Papers

Inferring thermal fields and thermophysical properties from limited measurements is a fundamental challenge in micro- and nanoscale heat conduction, where the classical Fourier law breaks down and the phonon Boltzmann transport equation (BTE) is needed to capture non-diffusive transport effects. In this work, we extend Monte Carlo physics-informed neural networks (MC-PINNs), originally developed for forward phonon BTE problems [J. Comput. Phys. 542, 114364, 2025], to inverse multiscale heat conduction problems. Two representative classes of inverse problems are considered: (i) reconstructing the full thermal field from sparse interior temperature measurements when boundary conditions are unknown, and (ii) simultaneously inferring the unknown relaxation time together with the thermal field. Problem-specific MC-PINN architectures and training strategies are designed for each class. The mesh-free Monte Carlo sampling strategy enables a unified treatment across diffusive, transitional, and ballistic transport regimes without requiring a priori knowledge of the relaxation time. The proposed method is evaluated on quasi-one-dimensional, quasi-two-dimensional, and three-dimensional benchmark problems covering a wide range of Knudsen numbers, as well as on a realistic 3D fin field-effect transistor (FinFET) structure. Results demonstrate that MC-PINNs consistently outperform purely data-driven deep neural networks, particularly in the sparse-data regime, and can accurately infer spatially uniform relaxation times. For spatially varying relaxation times, the inferred distributions capture the dominant thermal response, and numerical simulations using the recovered parameters reproduce the macroscopic fields with good accuracy. These findings establish MC-PINNs as an effective and physically consistent framework for inverse thermal analysis at micro- and nanoscales.

The T-shaped mixer is widely used in fundamental studies of chemical engineering. Its transitional regime is well understood, whereas the turbulent dynamics has received scarce attention so far. Here we perform direct numerical simulations of the turbulent regime for Reynolds numbers up to $Re=2000$ at Schmidt number $Sc=1$. Our analysis reveals two distinct stages along the mixing channel prior to relaxation toward duct flow. Near the junction, a jet-like flow forms and exhibits the approximately self-similar behaviour of transitional planar jets. Subsequently, a decay region characterised by power-law decay of turbulent kinetic energy, dissipation and scalar variance emerges. For the velocity field, the observed exponents are consistent with those of decaying turbulence in bounded domains, whereas the scalar-variance exponent is consistent with that of unbounded turbulence. We argue that this apparent discrepancy is a consequence of the mixing process progressing from the center of the channel toward the side walls in the decay region, while turbulence already fills the channel cross-section entirely at the end of the jet region.The time-averaged mixing state presents error-function profiles of the scalar in the transverse direction, similar to the laminar cases, and is quantified here through a stream-wise evolving effective diffusion coefficient.

Optical rotatory dispersion (ORD) in chiral media, classically demonstrated as the "sweet monochromator," provides a robust mechanism for liquid-tunable spectral filtering. However, the ultimate physical boundaries governing its spectral bandwidth remain fundamentally unexplored beyond classical electromagnetic theory. Here, we present a comprehensive framework bridging macroscopic optical filtering with the quantum electrodynamic (QED) limits of chiral light-matter interaction. Using time-dependent density functional theory (TD-DFT) combined with Boltzmann conformational averaging, we accurately compute the ORD curves of representative saccharide systems, revealing that the theoretical minimum bandwidth is intrinsically tied to the anomalous dispersion (Cotton effect) near the molecular absorption band. While our macroscopic experiments demonstrate a classical bandwidth limit of ~ 20 nm due to heterogeneous broadening and instrumental constraints, our first-principles calculations predict achievable sub-nanometer bandwidths utilizing visible-absorbing chiral molecules. Furthermore, we extrapolate this framework to the strict quantum limit, proposing a single-photon QED architecture operating at ultra-low temperatures (~ 10 mK). In this regime, the spectral purity is constrained solely by the natural lifetime of the molecular excited state, governed by the Heisenberg uncertainty principle. This work establishes the ultimate theoretical criteria for chiral optical filters and pioneers the concept of the "quantum monochromator" for ultrasensitive chiral spectroscopy and quantum information processing.

Many laws of chemistry are exact within a limited scope and acquire a separate caveat outside it, and the caveats are usually treated as unrelated. This work argues that they share one cause. Each caveat marks a point where a question is asked of a description too coarse to answer it: the question belongs to a richer level of structure than the description carries. To make these levels explicit, the paper builds a tower of categories over the free symmetric monoidal category of a Petri net, the simplest categorical presentation of a reaction network. The levels, from the bottom up, are stoichiometry, thermochemistry, equilibrium, reaction kinetics, reaction mechanism, molecular geometry, and electronic structure. Each adds one kind of chemical content over the level below, and a forgetful functor runs back down. One question runs through the tower: what can a level express that the level below cannot? Answering it places each measurable quantity at the level where it lives, and identifies the content the levels below could record but not account for. The method is then turned on with two pieces of known chemistry. It recasts a classical criterion for when a reaction network has a unique stable equilibrium, the deficiency-zero theorem, as the rigidity of a single forgetful fibre. It also follows one familiar reaction up the tower: the ring opening that the Woodward-Hoffmann rules govern. At each level, from stoichiometry to electronic structure, the reaction becomes a distinct categorical object.

Multi-mode interferometers for bosons in Gaussian states are important systems for quantum metrology with precision beyond the standard quantum limit and for bosonic quantum computing. However, there is a lack of theoretical foundation for generic $N$-mode Gaussian interferometry. In this work, we study quantum metrology and quantum control in multi-mode bosonic systems with quadratic Hamiltonians, exploiting the fundamental Sp$(2N,R)$ symmetry of such interferometers. We show that the optimal quantum control to maximize sensitivity requires aligning squeezing and displacement in the same direction. We propose Sp$(2N,R)$ echo, a multi-mode generalization of the SU$(1,1)$ interferometry, to achieve the sensitivity of phase estimation set by the quantum Fisher information. In addition, we introduce a geometrical means for reversing many-body dynamics with Sp$(2N,R)$ dynamical symmetry, such as dynamics of the bosonic Kitaev chain. Our schemes are readily realizable in optical, atomic, and mechanical platforms.

This paper reviews the technical issues underlying space-based boost-phase missile defense and examines the current technology available for space-based interceptors and the characteristics of the missiles such a system would face. It then analyzes a particular space-based missile defense system that has been proposed to intercept in boost, ascent, and midcourse phases to illustrate the details of such an analysis and the constraints imposed on such systems by the physics of operating in space.

Gradient-based inverse lithography technology~(ILT) for extreme ultraviolet~(EUV) masks is presented. A novel framework treats the differentiable waveguide method and the recently proposed waveguide neural operator~(WGNO) as end-to-end physics engines, recovering the permittivity of the absorber of the mask through automatic differentiation of the full forward diffraction model. Numerical experiments on realistic 2D and 3D absorbers of the mask (TaBN, La, U) at $\lambda{=}11.2$~nm show that the considered ILT methods make it possible to obtain a mask structure that achieves the desired field on the wafer.

Wall traction in a rarefied gas is a half-range functional of molecules arriving at and leaving a surface, whereas hybrid solvers, moment methods, and learned wall models often transmit only finite full-range moments. We determine which directional information is lost and what restores the wall functional using direct simulation Monte Carlo (DSMC) of Mach-6 flow over three triangular-protrusion orientations. Output-specialized neural networks interpolate a 57-condition DSMC database. A distinct ExtraTrees regression holds learner capacity, auxiliary features, train/test split, and seeds fixed while comparing the primitive state $S_0$, the momentum-flux-augmented state $S_1$, and the heat-flux-augmented state $S_2$. Adding the momentum-flux tensor $P_{ij}$ reduces aggregate pressure error by 31% but does not make signed shear transferable. An analytic null-space construction supplies the mechanism: strictly positive distributions can share all full-range moments through degree three while producing different pressure and opposite signed shear because the wall kernel selects the incident half space. A finite-distance off-wall ExtraTrees state $S_{\mathrm{off}}$ shows that partial directionality is useful but representation-dependent. Finally, a parameter-free kinetic reconstruction combines the incident half-range wall-arrival state $S_{\mathrm{HR}}$ with the prescribed diffuse kernel and agrees with both a same-window DSMC tally sharing the incident events and a separate 40,000-step DSMC wall-tally window. The resulting design principle is direct: a rarefied wall closure must preserve the incident normal–tangential correlations required by the target load rather than merely adding higher full-range moments.

Traditionally, the critical blast wave parameters used to characterize loading conditions are obtained through pressure gauge measurements. However, these instruments are costly, require careful calibration, provide discrete location measurements only, and must be deployed in hazardous environments. Recent events, such as the Beirut port explosion have demonstrated that video recordings, which provide time of arrival (ta) versus distance data, offers valuable information for post-event blast analysis. However, methodologies capable of predicting key blast parameters, such as positive phase duration and impulse, using video data alone remain limited. This work proposes and validates a novel methodology to predict positive phase duration and impulse for spherical, non-cased, free air bursts of ideal explosives using ta data only. The proposed methodology was evaluated using experimental datasets from the literature for bulk and cartridge PE4, PE7, Composition B, and PETN. The positive phase duration and impulse models achieved, respectively, mean absolute percentage errors of 5.3% and 5.3%, maximum deviations of 20% and 9.4%, absolute biases of zero and 3.1%, and confidence interval coverages of 86% and 83%. The predicted results achieve remarkable comparison to all reported experimental data, verifying the ability to capture positive phase blast loading for high speed video; a step-change in explosive characterisation through full spatial and temporal primary shock characteristics.

Volumetric particle tracking velocimetry (PTV) produces scattered Lagrangian tracks that must be reconstructed on an Eulerian grid before velocity gradients, pressure, or hydrodynamic loads can be evaluated. This step is usually performed on a domain treated as entirely fluid. When a solid body lies within the measurement volume, its surface kinematics are not imposed and the reconstruction is weakest in the steep-gradient region next to the body. We introduce LE-DM (Lagrangian-to-Eulerian reconstruction with Dynamic Masking), a constrained reconstruction framework for moving solid boundaries. A time-dependent signed-distance function classifies grid nodes as open fluid, boundary shell, or solid interior. The particle data, incompressibility constraint, prescribed surface velocity, and regularization terms are then assembled on the masked domain within a single solve. The method requires only a signed-distance field and a surface velocity, allowing stationary walls, translating, rotating, multiple, and deforming bodies to be represented in the same formulation. LE-DM is assessed using an analytical oscillating sphere, synthetic tracks from a CFD rising-sphere simulation, and a refractive-index-matched tomographic-PTV experiment on a freely rising sphere. The surface kinematics are enforced to solver tolerance, while the bulk reconstruction remains unchanged where no body is present. In the analytical case, the first-cell error is reduced from 14% to 3% of the body speed. In the experiment, LE-DM recovers the independently measured surface velocity, whereas an all-fluid reconstruction does not. The result is a divergence-free, boundary-consistent velocity field for pressure and force estimation.

The height of viral particles adsorbed on solid substrates is governed by the equilibrium between adhesion energy and capsid elasticity. While the resulting height distribution has been proposed as a non-invasive proxy for viral sti$\hookleftarrow$ness, the physical origin of its broadening is unknown. In this work, we combine Atomic Force Microscopy (AFM) topography measurements of Adeno-Associated Virus (AAV8) and Hepatitis B Virus (HBV) with a theoretical shell-deformation model to identify the determinants of height dispersion. By modeling the viral shell as an elastic body under adhesive load, we evaluate the relative contributions of thermal fluctuations and mechanical heterogeneity to the observed height dispersion. We demonstrate that thermal noise is insu cient to explain the width of the distribution. Instead, the data support a model where the dispersion in height arises from the intrinsic variability of capsid sti$\hookleftarrow$ness. This variability is associated to the surface inhomogeneity of identical capsids. Our results validate that, when this inhomogeneity is accounted for, the height distribution of adsorbed particles provides a quantitative measure of viral mechanics without the need for individual nanoindentation.

Straight dark solitons in two-dimensional (2D) quantum fluids usually decay by transverse modulational instability, with no intrinsic suppression in contact-interacting Bose–Einstein condensates (BECs). We theoretically show that anisotropic long-range interactions in a quasi-2D dipolar BEC stabilize an embedded straight soliton, with spontaneous stripe order providing stronger pinning. The excitation spectra show that the lowest transverse solitonic branch remains gapped, while stripe-supersolid density modulation further hardens this branch and increases the soliton bending stiffness, penalizing transverse deformation. Accessible in current $^{166}$Er and $^{164}$Dy platforms, these results establish interaction-driven protection for straight dark solitons in structured quantum fluids.

Opinions play a crucial role in shaping collective phenomena such as political polarization, cultural integration and demographic change. By continuously changing social environments in which opinions evolve, human migration serves as an important driver of collective opinion formation. While migration and opinion dynamics have both been extensively studied, the few existing models that couple the two are primarily deterministic and therefore cannot capture demographic fluctuations, finite-size effects or stochastic transitions between emergent collective states. To address this limitation, we introduce a unifying stochastic framework for opinion dynamics over migration networks that couples local opinion transitions, demographic processes and migration between communities. The dynamics are formulated through a spatio–temporal master equation, which provides a probabilistic description of the underlying population process. From this microscopic representation, we derive deterministic mean-field equations governing the co-evolution of community sizes and opinion compositions, thereby linking agent-level interactions to macroscopic population behavior. Using two representative case studies, we demonstrate how stochasticity and migration can qualitatively change the emergent dynamics and collective outcomes, including the emergence of consensus, polarization and the stabilization of oscillatory opinion dynamics. These examples highlight the rich interplay between social interactions, demographic change and migration in deterministic and stochastic settings, and they demonstrate that migration should be viewed as an integral component of collective opinion formation rather than only an external demographic process.

Metal thiophosphates are emerging as a multifunctional material platform for neuromorphic electronics due to their accessible polar phases and ion dynamics on biologically relevant timescales. While resistive switching in these materials is frequently attributed to ferroelectric or antiferroelectric polarization, the intrinsic role of ion dynamics remains underexplored. Here, we isolate and demonstrate purely ion-driven resistive switching in paraelectric CuCrP2S6. Robust and reproducible resistive switching is observed in the absence of measurable ferroelectricity. The conductance can be tuned through both voltage amplitude and sweep rate, revealing a rate dependence characteristic of ion dynamics. The resulting resistance states exhibit controllable volatility, where switching rate determines the decay time constant of the readout current, attributed to ionic relaxation. Using either inert or reactive electrodes, we observe electrical evidence of solid-state redox activity associated with the interfacial reduction of native Cu+ ions, enabling controlled formation of filamentary conduction pathways. Analysis of this process allows extraction of the Cu+ diffusion coefficient, providing quantitative insight into the underlying transport kinetics. The understanding of ionic-redox based resistive switching in CuCrP2S6 is crucial for unleashing its full potential as a material platform for dual- or multi-mode operation.

Classical invisibility cloaks are designed to suppress selected scattering signatures and thereby make an object appear absent to external electromagnetic probes. However, the suppression of a classical scattering observable does not, by itself, establish that all information about the concealed object has been removed from the detected quantum state of light. Here we formulate the detectability of classically cloaked objects as a quantum-state distinguishability problem. Treating a linear passive cloak as an effective Gaussian quantum channel acting on the accessible detected modes, we show that local quantum undetectability requires the detected first and second moments to be independent of the hidden-object parameter. In this framework, quantum Fisher information provides an operational criterion for whether the concealed parameter remains estimable from the detected output state. We derive displacement- and covariance-level detectability conditions and show that a nonzero parameter imprint surviving in the detected Gaussian state leads to a nonzero accessible quantum Fisher information. To connect the criterion with a physical cloaking model, we analyze a regularized cylindrical transformation-optical cloak in the Born limit and compare the scaling of the classical scattering response with the derivative-based quantum sensitivity. The analysis shows that reducing a scattering amplitude is not equivalent to eliminating local quantum-state sensitivity. Loss, environmental noise, and finite numerical aperture degrade the accessible information, but quantum undetectability is reached only when the parameter imprint is removed from the detected state or projected entirely outside the accessible subspace. These results provide a Gaussian-channel framework for assessing when classical cloaking does, and does not, imply quantum-state undetectability.

Under certain conditions, a long particle beam self-modulates in a dense plasma, that is, it breaks down into a train of short, stable microbunches under the influence of its own wakefield. During this process, the beam also changes its radial profile: initially having a Gaussian shape, it evolves to a highly peaked equilibrium state with a density singularity on the axis. This change makes individual beam slices several times more efficient at exciting the wakefield. We have developed an analytical iterative model describing the transverse equilibrium state of the microbunches, which is applicable to most beam cross-sections. The model is based either on the conservation of the transverse adiabatic invariant or on empirically established relationships in cases where adiabaticity is violated. It predicts the radial profiles of beam density and wakefield potential, as well as the particle distribution in the transverse phase space. The model is benchmarked against numerical simulations and demonstrates a high degree of accuracy. In addition to the full model, we present simplified engineering formulas based on elementary functions that bypass iterative procedures

This work presents test beam characterization of the ARCADIA Main Demonstrator 3, a 200 $\mu$m thick Fully Depleted MAPS developed using a custom LFoundry 110 nm CIS process on a high-resistivity substrate. Measurements using a 120 GeV proton beam demonstrate a detection efficiency exceeding 99% and a spatial resolution down to 3.8 $\mu$m. The study evaluates cluster size, spatial resolution, and efficiency as a function of the threshold and reverse bias voltage.

Quantum parameter estimation utilizes quantum mechanical effects to attain higher measurement precision than classical schemes. In practical implementations, however, noise is inevitably present during the measurement process, causing a decrease in precision. Quantifying the impact of noise on different measurements is of considerable significance. Here, we experimentally investigate robust optimal measurements based on the theory of Fisher information measurement noise susceptibility (FI MENOS), which quantifies how susceptible a measurement is to noise. By constructing a polarizing Mach-Zehnder interferometer, we implement phase estimation under controlled noise. Our results indicate that different measurements exhibit distinct sensitivities to noise. To assess the influence of diverse noise types on precision, we further construct an experimental setup capable of introducing various forms of noise. The experimental results affirm that FI MENOS represents the worst-case scenario for estimation precision, enabling us to evaluate the noise immunity of optimal measurements. Our work provides a deeper insight into quantum metrology with noise, marking a notable advancement in quantifying the robustness of quantum estimation schemes against measurement noise effects.

In the first part of this work, we construct the first type of two-dimensional (2D) hyperbolic neural quantum state (NQS) in the form of the Lorentz 2DRNN (Recurrent Neural Network) and benchmark its performance against the Euclidean 2DRNN in the paradigmatic $N \times N$ 2D Transverse Field Ising Model (2DTFIM) setting with different lattice sizes up to N = 12 at different transverse magnetic field strengths. We find that hyperbolic Lorentz 2DRNN NQS robustly outperforms Euclidean 2DRNN NQS in our experiments when the system is at the phase transition point, where the physics can be described by a conformal field theory (CFT) - known via the AdS/CFT holographic duality to possess an underlying hyperbolic geometry. In the second part of this work, we benchmark the performances of recently introduced one-dimensional (1D) hyperbolic NQS ansatzes (including Poincare RNN/GRU and Lorentz RNN/GRU) against their Euclidean versions in $N \times N$ 2DTFIM, converted to a one-dimensional sequence. The findings extend our previous results showing that 1D hyperbolic NQS robustly outperforms 1D Euclidean NQS, thanks to the combined effects of the induced 1st and Nth neighbor interaction hierarchy and the CFT physics at criticality. While studies on larger system sizes remain future work, this study serves as a proof-of-concept for the utility, effectiveness, and superior performance of 1D and 2D hyperbolic NQS ansatzes compared to conventional Euclidean NQS models in many-body quantum systems - particularly those exhibiting structural hierarchy, criticality, or a combination of both.

Numerical simulation of deformable particle suspensions in Stokes flow is computationally expensive due to nonlinear fluid-structure interactions, evolving interfaces, and multiscale hydrodynamics. We present VesNet, a hybrid framework that accelerates two-dimensional vesicle suspension simulations by approximating vesicle self interactions, including background flow coupling and short-range lubrication forces, while retaining conventional modules for boundary reparameterization and far-field hydrodynamics. A GPU-accelerated implementation achieves over 100x speedup compared to a multithreaded MATLAB CPU boundary integral solver and about 5x relative to its GPU counterpart. VesNet accurately captures key dynamics, including single-vesicle phase behavior, pair interactions, and large-scale suspensions in Taylor-Green and Poiseuille flows, enabling efficient simulations of thousands of vesicles on modest computational resources.

Organic diradical molecules have emerged in recent years as highly promising candidates for next generation optoelectronic and quantum information technologies. As efforts continue to synthesise diradicals with unique photophysical properties, developing our theoretical understanding of their complex electronic structure is crucial to enable rational design strategies. However, existing computational tools struggle to properly describe the multi-reference character of diradical electronic states while also preserving a low computational scaling that enables fast calculations on large emissive diradicals. This work presents a new computational method, termed Diradical Extended Restricted Open-shell PPP (D-ExROPPP) theory, which we show yields accurate and spin-pure electronic states at a very low computational cost relative to existing approaches. This is achieved by implementing a novel, spin-adapted configuration interaction approach within the semi-empirical Pariser-Parr-Pople framework. D-ExROPPP is then used to study the electronic structure of three prototypical organic diradicals, and benchmarked against results from computationally intensive, post-Hartree-Fock methods. Excitation energies for these molecules are predicted that show a similar level of accuracy as high-level ab-initio calculations, while requiring up to five orders of magnitude less computational time. We further apply D-ExROPPP to a number of recently reported stable, emissive, organic diradicals. In the majority of cases, electronic transitions measured in experimental UV-vis spectra are reproduced with qualitative accuracy and the spin states involved can be reliably assigned. Together, these results establish D-ExROPPP as a promising new tool for efficiently predicting and interpreting the photophysics of organic diradical molecules.

The isoreticular index of the eight-member embedded RHO zeolite hierarchy operates as a phenomenological design knob that tunes the mechanical critical pressure of the framework valve from $\sim 0.94$ GPa for parent RHO down to a predicted $\lesssim 0.03$ GPa for the largest member PST-28, more than an order of magnitude lower across a single family of synthesizable nanoporous solids. The molecular-valve effect that defines zeolite RHO (a reversible centric-to-acentric phase transition triggered by water, gas pressure, or mechanical loading) is shown here to be not a peculiarity of the smallest member but a generic property of the whole hierarchy, with a critical pressure that decays exponentially with the isoreticular order $k$. Combining lattice dynamics, full elastic-constant tensors, and finite-temperature free-energy reconstructions within a classical core-shell description of the pure-silica frameworks (independently validated against r$^2$SCAN+rVV10 density-functional theory on $G_1$ and $G_2$), we find that the entire family is well described by an effective mean-field Landau picture in which the framework distortion couples quadratically to the volumetric strain. We emphasise that $p_c$ is a mechanical (hydrostatic) critical pressure of the bare pure-silica framework, used as a proxy for the intrinsic framework softness and for the cation- and dehydration-driven response of the real aluminosilicates; it is not a gas-adsorption pressure. The exponential extrapolation places $p_c(G_6\text{-}G_8) \lesssim 0.1$ GPa (model-dependent band $0.03$-$0.15$ GPa), identifying the higher-order members as the softest, most stimuli-responsive frameworks of the hierarchy; whether this intrinsic softness translates into guest-driven switching at low gas activities will depend on the Al distribution, extra-framework cations and adsorbed molecules, and remains to be tested experimentally.

Rydberg atoms have become a promising novel type of microwave sensor due to their excellent physical properties – broad frequency coverage and large electric dipole moments. High sensitivity and broad instantaneous bandwidth are two indispensable requirements for deployable Rydberg microwave sensors. However, enabling broadband operation while retaining high sensitivity has been a longstanding barrier limiting their applications. We propose and experimentally demonstrate a Rydberg microwave sensor whose instantaneous bandwidth is significantly enhanced via an auxiliary microwave field. By finely modulating the Rydberg energy levels with this field, we broaden the bandwidth substantially while retaining the sensor's inherent high sensitivity. An instantaneous bandwidth of 44.6$\,$MHz ($\pm$22.3$\,$MHz) with a sensitivity of 225.7$\,$nV$\,$cm$^{-1}\,$Hz$^{-1/2}$ is realized in a thermal \(^{87}\)Rb vapor with the local microwave frequency of 16.03$\,$GHz. Our work delivers concurrent broad instantaneous bandwidth and high sensitivity for Rydberg microwave sensors, paving a technically viable path for their practical deployment in broadband microwave metrology, radar, and wireless communication.

We provide an accelerated computational framework to solve electromagnetic scattering problems in planarly layered media arising from extreme ultraviolet (EUV) lithography. To achieve this, we reformulate the EUV scattering problem into a scattering problem on a homogeneous background, in which the electromagnetic contribution of the layered media is captured by a recursively updated reflection of the layered stack. The system is numerically solved by employing the pseudo-spectral frequency-domain method paired with an iterative solver, whose iterative convergence is expedited by a free-space Green's function preconditioner. The proposed framework is evaluated on EUV mask geometries and multilayer mirror stacks, demonstrating a significant speedup over the conventional pseudo-spectral frequency-domain method.

The particle-in-cell (PIC) simulation method is often understood to solve the collisionless Vlasov equation due to the finite shape of its macroparticles. In reality, it can suffer from artificially high collisionality due to the underresolution of particle number; i.e., the use of a large macroparticle weight. The degree to which particle shape effects compensate for a large macroparticle weight in 1D, 2D, and 3D is presented. The collision time is calculated from PIC simulations based on the decay rate of the velocity autocorrelation function and compared directly with the kinetic theory of Okuda, Birdsall, and Langdon. The theory is found to accurately predict the simulated collision time with varied grid spacings, plasma conditions, and simulation dimensionalities. The result is a means to predict the timescale of self-consistent Coulomb interactions in the PIC simulation and thus characterize the relevance and implications of numerical thermalization as a function of grid spacing and macroparticle weight. It is determined that reaching the physical thermalization time, let alone approximating the collisionless Vlasov limit, may often be intractable in 3D for macroparticle sizes that resolve the Debye length.

The cavity-magnon-qubit system has recently been demonstrated as a new platform for preparing macroscopic quantum states in magnonic systems. Here, we propose to prepare a two-mode magnonic cat state, which is also a non-Gaussian entangled state, based on this practical system involving two yttrium-iron-garnet (YIG) spheres and a superconducting qubit coupled to a common microwave cavity. By adiabatically eliminating the cavity and resonantly driving the qubit, an effective magnon-qubit conditional-displacement interaction is achieved. Further working in the magnon-magnon strong-coupling regime and considering two identical magnon frequencies and coupling strengths to the cavity, two hybridized magnon modes are formed, of which the bright mode is prepared in a cat state after a projective measurement on the qubit, while the dark mode remains in its initial vacuum state. Such a state corresponds to a two-mode cat state of two original magnon modes, which share strong non-Gaussian entanglement. We also discuss practical dissipation and dephasing effects on the cat state. The results indicate that strong nonclassicality and non-Gaussian entanglement are present in the two-mode cat state using fully feasible parameters.

Discovering solid-state materials with target properties remains a central challenge in computational materials science. Existing approaches – high-throughput screening, surrogate optimization, and generative models – require extensive evaluations or training data and extrapolate poorly to unseen compositions. Here we develop a first-principles inverse-design framework, integrating reverse-mode automatic differentiation (AD) into KKR-CPA – the Korringa–Kohn–Rostoker method with the coherent potential approximation – where atomic compositions are continuous variables to be optimized. Reverse-mode AD yields gradients of objective functions with respect to composition at a cost independent of the number of candidate elements, enabling gradient-based optimization to identify materials from compositional spaces spanning dozens of elements. In this framework, any computable quantity can serve as the objective. We demonstrate this generality through two contrasting applications, magnetic alloys and half-metals, yielding candidates such as (Lu$_{0.553}$Yb$_{0.447}$)(Co$_{0.759}$Fe$_{0.241}$)$_2$Fe$_3$ and FeZr(Sb$_{0.94}$Te$_{0.06}$). Our framework offers a physically grounded route from a target property to the material that realizes it.

The state of the tunneling electron under the potential barrier is important in strong laser-atom interaction but is difficult to identify. Recent experiments showed that the tunneling electron may be located in a bound state with high symmetry [Phys. Rev. Lett. 134, 213201 (2025)]. However, the quantitative characteristic of the tunneling state in a tunneling event remains unclear. Here, we study tunneling ionization of atoms in strong circular laser fields. The calculated photoelectron momentum distribution (PMD) through numerical solution of time-dependent Schrödinger equation (TDSE) presents an isotropic ring-shaped distribution and the most probable momentum (MPM) along the ring can be easily identified. The kinetic energy related to MPM is remarkably smaller than that predicted by the strong-field approximation (SFA) that ignores Coulomb potential. Surprisingly, for different target atoms and laser parameters, the kinetic energy difference of MPM between TDSE and SFA is always close to half of the corresponding Coulomb potential at the tunnel exit. This phenomenon can be well described by a proposed model, which indicates that the tunneling electron is in an exit-position-dependent quasibound state agreeing with the virial theorem. These results quantitatively reveal the characteristics of tunneling states from a statistical perspective.

This study investigates propeller hydrodynamics at intermediate Reynolds numbers (Re), crucial for small-scale robotic systems but still uncharted. Experiments on a propeller-driven underwater vehicle and numerical simulations reveal thrust reversal–a phenomenon where clockwise propeller rotation leads to backward motion–in the approximate range 1.3 < Re < 150 under specific conditions. Notably, counterclockwise rotation consistently results in backward motion. Simulations reveal that this behavior arises when centrifugal suction, an inward force along the axis caused by radial outward flow from the propeller's rotation, dominates over fluid backward acceleration, the primary thrust mechanism at high Re. These findings provide critical insights into the unique dynamics of the intermediate Re regime and inform the design of efficient propulsion systems for miniature aquatic robots.

We prove a three-dimensional positive-cone continuation criterion for the stress-diffusion-free Oldroyd-B system on the periodic torus. Writing the positive conformation tensor as A = exp(B), we show that finite-time breakdown of a strong H^s solution, s > 5/2, can occur only through loss of the logarithmic spectral envelope of A or divergence of the endpoint vorticity clock given by the time integral of the B^0_{infty,1} norm of curl u. The proof combines compact positive-cone envelopes, endpoint Biot-Savart estimates, and high-order logarithmic conformation estimates, without using stress diffusion. We also derive a positive-cone Reynolds admissibility criterion with an exact residual-work cost. The least L^2 conformation residual needed to pay positive pressure-free residual work is determined by the entropy-dual lever G = I - A^{-1}, and this cost degenerates quantitatively near the equilibrium A = I. Together, the two criteria identify the same positive-cone obstruction in the strong and relaxed regimes: before breakdown one must control the endpoint flow clock on a compact logarithmic cone, while after passage to a relaxed description positive residual work must be paid for by an exact entropy-dual conformation defect.