Papers

In recent years, data-driven approaches emerged as alternatives to traditional physics-based retrievals, taking advantage of machine learning techniques such as learnable pseudoinverse, random forests, or deep learning architectures. Classical data-driven models generalize poorly to out-of-sample regimes, as they optimize over finite datasets without incorporating underlying physical laws. This often requires large models and extensive data to achieve reliability. Physics-Informed Neural Networks address this by embedding physical constraints into the learning process, enabling improved extrapolation. However, they requires substantial computational cost due to the need to solve governing equations at each training step. In this work, we introduce a novel deep learning architecture, based on latent twin approach, that balances model complexity, dataset size, and training cost, while providing a quantitative measure of data quality. This architecture is applied to IASI spectra, with the goal to assess the robustness of this method for retrieving atmospheric profiles, including temperature, water vapor, ozone, surface emissivity, and surface temperature, in real-world clear-sky conditions. The algorithm is first applied on synthetic radiances derived from the NWP SAF database using the fast radiative transfer code sigma-IASI/F2N. After validating the architecture on synthetic data, the algorithm is applied to IASI Level 1C observations, along with their corresponding Level 2 products which serve as reference to evaluate the reconstruction accuracy of the autoencoder-based retrieval. The retrieval performances are discussed along with possible strategies to provide an error analysis for the reconstructed thermodynamical profiles.

Diffusion-weighted MRI (dMRI) is a powerful tool for quantifying cellular microenvironment parameters. This study proposes a physics-assisted deep learning (DL)-based denoising framework designed to enhance dMRI signal quality and improve the robustness of subsequent biophysical model fitting. A dataset of paired noise-free and Rician-noise-corrupted dMRI signals was generated using the IMPULSED-dMRI signal model. Three denoising architectures were evaluated: Convolutional Neural Networks (CNN), Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM) networks. Denoised signals were then fitted to estimate cell diameter $d$, intracellular volume fraction $V_{\mathrm{in}}$, and extracellular apparent diffusion coefficient $D_\mathrm{ex}$. DL-based processing substantially improved dMRI signal denoising. The MLP and LSTM achieved similar performance, with the LSTM slightly better overall, and both outperformed the CNN. In the subsequent model fitting step, the LSTM produced modest reductions in parameter MAE. The dominant benefit was fitting stabilization, with the overall fitting failure rate reduced from 57.6% to 17.7%. The proposed framework improves dMRI signal quality and stabilizes subsequent IMPULSED-based microenvironmental parameter fitting.

Recent research grounded in experiments has connected neural ring models of vision to how animals navigate a complex landscape of attractive targets. In this paper we investigate the mathematical and biological implications of a three-stage model where animals pre-process visual stimuli to identify a discrete set of targets, process this input to select the dominant targets, and then post-process this information to navigate the landscape. Incorporating finite target sizes and a neural density allows consistent target acquisition and pursuit reflecting what is seen in nature. Mathematically, we show this model corresponds to an energy minimization problem, simplifying both its analysis and numerical implementation. Biologically, we argue that the model is physiologically motivated, reproduces experimental observations, and presents a fairly direct pathway to decoding neural geometry via empirical data. Our results also demonstrate an analysis pipeline for future model extensions and suggest specific avenues for experimental research to test and parameterize our quantitative framework.

We develop a moisture vorticity theory for the boreal summer quasi-biweekly oscillation (QBWO) in the tropics and analyze it with the aid of GPT-5.5. The model is formulated on a weak-temperature-gradient slow manifold, in which moisture anomalies diagnose the divergent circulation while the rotational circulation evolves through Rossby wave dynamics and vortex stretching. The key coupling is controlled by eddy advection of the background moisture gradient, with the projection $(\mathbf{k}\cdot\nabla\bar q)$ modifying propagation and the cross-gradient factor $(\mathbf{k}\times\nabla\bar q)_z$ controlling moisture extraction and growth. In the weak-coupling regime, which is relevant for the real-world situation, the QBWO is a Doppler-shifted Rossby mode whose growth or decay is determined by phase-coherent moisture extraction and subsequent vortex stretching; moisture damping weakens this feedback but does not introduce a sharp threshold in the minimal linear model. In terms of subtropical geographical locations, this renders the QBWO unstable over the Bay of Bengal with an intraseasonal growth rate and co-located vorticity and moisture anomalies, and it decays over relatively drier regions such as Central and West Africa with a quadrature relation between vorticity and moisture anomalies. The equations also admit a strong-coupling regime where the system approaches a moist-vortex limit. The theory interprets the QBWO as a Rossby-moisture-vorticity instability whose regional behavior depends on background moisture-gradient geometry, coupling strength, damping, and mean-flow advection.

A spacecraft closing on a target from three hundred kilometres to contact flies one guidance law across five orders of magnitude of range, and a controller that is provably safe at close range can lose that guarantee completely at long range while continuing to fly as though nothing were wrong. This paper derives the range at which the guarantee lapses and uses it as a design rule. The bound compares the prediction model's own linearisation error against the disturbance set the controller was built to reject, and needs only the sampling period, the orbit and that disturbance bound, so it can be evaluated before any simulation. On a Mars Sample Return approach it disqualifies the homing phase, where most of the propellant is spent, and clears the other two. Re-posing the disqualified phase in relative orbital elements restores the guarantee; re-posing a phase the rule already clears, in a frame two orders of magnitude more accurate, changes propellant by under a tenth of one per cent, and it is that second prediction that makes the rule falsifiable rather than descriptive. The constraint tightening also ties the prediction horizon to feasibility, so the horizon search limit becomes a mission parameter rather than a solver setting. Against a reimplementation of a published benchmark that reproduces its propellant to within one per cent, over five hundred dispersed Monte Carlo transfers per case on matched seeds, the controller saves 29% of the propellant on a circular target orbit and 40% on an eccentric one, docking inside the 0.20 m capture requirement on essentially every draw at a median miss near 5 cm. Two results run the other way: the saving is bought with time of flight and computation, and it comes from what the guarantee demanded of the terminal condition rather than from better disturbance rejection. Recursive feasibility and asymptotic stability are not claimed.

The Page–Wootters construction gives time a Hilbert space, representing it as an internal degree of freedom whose readings the rest of the system evolves with respect to. We ask whether such a clock degree of freedom can carry a topological invariant, and we propose a photonic architecture in which it does and in which the invariant can be measured. Signal–idler pairs from spontaneous four-wave mixing in a coupled microring array supply the clock–system partition, with the idler occupying a Rice–Mele band whose staggered coupling and detuning are cycled adiabatically around a gap-closing point. One cycle acts on that band as a rigid translation by \(C\) unit cells, times a geometric phase periodic in the crystal momentum, times a dynamical phase, an identity exact in the adiabatic limit and independent of the state on which it acts. The readout targets the winding of that geometric phase across the Brillouin zone. Reversing the traversal of the pump loop cancels the dynamical phase and isolates twice the geometric phase in a two-photon coincidence fringe, while energy anticorrelation lets a filter on the signal scan the clock momentum without touching the clock and supplies the phase reference relationally, in place of an external optical reference. The estimate is protected on two independent levels, by the gap against smooth deformation and by the digital character of the unwrapping against noise below a threshold. A parameter budget anchored to thin-film lithium niobate places the requirements within reach at a conservative operating point.

The LDA-1/2 method provides an efficient correction to semilocal density functional theory for improving ionization energies and band gaps, yet its application to molecular systems has remained limited. In this work, we present an all-electron finite-element implementation of LDA-1/2 within the NESSIE electronic-structure framework and apply it to the GW100 molecular benchmark set with systematically controllable numerical accuracy. The self-energy correction is constructed explicitly from neutral and half-ionized calculations for each molecule, avoiding the use of precomputed atomic correction potentials. The real-space finite-element formulation enables systematic convergence with respect to the discretization and provides a controlled assessment of LDA-1/2 performance. For the GW100 set, the present implementation yields a mean absolute error of 0.472 eV and a root-mean-square error of 0.645 eV relative to CCSD(T) reference ionization energies, substantially improving upon conventional LDA and the previously reported LAPW implementation of LDA-1/2, while achieving accuracy comparable to G0W0@PBE. Convergence tests show that third-order finite elements are sufficient to reach or approach chemical accuracy relative to higher-order calculations for the representative systems considered. The resulting corrected Hamiltonian also improves several lower lying valence states relative to LDA, although the improvement becomes less systematic away from the HOMO. This work provides accurate LDA-1/2 benchmark data for molecular systems and establishes a rigorous finite-element foundation for future molecular GW calculations.

The growing scientific literature contains decades of experimental and computational results that could support data-driven and physics-based modeling, yet much of this infor- mation remains locked in publications and is not readily usable for large-scale analysis or sci- entific software. Building structured databases from the literature is particularly challenging whenrelevantstudiesmustfirstbediscoveredamonglargecollectionsofpapersandreported quantities must be extracted with enough scientific context to remain usable. We present LitCurate, an open-source framework for building scientific databases from the literature using large language models within an auditable, stage-wise curation workflow. LitCurate integratesliteraturediscovery, relevancescreening, full-textprocessing, andstructuredinfor- mation extraction while retaining intermediate results and provenance, allowing researchers to inspect and revise individual stages rather than treating automated curation as a black- box process. We apply LitCurate to construct an equation-of-state database of lower-mantle and lower-mantle-relevant high-pressure mineral phases from experimental and theoretical studies, comprising 1,334 entries from 205 papers. The resulting dataset links reported equation-of-state parameters to mineral phases, compositions, equation formulations, meth- ods, and parameter constraints, and labels values as source-reported or citation-reported when provenance can be determined. The records are available through a searchable web application. By connecting scientific literature to traceable, machine-readable data, LitCu- rate provides a reusable approach for transforming accumulated literature into resources for scientific analysis and computational modeling.

I explore Conway's Game of Life (GoL) on six composite Archimedean lattices. On the Kagome lattice, on which small gliders and puffers appear particularly frequently across inputs, I use the output of a symmetry-constrained evolutionary search algorithm to construct a novel glider gun. The glider gun comprises four interacting bouncers and stably emits a small glider every 276th generation. Serving as an extension of classical GoL, I also propose cells with a phase degree of freedom and an associated local phase rule, which on the Kagome lattice is demonstrated to host phase-periodic gliders. This enables the possibility of phase-sensitive and interference-based computations.

Identifying the smallest set of elements whose removal dismantle a complex network, known as the network dismantling problem, is a fundamental task with many practical applications. Whereas network dismantling has been extensively studied over the past decade, most work has focused on developing efficient algorithms for large but finite networks. By contrast, the physics of the network dismantling process, namely how the network structural connectivity is affected by the removal of nodes or edges, remains largely unexplored in the thermodynamic limit. Here, we shed light on this understudied aspect of network dismantling by introducing an adaptive biased percolation process able to optimally dismantle a network. Through a systematic analysis of synthetic network models, we find that the proposed percolation process displays a universal phase transition, characterized by the abrupt and simultaneous disappearance of both the giant connected component and the largest 2-core, across networks with markedly different degree distributions. Simulations on real networks further support this universality, indicating that the physics of network dismantling is insensitive to a broad range of topological properties. Together, these results suggest that a topology-agnostic theory could be developed to explain the critical behavior of network dismantling.

Inferring elastic constants from resonant ultrasound spectra is a nonlinear and typically overdetermined inverse problem based on finite spectral data. We formulate the Rayleigh-Ritz inverse problem as a constrained inverse-isospectral problem on the set of physically admissible elasticity tensors. This induces effective low-dimensional variables for the inverse map on the admissible elasticity manifold: length and elastic scales, aspect-ratio coordinates, scale-free spectral features, and stability-respecting elastic ratios. We use these variables to construct a physics-informed learning pipeline in which a regression model acts only on reduced spectral and geometric features, while scale recovery and final elastic-constant reconstruction are imposed analytically. For the full cubic benchmark, the reconstructed constants have MAE values of $20.37(35.15)$, $24.30(41.33)$, and $2.13(3.66)~\mathrm{GPa}$ for $C_{11}$, $C_{12}$, and $C_{44}$. In the fixed-geometry benchmark, the corresponding cubic MAPE values are $4.14(3.87)\%$, $8.31(8.50)\%$, and $2.44(2.86)\%$, while the isotropic values are $4.0(3.6)\%$ and $0.4(0.3)\%$ for the bulk and shear moduli. The inverse problem then becomes a constrained regression problem in variables adapted to the geometry, scaling, crystal symmetry, and thermodynamic stability of Hookean elasticity.

Quantum optomechanical technologies benefit from mechanical oscillators that are high-frequency, can be coherently driven, and are capable of coupling to other quantum systems. Diamond supports all of these criteria: its large elastic modulus increases mechanical resonance frequency, its low nonlinear optical absorption increases the allowed intensity of fields used for coherent optomechanics, and it hosts spin qubits that interact with mechanical modes. Here we demonstrate a diamond optomechanical crystal cavity that supports multiple mechanical resonances with $\sim$12 GHz frequency and high $Q_\text{m} \times f_\text{m}$ product that can be coherently coupled to multiple optical modes. By exciting this sideband resolved system into mechanical self-sustained oscillations, we generate a frequency comb spanning 143 GHz. Analysis of the comb spectrum, combined with systematic characterization of the system's optomechanical coupling, allows us to quantitatively show that its mechanical oscillation amplitude reaches 130 pm. This corresponds to a maximum total dynamic strain of $1.1 \times 10^{-3}$ that is sufficiently high for future demonstrations of optomechanical control of diamond spin qubits.

Training effective foundation models requires massive and organized datasets, yet scientific domains such as nuclear fusion present unique challenges due to largely heterogeneous and sparse data. Here we characterize the data used in developing such a model: with over 20 sensor types spanning 5 orders of magnitude in sampling rate, mixed tensor structures (point measurements, spectrograms, images), and nonstationary physics. We analyze our input complexity and discuss trade-offs between temporal context and frequency resolution. Our analysis provides a template for representing multi-modal fluctuation data at scale, with implications for both multi-modal control systems and nuclear fusion.

Neutral-atom quantum computers are a promising platform for fault-tolerant quantum computation, but logical performance depends on systemic realistic noise factors during syndrome extraction. In dual-isotope Yb arrays, the roles of data and ancilla qubits are separated spectrally, allowing ancilla qubits to be measured in place without additional transport or shelving operations. Here we quantify the advantage of a dual-isotope Yb architecture for surface code memories. We develop an experimentally motivated Clifford-compatible noise model for dual-isotope 171Yb-174Yb systems using generalised Pauli twirling and implement it as a wrapper for Stim called DualYbSim, which has been packaged as an open source Python library. Simulations of rotated and XZZX surface codes show that a dual-isotope architecture with in-place measurement achieves the lowest logical error rates among the architectures considered, outperforming single-isotope schemes based on shelving or zoned measurement. Our error-budget analysis also identifies Rydberg-state decay as the dominant limitation, contributing to 74-80% of the logical error rate scaling, highlighting concrete experimental targets for improving FTQC performance.

Massive cryogenic calorimeters read out by transition-edge sensors (TES) can reach eV-scale baseline resolution, but the single-channel dc-SQUID readout conventionally used in rare-event searches, with one amplifier chain and several wires per detector, limits practical arrays to a few tens of channels. We propose to apply microwave SQUID multiplexing ($\mu$MUX), developed for fast X-ray and neutrino-mass microcalorimeters, to massive BGO (Bi$_4$Ge$_3$O$_{12}$) calorimeters operated at $\simeq 20$~mK; sapphire and TeO$_2$ absorbers are covered by the same framework. Using the established thermal and noise model for massive TES calorimeters, we derive a noise model for the multiplexed readout, including the HEMT, two-level-system and SQUID contributions, and its scaling with the multiplexing factor $N_{\rm mux}$. Since the slow thermal signals require only about a kHz of sampling per channel, the limiting requirement is not bandwidth but an input-coil sensitivity of $( 0.1$ to $1)\,\mu$A$/\Phi_0$, a factor of $10$ to $30$ beyond current $\mu$MUX devices, matched to the detector current noise of ($9$ to $10$)~pA$/\sqrt{\rm Hz}$, which is independent of the absorber mass. With a total flux noise of $\simeq$$1.2\,\mu\Phi_0/\sqrt{\rm Hz}$, the readout degrades the baseline resolution by less than $2\%$ up to $N_{\rm mux}=1000$, negligible compared with the TES-limited resolution. These results are implemented in a single-tower design: 52 BGO crystals of 100~g each and one HEMT amplifier. The tower is the unit of a multi-tower array of several kilograms aimed at CE$\nu$NS and dark matter searches.

This paper presents a unified compact model for emerging transistor technologies, including oxide-semiconductor field-effect transistors (OSFETs), 2D material FETs (2DFETs), carbon nanotube FETs (CNFETs), and cryogenic MOSFETs. A unified charge-density formulation is developed to account for quantum confinement, trap charges, and band-tail states in channel charge calculations. A physics-based transport model is introduced to seamlessly capture carrier transport from the long-channel diffusive regime to the short-channel ballistic limit. Scaling models are incorporated to accurately describe 2D electrostatic effects. Cryogenic operation is modeled through the inclusion of band-tail states and temperature-dependent mobility and threshold voltage. The proposed model is validated against experimental data from the fabricated OSFETs with multiple channel lengths and published measurements of 2DFETs, CNFETs, and cryogenic MOSFETs. Excellent agreement is demonstrated across diverse device architectures, operating conditions, and material systems.

Thermoelectric p- and n-type legs are commonly paired by matching their Seebeck magnitudes, although a cooler responds to heat transported through its complete electrical and thermal network. We decompose the leg coefficients into differential thermopower $\alpha=S_p-S_n$ and common thermopower $M=(S_p+S_n)/2$. In a connected steady-state scalar thermoelectric network, a temperature-independent co-shift applied to every electrically active segment is an exact terminal null. A temperature-dependent perturbation of the legs relative to fixed leads is instead physical. At fixed current and shared isothermal endpoints, its first-order cold-port response is the action of $\Gamma_m=T\,dm/dT$ on the difference between the p- and n-branch oriented collection measures. We prove that every continuous $\Gamma_m$ cancels if and only if these measures are equal. In the constant-property, linear-common-mode limit, matching $R_i/K_i^{\rm leg}$ is sufficient and does not require identical legs. One- and two-dimensional calculations confirm the analytic reductions within their stated domains. For split thermal pads, the analysis gives the exact array law $\Delta Q_{c,\Sigma}=\sum_j C_jI_j\Delta T_{c,j}$ and, for series elements with isothermal hot pairs, $I\Delta V_\Sigma=-\Delta Q_{c,\Sigma}$. A representative seven-pair model gives corresponding increments of 7.87 mW and $-2.80$ mV. Branch transfer and endpoint topology therefore provide distinct material-pairing and device-test criteria for common-mode Thomson heat.

Multiphase porous piezoelectric media are essential for advanced transducers and smart sensors. Existing theories typically postulate Newton's second law for each phase or rely on phenomenological Hamiltonian constructions. The former forces ad hoc virtual-mass tensors to describe interphase inertia, while the latter provides no intrinsic safeguard against thermodynamic inconsistency when piezoelectric and multiphase couplings are superposed. In this work, we establish a linear dynamic and constitutive theory for anisotropic multiphase porous piezoelectric media from global energy conservation (GEC). From an abstract energy density functional, Taylor expansion and symmetry constraints derive the standard kinetic and potential energy densities and electric enthalpy, rather than assuming them a priori. Localization of the GEC integral yields the multiphase momentum equations, Gauss's law, the coupled constitutive relations, and the boundary conditions as mathematical corollaries, without invoking Newton's law or Hamilton's principle. The framework eliminates virtual-mass parameters entirely: interphase inertial coupling emerges organically from the off-diagonal kinetic-energy coefficients $\rho_{ij}^{\alpha\beta}$. Because all coefficients derive from a single smooth potential, Schwarz's theorem automatically guarantees Maxwell reciprocity and full thermodynamic self-consistency. The formulations agree with those from Hamilton's principle and reduce exactly to Biot's poroelastic theory and Tiersten's single-phase piezoelectric theory in the respective limits. Finally, linear plane-wave analysis produces a generalized Christoffel eigenvalue equation, and numerical phase-velocity calculations for water-saturated porous PZT-2 illustrate the modal structures and reveal strongly directional electromechanical coupling.

Traditional frameworks of political communication operate under linear, event-driven assumptions that treat voter persuasion as a static, transactional function. This paper introduces Dynamic Consent Engineering (DCE), a novel interdisciplinary paradigm that synthesizes Edward Bernays' foundational principles of public relations with the S-E-E-D (Snowball, Equilibrium, Elasticity, Dominance) framework of system dynamics. By expanding Bernaysian operational constraints into a four-dimensional resource matrix (incorporating algorithmic media infrastructure alongside manpower, mindpower, and capital), we mathematically formalize how democratic institutions construct, optimize, and sustain political dominance. We map Bernays' classic eight-step engineering workflow directly onto non-linear feedback loops, information time lags, and demographic carrying capacities. Through rigorous modeling of systemic feedback structures, we isolate the dynamic root causes of political elasticity, policy resistance plateaus, and threshold-triggered trust collapses. Finally, we establish a four-step diagnostic pipeline and validate the framework via historical reference mode verification, demonstrating that long-term political stability depends not on ephemeral rhetoric, but on the structural synchronization of policy execution and dynamic feedback calibration.

Megawatt-class orbital data centers require continuous maintenance, replacement, inventory, and service capacity in addition to spacecraft power/thermal systems. We formulate an analytical lifecycle framework for permanent/transient failures, modular orbital replacement units, robotic servicing, spare inventory, scheduled technology refresh, correlated faults, cybersecurity, optional human support. The model combines nonhomogeneous component hazards, capacity-weighted availability, multiclass robotic-service capacity, Poisson base-stock inventory, replacement-flow accounting, human-support break-even relations. For a 1 MW cluster with 10 active 100 kW nodes, 1 reserve node, ~200 5 kW compute cartridges, low, nominal, high deployed-mass allocations span ~50-75 kg/kW. Assumptions yield 70.2 random or life-limited interventions and 323-349 planned refresh operations/(MW-year), for a total of 393-419 standardized operations/(MW-year). Analysis gives a first-generation logistics of 5.3-9.0 t/(MW-year), with a nominal case of ~ 6.6 t/(MW year), 560-700 productive robot-hors/(MW-year). Planned refresh exceeds random replacement under the stated component populations, hazards, 3-15-year intervals. At ~400 standardized operations/(MW-year), the post-internal-recovery exception probability is <$10^{-3}$, with an objective near $10^{-4}$ at large scale; terminal non-recovery $p_U$ requires a smaller mission-level allocation. The target catastrophic-loss hazard for a 100 kW node is 0.01-0.03 1/yr. Parametric workload and cost cases place contingency visits at 10s of MWs, periodic campaigns at 10-100s of MWs, dedicated personnel at several 100 MWs to GWs. The reference first deployment is uncrewed, autonomously fault-managed, robotically maintainable, supported by specific inventory based on a 6-month replenishment horizon, compatible with later human access without permanent habitation.

Rydberg entangling gates driven by a two-photon transition in alkali atoms suffer from an adverse scaling of light-shift-induced errors. Robustness to such detuning errors is known to be impossible to achieve in the design of conventional Rydberg gate protocols, where only one of the qubit states is coupled to the Rydberg state. Here, we show that in a more general framework, in which both qubit states take part in the gate, full or partial robustness to these errors can be realized. We present two gate constructions, which either cancel the errors outright or convert them into single-qubit errors that can be corrected locally. We map the regimes – in terms of light-shift strength, intensity inhomogeneity, and Rydberg decay rate – in which these protocols outperform the widely used time-optimal Rydberg gate, and find that they already include the conditions of state-of-the-art experiments. Finally, we show the existence of a Rydberg `fly-by' entangling gate, an important primitive for an emerging class of neutral-atom quantum computing architectures.

We present a finite element framework to simulate rotating mixing devices using the Mortar Element Method as a domain decomposition strategy. The model is implemented within a matrix-free Navier-Stokes framework which uses a high-order Continuous Galerkin method. The discretized domain is subdivided into rotor and stator parts. An Arbitrary Lagrangian-Eulerian approach accounts for the relative rotor-stator motion, and stabilization is ensured through the Streamline-Upwind/Petrov-Galerkin and Pressure-Stabilizing Petrov-Galerkin methods. The rotor-stator domains are connected by an interface composed of mortar cells, and continuity is weakly enforced in a Discontinuous Galerkin fashion by accounting for boundary integrals at the rotor-stator interface. Verifications of the convergence order in two-dimensional steady and transient examples report optimal rates. The geometric non-conformity created at the mortar interface due to rotor rotation does not introduce significant error in the solution. A three-dimensional example is used to investigate the model's scalability, which yields ideal strong scaling for large problems. A two-dimensional Rushton impeller example uses a torque analysis to showcase the mesh convergence, and the corresponding velocity profile is in agreement with existing numerical results. In a three-dimensional pitched blade turbine case, the power number curve (Np vs Re) shows good agreement with experimental data for Reynolds number values from 1 to 2000. An energy balance analysis reports a numerical dissipation of 1% for Re=200 and of 10% for Re=2000. By exploiting modern hardware capabilities through matrix-free methods, the proposed model is a robust, accurate, and efficient framework suitable for simulating flows with rotating geometries.

Spontaneous breaking of a continuous symmetry produces a massless Goldstone mode that can evolve across a degenerate manifold at zero energy cost. Goldstone modes have been identified primarily through excitation spectra, mode softening or collective oscillations. However, their time-domain transport under intrinsic fluctuations and dissipation has remained largely unexplored. Here we directly track the stochastic transport of a Goldstone mode in a self-organized atomic crystal inside an optical ring cavity. The ring cavity maps the order-parameter phase onto the real-space position of the emergent crystal. Without any external perturbation, fundamental photon-scattering recoil drives the collective transport, while cavity dissipation generates friction. We monitor individual trajectories of the atoms and their self-generated optical lattice by measuring the cavity output phase. We find that the diffusion constant decreases as $1/N$, indicating that all atoms move collectively as a rigid object rather than independently. By tuning the Langevin driving force and cavity-mediated damping, we show that the normalized diffusion constant collapses onto a single universal curve. This work extends the study of continuous symmetry breaking from excitation-frequency measurements to real-time tracking of transport, and opens routes for studying non-equilibrium collective transport, phonon dynamics, and defect formation in driven-dissipative quantum matter.

Charge-transfer-to-solvent (CTTS) excitations provide a chemically central route to generating hydrated electrons and initiating redox chemistry in solution, yet the earliest stage of CTTS—the formation of the excited state itself—is usually treated as instantaneous. Here we present a time-domain perspective of how CTTS character builds up during core-level photoexcitation of an aqueous metal ion. Using time-dependent configuration interaction, we simulate the coherent evolution of a dense manifold of core-excited states and track the ultrafast flow of electronic charge from the initially localized site into solvent-supported final states. We find that the dynamics evolves from a few-state, oscillatory behavior to effectively irreversible delocalization, as the charge disperses among many coupled configurations, providing a microscopic mechanism for the early-time emergence of CTTS character. Our results offer a transparent real-time interpretation of what core-level spectroscopies and core-hole-clock-type measurements can probe in solutions, outlining experimental signatures for probing the build-up of CTTS states on the core-hole-lifetime timescale. Looking ahead, attosecond and sub-femtosecond X-ray pump–probe approaches at X-ray free-electron lasers provide a realistic route to directly time-resolve the core-excited CTTS wave packets in solution.

Recent advances in generative artificial intelligence have led to significant potential applications in conventional fluid flows, including those that are turbulent. Can these methods be carried over to studies of novel types of turbulence, such as turbulence induced by non-reciprocity in binary-fluid systems? To answer this question, we analyze the statistics of Lagrangian-tracer particles in non-reciprocal binary-fluid turbulence, which has been studied recently in the non-reciprocal Cahn-Hilliard-Navier-Stokes (NRCHNS). We obtain our ground-truth data via extensive pseudospectral direct numerical simulations (DNSs) of the two-dimensionsl (2D) NRCHNS model. Our study yields a variety of intriguing results for probability distribution functions (PDFs) for particle accelerations and velocity-component PDFs; the latter turn out to be bimodal, completely unlike their 2D-fluid-turbulence counterparts. We relate this bimodality to lane-type structures in Eulerian-velocity components. Furthermore, we characterize Lagrangian multiscaling via Lagrangian velocity increments, their structure functions and flatnesses, and multiscaling exponent ratios, for the first time in non-reciprocal hydrodynamics. Finally, we use generative diffusion models to obtain synthetic Lagrangian trajectories for the NRCHNS system, assess how effectively they can emulate the Lagrangian statistics that we obtain from our DNSs, and highlight open challenges in the application of generative artificial intelligence in non-reciprocal systems.

Forbidden reflections (FRs) in X-ray diffraction have inherently weak intensity and have long been studied, in particular in semiconductors like silicon. They serve as sensitive probes of symmetry breaking, local strain, impurities, and weak charge redistribution. Despite extensive experimental work, the theory of their temperature dependence has typically relied on simplified models. While atomic Debye-Waller factors work well on allowed reflections, their applicability to the valence charge between atoms, which determines the intensity of FRs such as Si (222), is questionable, and previous agreement between theory and experiment relied on ad-hoc Debye-Waller corrections. We compute the temperature-dependent valence charge density $\rho(\mathbf{r},T)$ of silicon from first principles, using two methods: (i) perturbation theory, and (ii) averaging over thermally distorted supercells in a non-perturbative approach. The perturbative expression for the charge density is far more demanding than that for electronic energies, since it depends on the wavefunctions themselves and requires an explicit sum over unoccupied bands. We use an acoustic sum rule to express the second derivatives of the potential in terms of first derivatives, making the expression tractable within existing frameworks. The (222) FR then follows directly from the Fourier transform of $\rho(\mathbf{r},T)$, with no ad-hoc factors. Both methods give similar results, in reasonable agreement with experiment, with thermal expansion noticeably affecting the temperature dependence. The charge density answers a question the measured intensities could not settle: how the valence charge actually redistributes with temperature. Relative to the rigid model, we find more charge in the bonds and less in the core regions, a redistribution that shows up in the intensity as a somewhat weaker temperature dependence of the (222) FR.

We investigate bottleneck formation in turbulence using the Voigt-regularised SABRA model and DNS of the corresponding Voigt-Navier-Stokes (NSV) equations. The Voigt regularisation introduces a scale-dependent slowdown of nonlinear interactions without enhancing dissipation, providing a natural setting to study the interplay between nonlinear transfer and thermalised behaviour. We find three distinct spectral regimes: an inertial range at $k<k_I$, an intermediate equilibrium-like range associated with partial thermalisation for $k_I<k<k_{II}$, and a high-$k$ thermal regime for $k>k_{II}$, where the Voigt contribution dominates the conserved invariant. The crossover to the high-$k$ regime occurs at $k_{II}\sim 1/\alpha$, while $k_I$ marks the onset of thermalised behaviour. Equal and multi-time statistics reveal a progressive suppression of intermittency and a tendency towards Gaussianity at small scales, together with a transition from dynamic multiscaling in the turbulent regime to simple scaling in the equilibrium ranges. The shell model resolves these three regimes over a broad range of scales, while DNS of the corresponding NSV equations reproduces the same qualitative trends, including bottleneck formation, delayed cascade completion, reduced intermittency, and a tendency towards Gaussianity at small scales. We find that bottleneck formation might be associated with scale-dependent dynamical slowdown and incipient thermalisation, rather than being purely dissipative in origin. We provide strong evidence that, in the regime where the regularization parameter $\alpha$ is much smaller than the dissipation length scale, the Voigt model reproduces the same inertial-range turbulent regime and turbulence statistics as the Navier-Stokes (NS) equations. This provides evidence that the Voigt model constitutes an excellent practical approximation to the NS equations for small $\alpha$.

The field of photonic integrated circuits (PIC) has flourished in the past two decades, fueling numerous cutting-edge applications across sensing, networking, data interconnect, and quantum information processing. As a guiding material for PIC, Si$_3$N$_4$ has seen extensive use for its ultra-low loss, broad transparency, and diversity in implementation across both thin and thick films. Although the standard, traditional silicon dioxide (SiO$_2$) on silicon (Si) substrates that underpin the majority of Si$_3$N$_4$ photonics face drawbacks in the form of long-wavelength transparency limited by SiO$_2$, high-stress deposition for anomalous dispersion thick-film Si$_3$N$_4$, and leakage loss to the Si layer for low-confinement thin-film Si$_3$N$_4$. Featuring increased long-wavelength transparency into the mid-infrared, low-stress deposition of Si$_3$N$_4$, and a low index, this work investigates sapphire substrates as alternate hosts for Si$_3$N$_4$ photonics with greater spectral coverage and reduced fabrication complexity. This work presents a robust method of fabricating ultra-low loss photonic integrated circuits on a 500-nm-thick Si$_3$N$_4$-on-sapphire platform, exhibiting record-low losses below $0.1 \rm \;dB/cm$. Implemented using this process are high-Q microrings with intrinsic quality factors in excess of $4.5\times10^6$ and coupled-ring photonic molecules to support nonlinear gain. Leveraging the achievable low loss and high-Q, this work further reports the first demonstration of Kerr-comb and soliton generation on the Si$_3$N$_4$-on-sapphire platform. These advances in loss, quality factor, and soliton generation on this versatile, broad-transparency platform pave the way for future work in spectroscopy and quantum-enhanced sensing across previously prohibited spectral regions for Si$_3$N$_4$ photonics with reduced fabrication complexity.

We present a systematic design for large-scale quantum kernel classification, demonstrated through a quantum support vector classifier (QSVC) for particle-track classification using centroid-based CLAS12 drift-chamber features. Each event is encoded into a six-qubit state via a fully entangled ZZFeatureMap, whose fidelities define a quantum kernel within a standard SVM framework. By decoupling state preparation from kernel construction and distributing evaluation across a multi-node MPI-based HPC allocation, the approach scales to 1.0x10^5 training and 4.0x10^5 test events with an exactly constructed kernel matrix, to our knowledge more than an order of magnitude larger than prior high-energy-physics quantum-kernel studies. Benchmarked against linear, polynomial, RBF, and sigmoid SVM kernels and extremely randomized trees (ERT), the ideal QSVC achieves the highest recall (99.99%) among all models. Under a calibrated hardware noise model (FakeMumbaiV2, 500 training / 2,000 test events), AUC falls from 0.9985 to 0.9671 and peak significance improvement falls from 17.5 to ~3.5, yet recall remains at 99.51% – indicating this signal-retention advantage is attenuated but not eliminated by circuit-level decoherence. Geometric analysis of the quantum embedding shows near-orthogonal inter-class states with coherent intra-class neighborhoods under ideal simulation; under noise this structure compresses toward the maximally mixed state while preserving its relative ordering. These results demonstrate a scalable, reproducible workflow for quantum kernel experimentation at HEP-relevant scale, quantifying the practical cost of realistic hardware noise on quantum-enhanced classification.

We present details of an experimental platform for studying Fermi polarons in a quantum-gas mixture. The system consists of about a thousand bosonic $^{133}$Cs impurities immersed in a deeply degenerate Fermi gas ($T/T_F \sim 0.2$) of approximately $2\times 10^5$ $^6$Li atoms in a single hyperfine state, with interspecies interactions tunable via a Feshbach resonance. Using optical Raman spectroscopy without relative momentum transfer, we perform injection spectroscopy and thereby create the Fermi polaron. Owing to the large mass imbalance between the two species, the setup provides access to previously unexplored regimes of Fermi polarons.