaining how electrons are continuously supplied to the accelerating channel. Beyond this continuous supply process, the injection dynamics are further modulated by the periodic variation of the carrier phase at the laser-pulse front. The spatial locations of injected electrons are found to be closely associated with magnetic-island structures formed under laser-phase modulation, suggesting that the laser wavefront not only supplies electrons but also organizes their entry into the accelerating channel. These findings advance the physical understanding of energetic-electron generation in relativistic laser-driven subcritical-density plasma channels and are relevant to the development of compact DLA-based particle and radiation sources.

Mathematics has often been organized around an authorial subject: one person, or a small group, composing proofs through language, notation, and judgment. Large language models, proof assistants, formal libraries, and repositories now make another production unit technically credible: a human-machine assemblage. This article calls that unit a studio ecobiont and asks when it is epistemically legitimate. Its governance thesis is that human participation is substantive only when the system preserves traceable provenance, reconstructible human competence, capacity to challenge the result, effective authority to stop or withdraw it, and public responsibility. These conditions distinguish a governed studio from a degenerate studio whose human oversight is ceremonial. A comparison of Polymath, the Liquid Tensor Experiment, Danus, and the Jacobian counterexample episode shows that collaboration, formalization, technical orchestration, and epistemic governance are independent dimensions. The proposed understanding audit and contribution-authority trace are governance designs, not validated measures. No causal superiority over authorial practice is claimed.

The electromagnetic properties of a dilute unpolarized nonrelativistic neutron gas are described in the case when neutron-by-neutron scattering is negligible. The Gaussian (Maxwell-Boltzmann) and Fermi-Dirac one-particle density matrices for the neutron gas are considered, the typical space scale of variations of these density matrices being assumed to be much larger than the the typical space scale of variations of the electromagnetic field. The Green functions for the static effective Maxwell equations are obtained. The parameters of a nonequilibrium neutron gas are found where this gas possesses a ferromagnetic instability. The analog of Friedel oscillations is described. The properties of plasmon-polaritons in the neutron gas are revealed. It turns out that the dispersion law of longitudinal and transverse plasmon-polaritons has an infinite number of branches at a given momentum. The region of parameters where the plasmon-polaritons can be regarded as quasiparticles is found. The plasmon-polaritons on a Gaussian wave packet of a single electron are described. Their dispersion law proves to have an infinite number of branches at a given momentum. It is shown that there exist stable longitudinal plasmon-polaritons even on a single electron.

Quantifying the interactions between neighboring projects will be essential as carbon storage operations expand in scale. However, many existing basin-scale studies are deterministic or rely on hypothetical scenarios. In addition, simulations that do not fully account for neighboring projects may suffice for initial permitting. In this study, we develop a unified basin-scale model for the Broom Creek Formation in North Dakota. The model includes a total of five (existing and planned) projects, involving 18~injection wells with a maximum potential injection rate of 30~MTPA. The unified model, constructed by integrating publicly available geological and project data, contains $44\times10^6$ cells and requires over two days to run. To enable faster simulations, we present a multilevel treatment that combines four-level nested local grid refinement, constructed through the solution of a constraint-satisfaction problem, with optimized power averaging for permeability in coarse regions. After demonstrating the accuracy of the coarse model, we use it to assess uncertainty in key quantities of interest (QoIs), including total CO$_2$ injected and plume area after 50~years, across a wide range of geological realizations and model parameters. In addition, we develop a deep learning surrogate model, which is used in global sensitivity analyses to identify dominant contributors to QoI variance. Comparisons between the unified model and standalone project simulations are presented to quantify inter-project interference. This can be substantial in some cases, e.g., for one project, the median total CO$_2$ injected decreases from 271~MT (standalone) to 192~MT (unified model).

AI data center power demand is spurring rapid deployment of on- and near-site natural gas turbines. The resulting nitrogen oxide NO$_x$ pollution is a growing concern but has not previously been quantified with atmospheric observations. Here we demonstrate space-based detection and quantification of NO$_x$ emissions from the SpaceXAI Colossus 2 power plant in Southaven, Mississippi. Using observations from the geostationary TEMPO satellite instrument, we detect a strong increase in local mean NO$_2$ column concentrations after the plant began operations in late 2025. We then use TEMPO to estimate two-week-average NO$_x$ source rates from August 2025 to August 2026, calibrating against continuous emission monitoring system (CEMS) data from US power plants. TEMPO first detected NO$_x$ emissions in December 2025 at 530$\pm$220 kg h$^{-1}$. We find that emissions increased through August 2026, averaging 870$\pm$165 kg h$^{-1}$ after February 2026, roughly 19$\pm$4 times higher than expected from the facility's March 2026 permit for 41 turbines operating under best available control technology (BACT) requirements ($\sim$47 kg h$^{-1}$). Emissions at the expected level would be undetectable by our TEMPO analysis.

Accurate modeling of the daily evolution of sea ice concentration (SIC) is central to improving the credibility and operational forecasting capability of deep learning-based sea ice prediction. However, existing deep learning methods often couple the underlying sea ice evolution relationships and data errors within high-dimensional nonlinear mappings, making it difficult to construct a stable and verifiable evolution core and apply it reliably to practical forecasting. To address this issue, this study proposes a reanalysis-forecast dual-domain decoupled framework for learning sea ice evolution operators. The framework builds upon a lightweight physical baseline to generate daily evolution trajectories, employs a temporally constrained joint multi-lead compensation network to com?pensate for unresolved processes, and introduces an ice-mass?aware transport mechanism to suppress numerical dissipation. In the forecasting stage, the parameters of the base evolution core are fixed, while a lightweight variable-semantic adaptation mechanism calibrates inter-domain distributions and evolution responses, thereby separating forecast-domain errors from base evolution errors. Experiments show that the constructed base evolution core can accurately and stably simulate daily sea ice evolution at both short-term and annual scales under reanal?ysis forcing, and can be effectively transferred to the forecast domain through lightweight adaptation, achieving stable prac?tical forecasting capability while preserving the base evolution structure. The source code will be made publicly available at https://github.com/zhangmaqun65535/SNM upon acceptance of this manuscript.

Our recently published papers reporting results of Direct Numerical Simulation (DNS) of forced convection flows in porous media suggest that in a porous medium the size of turbulent structures is restricted by the pore scale. Since the turbulent kinetic energy is predominantly contained within large eddies, this suggests that turbulent flow in a porous medium may carry less energy that its counterpart in a clear fluid domain. We use this insight to develop a practical model of turbulent flow in composite porous/fluid domains. In such domains, most of the flow is expected to occur in the clear fluid region; therefore, in most cases the flow in the porous region either remains laminar or starts its transition to turbulence even if the flow in the clear fluid region is fully turbulent. This conclusion is confirmed by comparing appropriate Reynolds numbers with their critical values. Therefore, for most cases, using the Forchheimer term in the momentum equation and the thermal dispersion term in the energy equation may result in a sufficiently good model for the porous region. However, what may really affect turbulent convection in composite domains is the roughness of the porous/fluid interface. If particles or fibers that constitute the porous medium (and the pores) are relatively large, the impact of the roughness on convection heat transfer in composite porous/fluid domains may be much more significant than the impact of possible turbulence in the porous region. We use the above considerations to develop a practical model of turbulent flow in a composite porous/fluid domain, concentrating on the effect of interface roughness on turbulence.

We present a multi-parameter simulation-based inference framework for joint Bayesian recovery of subsurface velocity and acoustic impedance from seismic data. A score-based diffusion model is conditioned on two complementary Common Image Gathers (CIGs): an inverse-scattering CIG encoding reflectivity amplitude and an anti-ISIC CIG encoding kinematic velocity errors. The model simultaneously samples the posterior distributions of both parameters. Training labels are deliberately decoupled to prevent the model from exploiting the Gardner relationship: velocity targets are lightly smoothed to match the long-wavelength content of the anti-ISIC CIG, while impedance targets retain the unsmoothed ground truth. On the Compass benchmark the model achieves velocity SSIM of 0.967 (RMSE 0.050 km/s) and impedance SSIM 0.867 (RMSE 0.279 km/s g/cm^3), with velocity quality confirmed by CIG focusing.

The USS Stargazer, commanded by Captain Jean-Luc Picard, battles a Ferengi vessel. In an effort to survive the battle, the Stargazer does the unthinkable: they move toward the Ferengi vessel at a faster-than-light speed, stopping just before a crash. The Ferengi vessel is said to see two images of the Stargazer, and ends up shooting the wrong one. In this note, I show with spacetime diagrams that, in fact, the Ferengi vessel sees up to three images of the Stargazer. This gives a simple example of how there can be multiple values of retarded time when an object moves faster than light, and how these multiple values are not restricted to only two when the object can accelerate.

Ion channels and pumps drive ion-selective currents through cell membranes at localized sites, yet a cell's electrical state is routinely summarized by a single transmembrane voltage. Combining theory and numerical simulations, we resolve the spatiotemporal dynamics of charge reorganization driven by a localized current on a spherical membrane vesicle. At early times, the response is insensitive to membrane geometry: as in the case of a flat membrane (arXiv:2407.11947; arXiv:2508.14001), the transmembrane voltage decays in a monopolar fashion, varying inversely with distance from the source, and crosses over to a dipolar tail that scales as the inverse cube of distance. Under sustained current, this monopolar response spreads outward from the source. Because the vesicle is closed, this response cannot persist indefinitely; once the monopolar front traverses the entire vesicle, the subsequent charging dynamics is dominated by a spatially uniform mode corresponding to capacitive charging of the membrane. We further decompose the bulk potentials into an electrostatic image-charge component that generates the bulk electric fields and a spatially uniform capacitive mode that can be represented as an equivalent circuit. We also derive a nonlocal cable equation governing the transmembrane voltage dynamics and show that the uniform mode is its long-time solution. This work provides a first-principles basis for the electrophysiological simplification of an electrotonically compact cell.

A thin volatile film carrying N particle species of different sizes evaporates on a deep immiscible liquid subphase. The spreading coefficient is positive, so nothing pins. The film ends at a receding front whose motion falls out of the film equations; no contact-line law is imposed. We solve the lubrication problem with a conservative depth-integrated treatment of species transport, and the front turns out to be a chromatograph. Every species piles into a concentration spike at the front, each held there in proportion to its Péclet number. The smaller, more diffusive species leaks continuously into the fluid that survives the front's passage; the larger species is laid down along the sweep path. The dried deposit is the time-integrated record of that sweep, and it is sorted by size: small-rich centre, large-rich mid-annulus. Pinned bidisperse droplets sort the other way. Depinned droplets on solids sort this way, but by a force balance; here, diffusivity contrast alone picks the direction. Size enters the transport problem only through the Péclet number, so diffusivity contrast is the only symmetry-breaking available to choose a direction: Marangoni stresses, colloidal interactions and wetting effects act on the magnitude and, by the symmetry of the transport operator, cannot set the direction. Adjacent-species band separations scale with the difference in inverse effective Péclet number, a law derived and measured here: the resolution of the chromatograph. That scaling points to a route to size fractionation of polydisperse nanoparticles in the few-nanometre regime, exactly where standard methods struggle.

Identifying atomic defects at elevated temperature is difficult because thermal fluctuations blur the local symmetry that both geometric heuristics and supervised classifiers rely on: trustworthy labels exist in low-temperature reference configurations, while the high-temperature regime where robust analysis matters most is effectively unlabeled. We cast this as a cross-temperature domain-shift problem and align the two domains at three levels: an equivariant denoiser at the input level, cross-temperature contrastive learning at the representation level, and a morphology-aware regularizer that steers predictions toward the compact geometry of physical defect structures. Because no atom-wise truth exists at temperature, we further introduce a label-free evaluation suite that scores predicted defect structures along five spatial and physics-based axes, enabling model assessment and selection without high-temperature labels. Near the melting point, the framework identifies vacancies and self-interstitial atoms across face-centered-cubic, body-centered-cubic, and hexagonal-close-packed iron systems with every interstitial localized and zero false detections in every vacancy system against Wigner-Seitz ground truth, with no high-temperature labels used in training. It sustains this fidelity on a million-atom, 2.5 ns trajectory, resolving single vacancy hops and complete Frenkel-pair recombination, and captures grain-boundary phase transformations in aluminum bicrystals, distinguishing two nucleation modes. Multi-level domain alignment thus offers a practical, label-efficient route to temperature-robust structural analysis of large-scale molecular dynamics.

Three-dimensional woodpile photonic crystals constitute one of the most successful architectures for realizing photonic band gaps, yet their optical response is traditionally fixed by the geometry established during fabrication. Here, we introduce a twist-controlled woodpile photonic crystal in which the relative angular orientation between successive rod layers acts as an additional, in situ-tunable geometrical degree of freedom. Using an extension of rigorous coupled-wave analysis adapted to multilayer structures with rotated reciprocal lattices, we systematically investigate the evolution of the transmission spectrum as a function of twist angle. We show that twisting drives the structure through three distinct photonic regimes. In the fully aligned configuration, broad frequency intervals exhibit near-unity transmission. At intermediate twist angles, the spectrum becomes populated by strongly dispersive resonances displaying characteristic Fano line shapes, high quality-factor and pronounced angular sensitivity. As the twist angle approaches 90{\deg}, the conventional woodpile structure is recovered, and these resonances evolve into a broad photonic stop band characteristic of three-dimensional photonic crystals. A simplified analytical model based on reciprocal-lattice considerations accurately reproduces the principal resonance modification observed in the numerical calculations. Our results demonstrate a continuous twist-induced transition from broadband transmission to photonic stop bands through an intermediate Fano-resonant regime, establishing twisted woodpiles as a versatile platform for three-dimensional twist-engineered photonics.

Kalman filtering (KF) recursively infers plasma quantities, represented by a state, from noisy diagnostics while propagating uncertainty in the inferred state separately from diagnostic noise. For linear dynamical and measurement models with Gaussian probability density functions (PDFs), the state mean and covariance provide the KF description. We modify this KF for open plasmas with particle and energy sources by extending the physical state from Maxwellian variables to retained NMDF coordinates, whose evolution follows a projection of the nonlinear Landau-Fokker-Planck equation. The same NMDF state is propagated through a fixed diagnostic response to obtain the corresponding measurement PDF. Because a non-Gaussian likelihood can drive the posterior outside the Gaussian family, the mean-covariance representation is extended to a finite moment closure whose coordinates are related to the retained moments through their Jacobian. The recursive prediction-correction structure is preserved, recovering conventional KF results for linear models with Gaussian PDFs. As a proof of concept, we use seven published non-Gaussian Alcator C-Mod Langmuir-probe current PDFs. For each target PDF, NMDF response parameters are calibrated from the other probe PDFs and frozen; this excluded target tests independent prediction. This test shows that different plasma regions require different NMDF structures, instead of the standard assumption of Maxwellian velocity distributions across all regimes and locations. Because the published measurement PDFs do not retain time ordering, this proof of concept does not yet test recursive uncertainty dynamics. Implementing the projected Landau-Fokker-Planck prediction with time-resolved diagnostics is the next step toward full validation of the modified KF.

Standard epidemiological models coupled with evolutionary game theory (e.g., vaccination games) typically rely on the free-rider dilemma inherent to infectious diseases. In this paper, we present a coupled behavior-disease model where the underlying disease is non-infectious, and transmission occurs strictly at the cultural level. We model the competition between two medical paradigms, namely evidence-based medicine ($M$) and alternative medicine ($A$), using replicator dynamics driven by treatment-specific social-amplification coefficients. We show analytically that the nonlinearity of the coupled system arises from social feedback rather than from the underlying biological dynamics. Crucially, the stability analysis reveals that the system lacks isolated fixed points; instead, it converges to a one-dimensional continuous manifold of non-hyperbolic neutral equilibria, generating persistent path dependence in the cultural composition. Furthermore, we derive an explicit constant of motion that constrains each trajectory to a distinct invariant level set. Restricting this invariant to the equilibrium manifold yields a strictly monotonic selection function, uniquely determining the equilibrium selected from any given initial state. Our findings show that derivative-based cultural feedback is sufficient to generate a continuum of coexisting health paradigms, while asymmetric social amplification modifies this selection. Because transient interventions that directly alter paradigm adoption break this invariant, they shift the system to a different level set, producing persistent changes in the long-run cultural composition. The model thus reveals how societies retain a deterministic memory of prior health and treatment states, demonstrating that both misinformation shocks and targeted public health interventions can leave lasting structural changes long after the perturbation has ended.

Coherent radio emission is observed from compact astrophysical objects with relatively weak magnetic fields, including millisecond pulsars and white dwarfs, the latter being proposed as possible sources of long-period radio transients. In such environments, the standard pair discharge mechanism—driven by curvature radiation and one-photon pair production—can fail because the low magnetic field strength suppresses photon conversion. We analyze a discharge mechanism that operates efficiently in weak-field magnetospheres, including two-vertex quantum electrodynamic processes: inverse Compton up-scattering of background photons followed by either two-photon or one-photon pair creation depending on the seed photon temperature. Using first-principles radiative particle-in-cell simulations incorporating exact QED cross sections, we demonstrate that these mechanisms robustly generate pair cascades in millisecond-pulsar magnetospheres and in hot white-dwarf environments. The resulting system exhibits limit-cycle behavior and generates electromagnetic field fluctuations capable of producing coherent radio emission, providing a natural explanation for radio activity in low-field compact objects.

We derive an exact and novel expression for an averaged two-point correlation function in the statistically stationary, forced-dissipative two-dimensional Navier-Stokes equations subject to the Coriolis force under the beta-plane approximation. This identity is related to the so-called geostrophic balance: it connects the effect of the Coriolis force to the pressure gradient through a two-point correlation function. Additionally, we provide sufficient conditions under which the asymptotics of the averaged third-order structure function at large spatial scales follow the universal third-order law of two-dimensional turbulence in the absence of the Coriolis force. This complements our previous results on small spatial scales. Together, our results provide a clear picture of the role of the Coriolis force in beta-plane turbulence. On the one hand, the spherically averaged rates of enstrophy and energy transfer are not affected by the Coriolis force. On the other hand, the Coriolis force contributes to anisotropic large-scale organization by altering the spatial distribution of energy and promoting the formation of zonal structures. The proof relies on a new formulation of the Karman-Howarth-Monin relation. For the geostrophic balance, we use a novel antisymmetric projection of the KHM relation under which only the pressure and Coriolis terms survive. For the cascade laws, we show that the Coriolis contribution to the averaged classical KHM relation vanishes identically at any scale.

Regional climate risk assessment is critical for applications such as infrastructure design, disaster forecasting, and insurance resource allocation. However, estimating regional (i.e., high-spatial-resolution) risk with global climate models (GCMs) remains computationally prohibitive, which has driven the development of downscaling methods for coarse GCM outputs. Downscaling is vital for rare events, since quantifying their extreme properties requires high spatial resolution and very long GCM simulations. These methods non-intrusively increase GCM resolution while correcting statistical biases from unresolved fine-scale processes, thereby improving the accuracy of extreme event statistics with long return periods. A key challenge is preserving dynamical consistency, as freely evolving GCM trajectories are not expected to track the observational dataset used for training the correction operator. This is critical for causal extreme event analyses, where storyline-based risk assessment, i.e., extreme event catalogs, is necessary for effective planning. We address this challenge by introducing Dynamically and Statistically Consistent downscaling (DySCo), a non-intrusive framework yielding high-resolution climate projections consistent with coarse GCM dynamics. DySCo relies on a data-driven reformulation of nudging to create dynamically paired training trajectories without intrusive GCM modifications. Using these paired trajectories, we train a dynamically and statistically consistent, two-stage operator. We evaluate the method by downscaling the Community Earth System Model v2 Large Ensemble (LENS2) in time and space towards historical reanalysis. Results show DySCo achieves superior dynamical consistency with the coarse GCM trajectories, essentially applying a minimal, causal correction to the GCM, preserving top statistical performance comparable to state-of-the-art unsupervised models.

We consider turbulent flow in a half-space along an infinite plane. We regard it as the limit to which the motion in the viscous sublayer of turbulent near-wall flows tends as Reynolds number increases indefinitely. In addition to the no-slip condition, an averaged shear stress value is specified on the streamlined surface. A numerical solution algorithm is proposed, based on the well-known spectral method, which demonstrates that the boundary conditions on the wall along with the physically justified constraint that the velocity components do not grow exponentially at infinity uniquely determine the turbulent flow under consideration. The implementation of the algorithm will allow us to draw conclusions about the validity of the idea that the flow in the viscous and logarithmic sublayers is independent of the external parameters of turbulent near-wall flows.

Radio pulsars can power their coherent radio emission through intermittent plasma discharges in magnetospheric gaps. The nonlinear coupling between particle acceleration, quantum electrodynamic (QED) processes, and electric field screening remains difficult to model self-consistently. We present an analytical concurrency model and first-principles particle-in-cell (PIC) simulations of polar cap discharges. The model predicts limit-cycle behavior where the electric field and plasma density oscillate with a frequency set by the local plasma frequency, modified by the plasma inertia. One-dimensional simulations with exact QED rates and realistic plasma parameters validate these predictions. The discharges generate pair multiplicities up to $M_\pm \sim 10^4$ and excite electric field oscillations with spectra consistent with pulsar radio microstructure. These results provide a theoretical framework connecting microphysical gap dynamics to macroscopic observables.

In low-field, lightweight, pole-pieceless permanent-magnet MRI systems built with sintered Nd-Fe-B or Sm-Co magnets, the rapid switching of gradient fields readily induces eddy currents in the sintered magnets, leading to image artifacts. To address this, we report for the first time a Sm-Fe-N permanent-magnet MRI system based on anisotropic Sm-Fe-N bonded magnets, whose high electrical resistivity reduces the eddy currents in the X, Y and Z directions to 0.093%, 0.172% and 2.38%, respectively, while a magnetic field inhomogeneity below 150 ppm is achieved at the boundary of a 220 mm diameter of spherical volume (DSV). Compared with sintered Nd-Fe-B and Sm-Co magnets, using Sm-Fe-N bonded magnets as the source of the static magnetic field not only suppresses eddy currents but also makes a closely tiled, densely packed magnetic-circuit layout feasible, providing a more uniform static magnetic field for the MRI system. Imaging results free of obvious geometric distortion and banding artifacts further indicate that the Sm-Fe-N magnet system delivers low eddy currents and high static magnetic field homogeneity.

We present a unified calculation of bound and resonant states in purely leptonic Coulomb systems: $e^-e^-e^+$ ($\mathrm{Ps}^-$), $\mu^+e^-e^-$ ($\mathrm{Mu}^-$), $\mu^+\mu^+e^-$ ($\mathrm{Mu}_2^+$), $e^+e^+e^-e^-$ ($\mathrm{Ps}_2$), and $\mu^+\mu^+e^-e^-$ ($\mathrm{Mu}_2$). Using an extended stochastic variational method combined with the complex scaling method, we resolve the natural-parity $S$- and $P$-wave spectra. Near their respective $n=2$ thresholds, all three trilepton systems exhibit Gailitis–Damburg sequences generated by $2S$ and $2P$ Stark mixing and the resulting inverse-square attraction. Although microscopically distinct from the Efimov effect, this mechanism produces the same inverse-square asymptotics and geometric scaling. The $\mathrm{Ps}^-$ and $\mathrm{Mu}^-$ systems exhibit resonance sequences of comparable density, whereas the $\mathrm{Mu}_2^+$ produces a much denser spectrum, with 20 resolved members in the $^3P^o$ channel. In $\mathrm{Mu}_2^+$, the deeper states follow molecular Born–Oppenheimer configurations, while the near-threshold spectrum is governed by the atomic $\mathrm{Mu}(2)+\mu^+$ structure. In $\mathrm{Ps}_2$, coupling between threshold-degenerate configurations is essential for a near-threshold bound state. In $\mathrm{Mu}_2$, the Born–Oppenheimer organization of the resonance spectrum is channel dependent.

The electronic circular dichroism (ECD) spectrum is a primary experimental probe for assigning the absolute configuration of chiral molecules, yet interpreting a measured spectrum requires time-dependent density functional theory (TDDFT) calculations that can cost hours per molecule and must be repeated for every candidate stereoisomer and conformation. We present PhysECD, a physics-constrained, parity-aware E(3)-equivariant framework that bypasses computationally expensive TDDFT and predicts ECD spectra directly from the 3D structure of an individual conformer. Instead of regressing the spectrum as an opaque sequence, PhysECD predicts the physical quantities that generate it: per-state excitation energies and electric and magnetic transition dipoles. These quantities determine the rotatory strength R – the dot product of the two dipoles, a pseudoscalar that reverses sign under mirror reflection – and yield the final spectrum through a differentiable Gaussian-broadening formula derived from the underlying physics. The parity structure of the equivariant features guarantees the correct chiroptical symmetry: reflecting a molecule exactly negates the predicted spectrum. On the CMCDS dataset, PhysECD attains a per-molecule spectral Pearson correlation of 0.642 (mean) / 0.822 (median), substantially exceeding prior learned predictors while remaining physically interpretable. Experiments across multiple backbones further show that the framework is backbone-agnostic, paving the way for real-time assignment of absolute configuration.

Optical computing is emerging as a promising paradigm for next-generation information processing. Diffractive optical processors rely on spatially distributed trainable degrees of freedom, leading to extended architectures. Here, we propose a compact neuromorphic optical-computing approach where the entire trainable transformation is implemented by a single Mie scatterer. By formulating computation in vector spherical harmonics basis, trainable modal couplings can be concentrated within a finite object through its T-matrix. Since available T-matrix parameters scale as the fourth power of maximal multipole order, this architecture can overcome trainable-parameter-density limitations of conventional spatially distributed diffractive processors. We demonstrate classification of phase-encoded MNIST images using scattered-field intensity. At a particle size parameter of $ka = 15$, the trained T-matrix reaches approximately 90% test accuracy, comparable to a single-layer artificial neural network. Similar performance can be achieved using near-fields, enabling on-chip integration. We show how reciprocity, passivity, and particle symmetry constrain performance: passivity reduces the accessible operator space while improving robustness, whereas symmetry reduces the number of independent parameters. Finally, we inverse-design a non-absorbing dielectric scatterer that realizes the classification task with 84% accuracy. These results demonstrate that nontrivial neuromorphic transformations can be encoded within the multipolar response of a single compact scatterer.