Papers

We develop multilevel adaptive-rank iterative methods for the solution of linear and nonlinear systems arising from high-dimensional partial differential equations. Our contributions are threefold. First, we extend the projection method of Ballani and Grasedyck [6] to enable flexible preconditioning of high-dimensional linear systems in low-rank tensor formats. Second, we construct multilevel preconditioning strategies by adapting geometric multigrid methods to the low-rank setting. In contrast to prior work, which primarily employs multigrid as a standalone solver, we emphasize its role as an efficient and robust preconditioner. Third, we integrate these techniques within an inexact Newton framework for the solution of nonlinear systems. The proposed methods are evaluated on a range of model problems, including both linear and nonlinear equations, to assess their convergence behavior and computational efficiency. The results demonstrate that multilevel adaptive-rank strategies yield robust and scalable preconditioners, providing effective solvers for high-dimensional problems in low-rank formats.

Motivated by the need to extrapolate the results from ground-based experiments to the conditions of high-speed flight, we present the Lie equivalence symmetry analysis of the hypersonic stagnation-point boundary layers. We demonstrate the application of the equivalence symmetry on the set of coupled ODEs which are physically relevant in hypersonics. By allowing the property laws to transform along with the independent and dependent variables, the invariants derived within the similarity-reduced stagnation-point ODE formulation identify families of non-linear maps that can be used to extrapolate the laboratory-scale predictions to flight. In practice, implementing these maps requires the laboratory-scale ODE solution together with the lab- and flight-side thermochemical property data, which are generally available from existing databases. The maps are derived for the similarity-reduced non-dimensional temperature across the boundary layer and are shown to {collapse with independently computed flight solutions} for a range of relevant cases.

We present a rigorous electronic-structure theory of strictly localized orbitals associated with a spatial partition of the one-electron Hilbert space that remains well defined in the complete basis-set limit. Each strictly localized orbital is supported on a spatial domain and may be discontinuous at domain interfaces. Using the interior-penalty discontinuous Galerkin method, these strictly localized orbitals can be employed in variational electronic-structure calculations despite their discontinuities at domain interfaces. As a proof of concept, we present numerical illustrations on one-dimensional diatomic model systems. They show that variational calculations can be carried out in a basis of strictly localized orbitals while maintaining good agreement with conventional calculations. Moreover, these strictly localized orbitals naturally lead to chemically intuitive representations of many-electron wave functions in the spirit of valence-bond theory.

A variation of the measured neutron flux using ${}^3$He detectors at a TRIGA Mk II nuclear research reactor during steady-state operation is reported in this article. The observed effect shows a statistically significant anti-correlation ($> 7\,\sigma$) between the temperature in the reactor pool and recorded neutron count rates. Following reactor start-up under nominal operating conditions, the effect occurs for a specific time period until both thermal equilibrium and a constant neutron count rate are reached. Simultaneous neutron and temperature measurements are performed in different configurations around the reactor in order to gain qualitative insights about the temperature dependent count rate behavior.

The multipoint kinetics (MPK) equations extend point kinetics models by tracking neutron populations in lumped reactor regions, offering improved spatial resolution at modest computational cost. However, determining the coupling coefficient matrix $K$ from transient data remains an open problem without established experimental procedures. This work provides some commentary on the task of recovering $K$ from observable forward flux measurements during reactor transients. Analytical solutions to the Kobayashi MPK formulation in the case of no precursors and with a single precursor family are derived in spectral form, enabling a mathematically rigorous investigation of eigenmode behavior and parameter observability. Coupling coefficient recoverability is investigated using modal sensitivity analysis and the Hessian condition number of the spectral recovery problem. The inclusion of delayed neutron precursors delays this eigenmode divergence but may be insufficient for enabling practical recovery of $K$ in many scenarios. In conclusion, capturing the behavior of nondominant eigenmodes without oversampling the dominant eigenmode is critical to the recoverability of $K$ through transient data.

The scientific utility of large single-dish radio telescopes depends critically on the stability and fidelity of their beam patterns, which govern angular resolution, sensitivity, and polarimetric accuracy. For the 32-m Ghana Radio Astronomy Observatory (GRAO) antenna, electromagnetic simulations reveal residual sidelobes, structural diffraction, and cross-polar leakage that limit performance in high-dynamic-range and polarisation-sensitive observations. To address these limitations, we develop a finite-impulse-response (FIR) spatial filtering framework that reformulates beam optimisation as a digital signal processing problem. By exploiting the equivalence between angular displacement and spatial frequency, classical FIR design methods, window-based and Parks-McClellan algorithms are adapted to operate directly on simulated Jones fields. This approach enables controlled suppression of high spatial frequency artefacts responsible for sidelobes and polarisation mixing, while preserving the telescope's diffraction-limited resolution. Applied to the GRAO 5 GHz beam model, the method achieves substantial reductions in near-in sidelobe ripple, improves beam smoothness, and lowers cross-polar leakage below -30 dB at boresight. These improvements translate into enhanced calibration stability and polarimetric precision, strengthening the telescope's capacity for Very Long Baseline Interferometry, spectral-line surveys, and pulsar timing. Beyond GRAO, the method provides a generalisable, non-invasive, and computationally efficient pathway for beam control applicable to other single-dish and phased-array instruments. The results establish digital spatial filtering as a practical complement to conventional optical or mechanical optimisation, advancing the integration of electromagnetic modelling and signal processing in next-generation radio astronomical instrumentation.

In X-ray scanning probe tomography, reconstruction quality generally improves with larger numbers of projections. However, additional projections increase experiment costs, acquisition time, and the radiation dose imparted to the sample. One mitigation to these trade-offs is to adopt a sequential design of experiments, in which each subsequent measurement is determined as a function of previously acquired data in order to maximize information gain. In scanning probe tomography, a widely used heuristic to maximize information is to align beams with the edges of the sample. A key challenge, however, is that the true sample is unknown, so identifying edge-aligned beams typically requires reconstructing the sample based on available measurements. This work proposes a novel sequential design method that identifies edge-aligned measurements directly from the sinogram, bypassing any reconstruction, thereby improving computational efficiency and reducing the experimental design's susceptibility to reconstruction errors. Our method dynamically selects the next set of measurement beams by maximizing an acquisition function that balances exploration and exploitation over the domain of all possible measurements, improving reconstruction quality while reducing measurement redundancy.

Temporal networks underlie a wide range of social, technological, and biological phenomena, highlighting how temporal inhomogeneities drive interactions in complex systems. Despite vast research on the area, the way temporal network connectivity evolves across time scales remains poorly understood. By analyzing temporal network data of informational and societal origin, involving tens of systems, millions of nodes, and observation periods from days to years, we find systematic evidence of an optimal time scale for coarse-graining interaction events. At this level of aggregation, networks are maximally dynamic in their local structure, while retaining system-wide connectivity. To understand the origins of such a seemingly generic interplay of time and topology, we explore a minimal temporal network model based on uncorrelated renewal processes, and show that intermittent yet globally connected activity may arise solely due to heterogeneities in inter-event times and degrees, and no other system-specific details. All coarse-grained empirical networks studied show persistent patterns of cyclic node degree change yet stationary system-level degree distributions, a striking coexistence of microscopic self-regulation and macroscopic stability. Our results give support to the notion of a universal pattern in temporal networks that involves both time and topology, via the nontrivial interplay of aggregation and temporal inhomogeneity, with consequences for the study of spreading dynamics on networks and the balance between robustness and adaptability in complex systems.

We develop a macroscopic continuous-variable model of stimulated four-wave mixing (4WM) yielding closed-form expressions for the mean intensity, variance, and covariance of bright probe and conjugate beams, valid for arbitrary transverse profiles of the pump and seed to all orders in the nonlinear interaction strength. The model uses a Dirac-comb expansion of the pump field, making the photon statistics computable pixel-by-pixel from one set of expressions. In the plane-wave limit, we recover the phase-insensitive amplifier results. Beyond this limit, the formalism explicitly demonstrates the spatial routing of information observed in earlier experiments: the seed angular spectrum is transferred to the twin-beam intensities, whereas the angular spectrum of the squared pump field is transferred to the spatial cross-correlation between the probe and conjugate. We characterize the parameter window of high-fidelity image transfer in the covariance, which is controlled by the seed-to-pump waist ratio and the interaction strength. Our model provides, in one analytical framework, a theoretical account of experiments encoding information in the bright intensities or non-locally in the spatial cross-correlations.

We investigate the intensity stability of the superradiant laser. Our study focuses on the architecture where a continuous beam of atoms in an electronically excited state crosses the mode of a high-finesse Fabry-Perot cavity, which has been proposed as a new architecture of an active optical clock. We show that such superradiant laser can become unstable and develop chaotic behavior. We derive an analytical criterion for this instability and find that it may only occur when the lifetime of photons in the cavity is significantly shorter than the lifetime of atoms. This criterion allows for refining the necessary parameters to run a superradiant laser as a frequency reference in the optical domain. In particular, we point-out the consequences of the instability on intensity fluctuations and laser linewidth. On the other hand, we also point out that the superradiant laser, when in the unstable regime, can become an interesting playground for studying chaos. At the mean-field level, there is a direct mapping to the Bénard instability associated with fluid turbulence; however quantum fluctuations associated with photon out-coupling and atom re-filling substantially modify the expected behaviors. Finally, we point-out the existence of a regular self-pulsing regime at large atom numbers.

Resolving the impact of magnetic field instabilities in triggering small scale turbulent flow and the associated rearrangement of the field is of critical importance in understanding multimessenger observables in binary neutron star mergers, and angular momentum transport in neutron stars and accretion disks. Direct simulation of these instabilities are unfeasible, however large-eddy simulations can incorporate the impact of this turbulence with a subgrid model. We present the first machine-learning-based subgrid model for special relativistic magnetohydrodynamics, trained using a neural network. We demonstrate its performance in online simulations of the 3D Kelvin-Helmholtz instability through both a priori and a posteriori tests. Evaluated in a low resolution simulation, our model captures magnetic field amplification of a simulation at 4 times the resolution with a speed-up of a factor 44. This demonstrates the applicability of such methods in general relativistic simulations of neutron star mergers and other scenarios.

The receptivity of the inviscid flow on the stagnation streamline of a blunt body in supersonic flow is investigated theoretically for incoming freestream disturbances. The wave transmission and coupling are quantified by solving the linearized shock-fitting problem with a spectral method, whereas the steady base flow is obtained using a nonlinear shock-fitting spectral solver. Revisiting previous theoretical work, we identify and correct an error in a key coefficient in the analysis by Morkovin (J. Appl. Mech., 27, 1960), overturning the prior conclusion of body-induced damping and revealing amplification instead. The post-shock entropy disturbances display singular behavior near the stagnation point, which is treated analytically. Acoustic disturbances dominate pressure and velocity responses, while density is affected by both acoustic and entropy modes. The base flow pressure gradient introduces weak coupling between the acoustic and entropic components of the response. The actual stagnation-line base flow amplifies all disturbances more than the simplified model of uniform post-shock flow; as well as a shock without a body, and the differences are quantified for a range of Mach numbers. The responses to entropy, fast acoustic, and slow acoustic waves are compared as functions of the freestream Mach number.

Molecular dynamics simulation of plasma-surface interactions requires an interatomic potential that is simultaneously accurate, computationally efficient, and able to describe many elements and bonding types in reactive systems. In principle, a foundation model for machine-learned interatomic potential (MLIP) can meet these demands. We explore the use of the Universal Models for Atoms (UMA) model, developed by Meta FAIR, for the interactions of oxygen plasma species on a multilayer of WS$_2$, a promising 2D material. Starting from the pretrained uma-s-1p1 model under the Open Catalyst 2020 (OC20) task, we apply an iterative fine-tuning loop. Even in the absence of fine-tuning, the pretrained model reproduces the production-scale observables of interest, namely, chemisorbed S and O coverage under 15 eV O$^+$ and O$_2^+$ bombardment. These results were obtained without spin polarization and Hubbard $U$ correction. Nonetheless, fine-tuning with spin polarization and a Hubbard $U$ correction reduces the energy and force mean absolute error (MAE) to $4.5\times10^{-3}$ eV/atom and $0.076$ eV/angstrom, respectively.

Spontaneous breaking of time-translation symmetry and the emergence of self-sustained oscillations in quantum driven-dissipative systems is the hallmark of continuous time crystals (CTCs). An outstanding challenge is to achieve precise, coherent control of such dynamical phases, whose complex nonlinear dynamics makes them inherently difficult to manipulate. Here, we experimentally demonstrate coherent control of a solid-state CTC realized in a non-resonantly excited spin-polarized exciton-polariton condensate in a Ga(Al)As microcavity. We exploit two complementary control channels: an additional weak control laser and the optomechanical interaction with confined GHz phonons. By tuning the control laser energy and power, we stabilize distinct dynamical regimes, including frequency pushing, injection locking with continuous tuning of the limit-cycle frequency, and full suppression of the autonomous dynamics via phase locking. Under appropriate detuning conditions, the control excitation generates coherent mechanical self-oscillation through the polariton–phonon deformation-potential interaction, evidenced by spectral sidebands. The resulting dynamical back-action provides a phonon-mediated locking channel that fixes the frequency of the CTC. Together, the two channels dramatically enhance the temporal coherence of the GHz limit-cycle dynamics, as established through linewidth narrowing and time-resolved first-order correlation measurements $\gone(\tau)$. Our work establishes a way to harness the unique aspects of CTCs for practical applications in the GHz-range.

Deep unrolled networks (DUNs) integrate physical forward models with learned regularization in cascaded network architectures, achieving exceptional performance in inverse problems while maintaining interpretability. While most DUNs operate in the object domain (e.g., image space), recent variants explored representation spaces for improved information flow. However, these methods rely on heuristic methods for data consistency (DC), sacrificing fidelity with measurements. In this work, we introduce DUNE (Deep Unrolled Networks in rEpresentation space), a framework that maintains exact adherence to physical measurements while operating in learned representation spaces. By deriving the DC gradient via the chain rule and implementing it through the Vector-Jacobian Product (VJP), we enable exact backpropagation of measurement residuals into the representation space. This formulation supports diverse architectural backbones, including pre-trained encoders to guide the iterative process. We assess DUNE against state-of-the-art baselines on accelerated MRI reconstruction tasks, demonstrating that exact VJP-based gradients yield superior reconstruction quality and structural fidelity across both single-channel portable low-field and multi-channel clinical high-field MRI acquisitions. The code will be available upon publication at https://github.com/EfeIlicak/DUNE.

Modern cosmology is often taken to provide an increasingly accurate description of the universe's underlying ontology through progressively refined mathematical models. I challenge this interpretation by arguing that the empirical success of cosmology underdetermines not only ontology but also the mathematical and conceptual frameworks used to represent observational data. I propose instead that the objective content of cosmology is best identified with cross-representational informational invariants-features of observational structure that persist across empirically adequate descriptions. These invariants can be characterized in information-theoretic terms, including correlation structure, statistical distinguishability, and limits on accessible information, and formalized using tools such as the Fisher-Rao metric on model space. On this view, cosmological models are best understood as efficient encodings of observational structure rather than uniquely privileged descriptions of fundamental reality. Scientific progress, accordingly, consists not in convergence toward a fixed ontology, but in the progressive refinement of the informational structure accessible to observation.

The viscoelastic instability of cross-slot flow was first observed experimentally almost half a century ago and reproduced numerically two decades ago, yet its physical origin remains unresolved. We revisit this problem for two-dimensional creeping flow of Oldroyd-B fluid by combining direct numerical simulations, global stability analysis, structural sensitivity analysis, and energy-budget analysis. Our simulations reproduce the canonical pitchfork bifurcation, and the stability analysis consistently predicts the threshold and perturbation growth rates. The leading eigenmode consists of a chiral velocity–stress perturbation that tilts and rotates the birefringent strand generated by the extensional flow. Structural sensitivity and energy-budget analyses identify narrow high-extension-rate ridges within the extensional flow as both the spatial core and energetic source of the instability. In these ridges, the stress-based wavemaker co-localizes with large positive disturbance polymeric stress power density, indicating localized transfer of stored elastic energy to the disturbance flow. Analyses of cross-slot variants with rounded corners and with a centered cylinder further reveal that neither sharp corners nor a free central stagnation point is the essential destabilizing ingredient; rather, the instability originates from elastic-energy release in extension-dominated regions characteristic of cross-slot flow.

Altermagnets host momentum-dependent spin splitting without net magnetization, a symmetry-enforced band phenomenon whose photonic analogues have so far been realized only in square lattices governed by fourfold rotation. Here we introduce a photonic altermagnet on a hexagonal lattice whose helicity splitting is governed by mirror rather than rotational symmetry. Elliptical chiral elements of alternating handedness, placed at the vertices of a regular hexagon, leave the two opposite-chirality sublattices connected only by chirality reversal combined with a mirror reflection. Full-wave simulations reveal mirror-related splitting of the two opposite-helicity branches in the band structure and isofrequency contours, with the channels exchanged when the ellipse orientation is reversed. Using a finite photonic crystal slab, we show that such splitting separates a linearly polarized beam into handedness-resolved channels, thus enabling beam splitting and direction-selective helicity filtering with target-helicity output fractions above 0.85 and output paths continuously tunable through the ellipse rotation angle. These results extend photonic altermagnetism to a previously unexplored lattice-symmetry class and establish mirror-symmetric chiral textures as building blocks for altermagnetism-inspired on-chip chiral photonics.

Precise temporal delay generation is a key requirement for asymmetric Mach-Zehnder interferometers (aMZIs) used in high-speed quantum key distribution (QKD) receivers. In this work, a compact integrated aMZI architecture based on a silicon nitride on silicon dioxide (Si3N4/SiO2) photonic platform is presented. A 3-dB directional coupler enabling accurate 50:50 power splitting at the 1550 nm telecommunication wavelength is designed and optimized. The coupling length is initially estimated from the effective index difference between the even and odd supermodes and subsequently refined using three-dimensional eigenmode expansion (EME) simulations. The optimized structure employs single-mode Si3N4 waveguides with a cross section of 1 um x 0.4 um, providing a group index close to 2 and enabling accurate delay engineering. Spectral analysis demonstrates stable 3-dB power splitting across the C-band with insertion loss below 0.5 dB and negligible power imbalance, indicating high transmission efficiency and structural symmetry. An integrated Si3N4 aMZI delay line providing a 500 ps optical delay, corresponding to a 2 GHz free spectral range (FSR), is further demonstrated. Simulations show nearly constant group delay across the 190-200 THz frequency range with sub-10 ps variation and a smooth, near-linear phase response. These characteristics enable interference visibility above 0.99, corresponding to an estimated quantum bit error rate (QBER) below 0.5 percent for gigahertz-rate time-bin QKD systems. The wideband linear phase behavior also indicates compatibility with wavelength division multiplexed (WDM) QKD architectures. The results confirm that the proposed Si3N4 integrated aMZI provides a low-loss, dispersion-controlled, and spectrally stable delay solution suitable for scalable chip-scale quantum photonic receivers.

For centuries, the intrinsic chaos of unsteady fluid motion has stood as a formidable barrier to long-term forecasting. While machine learning (ML) has recently emerged as a transformative paradigm for predicting flow evolution, it encounters a pervasive yet unexplained "performance wall": an inevitable deterioration in accuracy as the forecast horizon extends. Here, we demonstrate that this deterioration is not a deficiency of model architecture, no matter how state-of-the-art, but a fundamental constraint imposed by the underlying system, which can be understood through turbulence theory established decades ago. In the setting of bluff body flow, a canonical phenomenon for spatiotemporal complexity in fluid mechanics, we reveal a scaling law governing the deterioration of ML predictability, derived from a Kolmogorov-inspired framework and validated through high-fidelity simulations. Our findings establish a closed loop between the predictability ceiling and its interpretation, bridging the gap between transparent physical theories and modern black-box inference. More broadly, this work provides a theoretical compass for constructing trustworthy ML in complex dynamical systems across the physical sciences.

Sustainability transitions unfold through interacting dynamics across social, technological, economic, environmental, and governance dimensions. However, many modelling approaches either isolate subsystems or rely on optimisation-based pathways that do not explicitly represent feedbacks, path dependence, and institutional constraints. This study develops a Sparse Orthogonal Regression Technique (SORT), a data-driven dynamical-systems framework for reconstructing multi-system transformation dynamics from harmonised European indicators. Inspired by Sparse Identification of Nonlinear Dynamical Systems (SINDy), SORT infers parsimonious cross-domain dependencies from observed data rather than specifying a full structural model ex ante. The prototype covers energy, emissions, innovation, digitalisation, resource productivity, environmental stress, policy, finance, wellbeing, and resilience. The compact dynamical system is interpreted as a structural representation of medium-term interactions, not as a forecasting tool. It reproduces differentiated empirical patterns, including steady renewable expansion, nonlinear scaling in transition finance, diffusion-like digitalisation, oscillatory environmental stress, and sustained reduction in ETS-regulated emissions. Forward simulations diverge from simple linear extrapolation where reinforcing feedbacks are present, illustrating the conditional nature of accelerated decarbonisation. By translating inferred dependencies into a feedback structure, the analysis identifies reinforcing decarbonisation sequences alongside balancing ecological constraints. The model contributes to sustainability transitions research by providing an empirically grounded representation of multi-system feedback dynamics that bridges quantitative modelling and transition theory. SORT offers a compact technique for exploratory reconstruction from real-world transition data.

Resonant photoionization in the intense high-frequency regime can exhibit extremely asymmetric Autler-Townes doublets whose origin remains debated. It has been attributed either to interference between perturbative ionization pathways or to a non-perturbative dressing of the continuum. Here we show that these interpretations arise from a common effective Hamiltonian framework, in which dressed-state stabilization is governed by the coherent interplay of resonant and nonresonant pathways. We demonstrate that further simplification, using the strong-field approximation, obscures this mechanism. In contrast, our time-dependent essential-state model, where electrons are rigorously coupled to the continuum, achieves excellent agreement with ab initio simulations of the time-dependent Schrödinger equation for helium.

This work provides an initial demonstration of Focused Coherent Microwave Scattering (F-CMS) diagnostics technique, an extension of the conventional Coherent Microwave Scattering (CMS) technique that enables spatially resolved measurements. The spatial-resolution functionality was achieved using PTFE lenses to focus the probing microwave beam and isolate a distinct probing volume. The feasibility of F-CMS was demonstrated using glow-discharge plasma volume with an electron number density of approximately 5x10$^9$ cm$^{-3}$ as a test object. A clear F-CMS output signal was successfully detected upon the discharge initiation. These findings establish a foundation for the further development of the F-CMS technique, which has the potential to enable highly sensitive, spatiotemporally resolved measurements of electron number density in real time, without the need for signal accumulation (sensitivity down to an electron number density of approximately 10$^8$ cm$^{-3}$ is expected). Such capability will be useful for a variety of applications including diagnostics of unsteady flowfields such as those occurring in hypersonic shock tunnels and spacecraft electric propulsion systems.

It has been shown in non-relativistic tearing theory that shear flows will slow the linear phase of tearing instability and can delay onset of magnetic reconnection. We find using kinetic particle-in-cell simulations that shear flow as well as guide field strength affect the onset time of relativistic magnetic reconnection. To model this we develop a numerical solver for the growth rate of the relativistic linear tearing instability, including effects of the motional electric field which has not previously been done. We find slowing of growth due to both shear flows and guide field, and at higher flow shear, transition through an intermediate regime to linear Kelvin-Helmholtz instability.

In recent years a weak but robust response of mean midlatitude circulation to Arctic amplification (AA) has emerged from modeling experiments. However, open questions remain about the mechanisms linking such circulation differences to weather extremes in the midlatitudes. In this study we investigate such mechanisms and the importance of zonal asymmetries in shaping the atmospheric blocking response to AA. We perform idealized aquaplanet simulations in two configurations: a zonally symmetric setup and a zonally asymmetric experiment featuring a localized midlatitude storm track. For each configuration, we examine the response to AA by imposing an anomalous surface heating in the polar region. In the zonally symmetric configuration atmospheric blocking increases uniformly with AA from mid to high latitudes. In the asymmetric configuration, the response is more complex; instead of a zonally uniform response, we observe an upstream displacement of the blocking maximum, which sits at the exit of the localized storm track. We interpret these changes through the lens of the Traffic Jam theory by diagnosing the carrying capacity of the midlatitude flow. In both configurations, the zonally averaged increase in blocking is primarily driven by a weakening of the zonal winds, which reduces the Doppler-shifted Rossby wave group velocity and, in turn, decreases the flow carrying capacity. While the reduction in carrying capacity has similar characteristics in the two configurations, in the asymmetric case it leads to an upstream shift of blocking frequency as a direct consequence of the threshold behavior of blocking onset that lies at the core of the Traffic Jam theory. This mechanism, which has received limited attention so far, highlights the importance of mean circulation characteristics in shaping the blocking response to external forcing such as Arctic warming.

Water confined within nanoscale capillaries exhibits phase behaviour and transport properties that differ substantially from bulk, and these effects are commonly interpreted as consequences of geometric confinement and reduced dimensionality. Here we show that confinement topology alone is insufficient to predict the behaviour of nanoconfined water. Using machine learning interatomic potentials with first-principles accuracy, we compute the density-temperature phase diagram of water confined within bilayer graphene nanocapillaries and compare AA and AB stacking arrangements, which differ only by a lateral shift of 1.4 Å. Despite this minimal structural change, AA stacking can stabilise different ice polymorphs, can increase the melting temperature by more than 100 K, can enhance proton transfer, and alters the onset of superionic behaviour relative to AB stacking. We trace these effects to stacking-induced changes in the hydrogen-bond network associated with modifications to the lateral free energy landscape and neighbouring O-O separations. Our results demonstrate that even subtle atomistic variations in the confining walls can qualitatively reshape the physical and chemical behaviour of nanoconfined water, with implications for the interpretation and control of fluids under angstrom-scale confinement.

We numerically verify multifractal theory (Frisch and Parisi 1985) for turbulence using simulation data at a high Reynolds number. First, we propose a simple method to directly estimate the multifractal dimension $D(h)$ of vortical structures with a given Hölder exponent $h$. Thus measured $D(h)$ is in good agreement with indirectly measured experimental data. Then, we demonstrate that these structures for $h\ll1/3$ form the hierarchy of coherent eddies, while those for $h\gg1/3$ are featureless.

Objective: Present OpenPINT (Open-source Planning for Isoeffective Nuclear Treatments), an open-source treatment planning system for nuclear therapies that integrates Monte Carlo dose calculations with modular dosimetric and radiobiological models for photon-isoeffective dose evaluation. Approach: We describe the software architecture, implementation choices, and data flow from segmented geometry and source configuration to NIfTI dose outputs. We define BNCT-relevant dosimetric metrics and evaluate the workflow with reproducible analytic and voxelized cylindrical-phantom benchmarks, supplemented by a geometric patient-positioning example. Main results: The module provides a reproducible and scriptable path for generating MCNP-ready inputs, extracting component-wise BNCT dose maps, and computing analysis-ready outputs for quality checks and decision support. Fine-resolution voxelized configurations reproduced the 1 mm analytic reference within 0.13% for the brain-limited irradiation-time endpoint, whereas the full voxelized sweep exposed deviations up to 4.42% in coarse 8–10 mm configurations. Patient-wide gamma pass rates were at least 99.60% for the evaluated mesh/interpolation cases, while low-dose DVH-tail quantities remained sensitive to boundary discretization. Significance: This first paper isolates and validates the simulation-preparation and dosimetric-analysis core of an open-source BNCT treatment-planning platform. It establishes a foundation for subsequent work on optimization, biological weighting, and clinical workflow integration.

Matter can organize not only through forces, but also through the information its constituents acquire from their surroundings. Here we use perceptive Brownian particles as a minimal model to isolate nonlocal sensing as an organizing principle for nonequilibrium matter. The particles undergo purely Brownian motion, with no mechanical interactions, self-propulsion, alignment, or auxiliary fields. Their only coupling is informational, through diffusivity regulated by density measured over a finite perception zone. Whereas local sensing, when unstable, produces conventional long-wavelength demixing, nonlocal perception restructures the instability spectrum, introducing finite-wavelength patterning and nonlinear bubbling instabilities. More fundamentally, it reshapes the ordering pathway by assembling a cascade of instabilities: macroscopic demixing creates dense domains, finite-wavelength modes pattern them internally, and nonlinear feedback hollows them into void bubbles. This produces hybrid phase separation, where a macroscopic dense phase coexists with a dilute background while retaining ordered internal microstructure, whose symmetry, anisotropy, and length scales are selected by the perception kernel. These results establish information acquisition as a constitutive principle of nonequilibrium matter, capable of governing both phase stability and the dynamical pathways through which order emerges.

Photomultiplier tubes (PMTs) are widely used in neutrino experiments. As a new-generation neutrino observatory, JUNO requires an excellent energy resolution of 3% at 1 MeV. This will be realized with a 20 kton liquid scintillator detector instrumented with more than 20000 20-inch PMTs and 25600 3-inch PMTs. These PMTs were successfully installed in JUNO from October 2022 to December 2024. During the installation, seven test campaigns were performed to validate the PMT functionality, including measurements dark count rate, gain, waveform, and charge spectrum. In this paper, we present the implementation of these tests and the corresponding results for the 20-inch PMTs throughout the installation process.