Papers

The performance of two machine learning (ML) atmosphere models - GraphCast and FourCastNetV2 - is evaluated in the context of sub-seasonal prediction, including their ability to represent key climate drivers of variability, namely the Madden-Julian Oscillation and the Southern Annular Mode. Model skill is assessed over both a 38-year hindcast period and a 2.5-year hindcast period. The longer period overlaps with the training windows of the ML models but provides a larger sample for robust evaluation, while the shorter period is independent of the ML model training period. This dual evaluation illustrates a compromise approach to the problem of insufficient independent data for evaluation of the models for sub-seasonal prediction. The ML models are compared against the Bureau of Meteorology's physics-based seasonal prediction system, ACCESS-S2, for the 38-year period, and a more recent physics-based coupled model for the shorter hindcast period. Across the two evaluation periods, both ML models have surprisingly good skill for sub-seasonal timescales, given they were designed for forecasting on medium range timescales. In general, the ML models are as skilful as the physical model ensemble mean at shorter lead times and comparable to the physical model ensemble members at longer lead times.

NeuXtalViz (Neutron Single-Crystal Visualization) is a Python-based software package developed at Oak Ridge National Laboratory to provide interactive three-dimensional visualization and analysis tools for single-crystal neutron diffraction experiments. Built on the Mantid framework for data reduction, and leveraging PyVista and Matplotlib within a Python Qt environment, NeuXtalViz adopts a model-view-presenter architecture that separates the user interface from the core processing components. The software provides a unified interface for tasks central to single-crystal diffraction, including UB-matrix determination, experiment planning, visualization of normalized reciprocal-space volumes, and real-space crystal-structure calculations. It also integrates with widely used community tools and has been deployed on instrument and analysis servers, where it is now being adopted by instrument teams and users. By embedding advanced three-dimensional visualization directly into the experimental workflow, NeuXtalViz enhances the planning, execution, and analysis cycle for single-crystal neutron diffraction experiments, while providing a flexible framework for future development.

Estimating past events in wall turbulence based solely on surface measurements and first principles is an ill-posed problem that is complicated by chaos. The sensitivity of a measurement to the earlier flow state is described by the adjoint Navier-Stokes equations, which are solved in reverse time starting from the measurement kernel at the sensing position and time. The resulting adjoint field is the spatio-temporal domain of dependence (DOD) of the sensor, which is a dual to the concept of the domain of influence (DOI) of an actuator in the linearized forward equations. In channel turbulence, the energy of each adjoint realization grows exponentially in backward time according to the Lyapunov exponent, even though the energy of the ensemble average should decay. We introduce a linear eddy-viscosity closure model in the ensemble-averaged adjoint equations, and directly compute the mean DOD and compare our prediction to the ensemble average. Furthermore, we demonstrate that the DOD of a wall-stress measurement and the DOI resulting from a wall-stress perturbation exhibit respective universal behaviors across Reynolds numbers. However, their spatio-temporal structures differ qualitatively, due to the time-asymmetry of the governing equations. The DOD field has a two-part structure: one component is associated with the Orr mechanism, characterized by rapid reorientation under mean shear, and the other is related to self-similar expanding streaky structures. These two components jointly define the sensitivity of the wall-stress measurement to past flow events.

Determining the quantum state of a given quantum-mechanical system is a fundamental task in physics. Quantum-state tomography has been pivotal for establishing quantum optics [1-4] and for revealing the properties of bound charges in materials [5-7]. An emerging other object for studying and utilizing quantum effects are free electrons, elementary particles that are central to high-resolution microscopy [8,9], electron-based quantum optics [10-17], ul-trafast electron microscopy [18-24] and particle accelerators [25-27]. However, free electrons are intrinsically incoherent, and we lack a broadly applicable method to measure and control their quantum state beyond special cases with discrete energy sidebands [28,29]. Here, we report a universal approach to measure arbitrary free-electron quantum states in continuous variables. Two monochromatic but spectrally shifted laser waves produce interfering quan-tum paths that directly reveal the density matrix and thus all essential properties of the pure wavepackets, the ensemble, and their interlinks. As a first application, we show how the quantum state of a single electron is modified by many-body Coulomb interactions of a sur-rounding electron gas. The reported concepts and results provide insight into otherwise hid-den correlations in electron beams and enable the controlled optimization of exceptional quantum states for free-electron quantum optics or quantum electron microscopy.

Quantitative imaging of stress fields in heterogeneous solids remains challenging because stress is not directly measurable and is typically inferred from deformation using constitutive models. Here we present Acoustoelastic Imaging (AEI), a non-destructive framework for reconstructing stress fields from shear wave propagation. AEI exploits the acoustoelastic effect, whereby pre-existing stress modifies local wave dynamics, and formulates stress recovery as an inverse problem of the governing wave equations. Using full shear waveform inversion with physics-informed learning, AEI reconstructs wave-equation coefficients from full-field wave measurements, enabling estimation of stress magnitude and principal directions without explicit constitutive model specification or material-parameter calibration. We demonstrate sub-wavelength spatial resolution (< 0.28 {\lambda}) and accurate reconstruction of nonuniform stress fields in heterogeneous materials through numerical simulations and ultrasound shear wave elastography experiments. These results establish a general framework for high-resolution stress imaging and provide a route toward non-invasive mapping of internal mechanical states in complex materials and biological tissues.

We demonstrate wide-field optically detected magnetic resonance (ODMR) under simultaneous high-pressure and high-temperature conditions using nitrogen-vacancy (NV) centers. Although NV-center magnetometry has been widely used for spatially resolved magnetic-field imaging, its application to extreme environments combining pressure and temperature remains challenging. In this work, we show that ODMR can be observed at 5 GPa and 500 K, demonstrating the feasibility of NV spin readout under such combined extreme conditions. We further perform wide-field ODMR of iron at 7 GPa and 500 K, where the stray magnetic field from the sample is spatially visualized through the pressure cell. These results establish NV-center magnetometry as a promising platform for imaging magnetic phenomena in materials under high-pressure and high-temperature environments.

A near-wall cavitation bubble is generally expected to acquire a wallward Kelvin-impulse bias and to rebound or jet toward the wall. Here we show that this canonical direction can be reversed by a wall-supported free sphere. High-speed imaging reveals a transition from away-from-wall to wallward rebound as the initial bubble–sphere separation is increased. By reconstructing the Kelvin impulse on a closed bubble boundary that includes both the visible free interface and the bubble-side contact closure, we find that the reversal is not governed primarily by the instantaneous velocity of the sphere. Instead, sphere displacement creates a contact closure on which the bubble-source contribution supplies an away-from-wall impulse. This contact-source impulse competes with a wallward background formed by the wall-image source and the quadrupolar component of the sphere-induced field. The resulting balance yields a calibrated geometric criterion, $\mathcal{M}_K$, and, in the comparable-size bubble–sphere regime, reduces to a contact number $a_z z_b/R_K^2$. These results identify a contact-geometric mechanism by which a movable particle can redirect the first-cycle jet and rebound bias of a near-wall cavitation bubble.

We developed a nanosecond-resolved 266 nm Mach-Zehnder interferometer for electron-density measurements of dense laser-produced plasmas generated in 100-bar supercritical-fluid (SCF) helium. A 1064 nm pump pulse was focused into the SCF helium medium, and the plasma-induced phase shift of a 266 nm UV probe beam was recorded using an ICCD-based interferometric imaging system. Plasma-arm-only and reference-arm-only images were used to normalize raw interferograms and improve the effective fringe visibility. The corrected interferograms were analyzed using a two-dimensional Fourier-transform method to reconstruct phase-shift maps, which were converted into line-integrated electron-density distributions through the plasma dispersion relation. Assuming cylindrical symmetry, Abel inversion was applied to the plasma with the largest line-integrated electron density, yielding a maximum local electron density of approximately \(2.5\times10^{18}~\mathrm{cm^{-3}}\). The measurement fidelity was evaluated by considering free-free absorption of the probe beam, probe-beam refraction by plasma electron density gradients, and effect of finite-collision-frequency effects on the plasma dielectric response. These estimates indicate that the inferred electron density is not altered by more than an order of magnitude under the present experimental conditions. The present system demonstrates the applicability of 266 nm UV interferometry to nanosecond-resolved density diagnostics of dense plasmas in high-pressure supercritical fluids.

Physics informed neural networks (PINNs) incorporate Maxwell's equations, initial conditions, boundary conditions, and measurement data directly into the learning process, transforming the solution of partial differential equations into a constrained optimization problem. As such, PINNs are attracting increased attention in computational electromagnetics as an alternative to traditional time-domain solvers. This paper presents a PINN formulation for the time-domain Maxwell's equations incorporating split-field perfectly matched layers (PMLs). A key advantage of the split-field PML formulation is that the same governing equations can be applied in both the physical and PML regions, simplifying the PINN formulation and loss construction. The proposed approach is validated using one-dimensional and two-dimensional Gaussian pulse problems with PML. The PINN solutions show good agreement with analytical and finite-difference time-domain (FDTD) reference solutions, demonstrating the feasibility of combining PINNs with split-field PMLs for open-domain time-domain electromagnetic simulations.

We prove a residual-work compatibility theory for positive-cone viscoelastic Reynolds states. In Oldroyd–B, the entropy cancellation eliminates incompressible transport and upper-convected stretching against polymeric stress work. After quotienting pressure tensors and spatial means, positive pressure-free residual work can be paid only by the conformation residual through the entropy-dual lever (G=I-A^{-1}). This yields the closed residual-work cone (P+(\alpha/2)\int_{\mathbb T^d}G:S,dx\le 0), exact windowed tests, and the least-cost Hilbert-space repair; constrained closures pay through the projected lever. Strong residual-data limits preserve this cone, while weak–weak limits require the product-defect measure carried by (G:S). The augmented topology is sharp, as shown by localized residual packets. For FENE-P, the lever (G_b(C)=((b-d)/(b-\operatorname{tr}C))I-C^{-1}) makes the finite-extensibility boundary a genuine residual-work boundary.

We analyze the volume distribution of kofun, large mounded tombs constructed in the Japanese archipelago mainly from the mid-third through the seventh century CE. We ask whether the distribution of collectively mobilized resources in an ancient society without a monetary economy or written administration could follow Zipf's law, as observed in modern firm sales. Here, Zipf's law refers to a power-law distribution with an exponent close to one. Using a nationwide database, we estimate kofun volumes as a proxy for labor and resources mobilized by the constructing groups, and the volume distributions archipelago-wide and by region, period, and mound type. We find that the volume distribution of keyhole-shaped kofun exhibits a Zipf-like upper tail with a cumulative power-law exponent close to unity, while its central part is close to log-normal—a shape that resembles the distribution of modern firm sales. Moreover, many regional and temporal differences appear primarily as scale differences: after median normalization, most distributions collapse onto a common curve and remain approximately Zipf-like. However, some exceptional groups exist—such as the politically central Kinki region—that show heavier-than-Zipf tails (cumulative exponents below unity), indicating stronger concentration among the largest kofun. To interpret these regularities, we introduce a Kesten-type stochastic growth model with stopping and reorganization. The model provides a unified account in which the log-normal-like body, Zipf-like tail, and regional/temporal variations arise from a common growth process. Together, these findings raise the possibility that collectively mobilized resources exhibited a Zipf-like structure already in the Kofun period.

The geostationary orbit (GEO) carries most of the world's satellite communications revenue. The International Telecommunication Union (ITU) coordinates and records frequency assignments and associated orbital positions through national administrations, without creating a market price or property title for longitude. We ask how commercial operators locate along GEO within this administrative regime. Using a spatial sorting model, we show that commercial satellites are placed in proportion to the reachable income below a GEO longitude, discounted by viewing angle at a spatial discount rate. We estimate a placement elasticity close to one, so that satellite counts above a longitude rise proportionately with reachable income. Government satellites, by contrast, are placed in proportion to the population a GEO longitude covers. We validate the commercial model by showing that parameters estimated on 2012 placements predict the 2021 distribution well and that the model rejects treating Chinese GDP as fully addressable to the international commercial fleet. Counterfactuals suggest that competition from low-Earth orbit (LEO) systems may tilt GEO usage toward Asia-Pacific, while higher spatial discounting may concentrate it over high-GDP countries.

Photocatalytic CO2 conversion driven by solar energy is a highly promising approach in addressing rising atmospheric CO2 levels; however, its practical application remains limited by low conversion efficiency. In this study, a new strategy to enhance CO2 reduction toward CO and CH4 is proposed through the employment of microalgae as a sacrificial agent, and the efficiency is compared with conventional CO2 conversion without and with the use of microplastics as sacrificial agents. To realize this strategy, an AB2O6-type high-entropy oxide (HEO), (Cs1/7Ba4/7Bi2/7)(Nb1/2Ta1/2)2O6, with bi-polymorphy of layered perovskite and pyrochlore, is rationally designed. The HEO incorporates alkali metal cesium and alkaline earth metal barium to increase surface basicity for CO2 chemisorption, bismuth with its stereochemically active lone pairs for localized polarization and charge separation, and tantalum and niobium to form octahedral crystalline frameworks for charge transport. The utilization of microalgae during photocatalytic reactions leads to a remarkable enhancement in CO2 conversion compared to catalysis with or without using microplastics, with CO and CH4 production increasing by 10- and 4-fold, respectively, compared to the system using only HEO. These findings not only demonstrate a new family of polymorphic AB2-type HEOs for photocatalysis but also show the potential of microalgae as a sustainable sacrificial agent, offering an environmentally friendly pathway for efficient CO2 capture (through photosynthesis by microalgae) and CO2 conversion (through photocatalysis by HEOs).

The growth of artificial-intelligence workloads is increasing the electrical and thermal demands on data-center power-delivery systems, making conversion efficiency, power density, and reliability critical design priorities. This review examines how gallium-nitride (GaN) power devices can be matched to specific stages of the grid-to-load conversion chain, including power-factor correction, isolated DC/DC conversion, 48-V intermediate-bus conversion, and point-of-load regulation. Si, SiC, and GaN are compared using converter-relevant metrics, and lateral, vertical, and specialized GaN architectures are evaluated in terms of voltage scalability, switching behavior, reverse conduction, thermal pathways, gate control, and technology maturity. The analysis shows that GaN provides a stage-dependent rather than universal advantage. Commercial lateral GaN HEMTs are particularly effective in high-frequency, low-to-mid-voltage stages, while specialized and hybrid devices support bidirectional operation, normally-off control, extreme conversion ratios, and integration. Vertical GaN remains an emerging option for higher-voltage and higher-power conversion. A quantitative framework links cascaded converter efficiency to electrical-loss reduction, cooling demand, annual facility energy use, and operational carbon emissions. Broad deployment further requires low-parasitic packaging, disciplined gate-drive and EMI co-design, mission-profile reliability qualification, scalable manufacturing, and supply-chain resilience. GaN is therefore best treated as a stage-specific system lever whose value depends on coordinated device, topology, package, and thermal co-design.

Digital rock physics (DRP) aims to estimate effective rock properties (e.g., elastic moduli) directly from 3D micro-CT images. However, direct numerical simulations (DNS) on high-resolution large 3D scans are often computationally prohibitive and severely limit the application of DRP. To address this bottleneck, we combine a lightweight 3D convolutional neural network (CNN) with hierarchical homogenization (HHM) and apply it to determine effective elastic moduli. In this scheme, a large rock image is divided into subcubes. The CNN replaces costly DNS by directly predicting subcube elastic moduli, while HHM upscales subcube-level predictions to the full rock. Using a shared convolutional backbone, we systematically compare three training targets: (i) full anisotropic $6\times6$ stiffness tensors, (ii) isotropic bulk and shear moduli $(K, G)$, and (iii) Hashin–Shtrikman (HS)-normalized factors. Across multiple rock types, all three models agree well with DNS results while substantially reducing the computational cost. Moreover, training from scratch on each rock type is fast enough that transfer learning is unnecessary. Across all three targets, the accuracy is comparable. In our comparative study, the HS-normalized factor offers the best overall speed–accuracy trade-off while guaranteeing physical consistency, making it a convenient default. The isotropic $(K, G)$ target is a slightly more accurate alternative.

Rapid and robust laser-frequency auto-locking is essential for the field deployment of quantum communications, quantum computing, and precision-measurement technologies; however, achieving this remains a considerable challenge. Here, we propose and demonstrate an auto-locking scheme employing Bayesian optimization and discrete biorthogonal wavelet transformation. First, the reference is rapidly sought by making intelligent use of historical observations, eliminating the inherent blindness of the traditional parameter-scanning method. Second, the frequency reference is robustly identified by pinpointing transition signals with the discrete biorthogonal wavelet transformation and analyzing their immutable frequency differences and relative magnitudes, which are determined by the inherent atomic structure and remain resistant to environmental disturbances. This proposed approach achieves a fivefold acceleration in reference searching compared to conventional scanning methods in the case where the laser frequency drifts far away from the reference. Crucially, it achieves an identification accuracy of more than 99.5 %, even under severe 50 % laser-intensity fluctuations, $9.95^\circ$ photodiode misalignment, and $18^\circ$C Rb cell temperature elevation. Finally, locking the laser frequency to the identified reference with a lead zirconate titanate-current double-servo loop narrows the linewidth to 20 kHz. We believe that this rapid, robust, and high-performance auto-locking technique will be pivotal towards the deployment of the next generation of practical quantum technologies in demanding field environments.

The consideration of diversity, equity and inclusivity (DEI) is an important part of promoting a robust and respectful workforce, and is critical to the continued success of organisations, including healthcare providers, academic institutions and professional societies. Many professional bodies representing medical physicists have made commitments to DEI principles in the form of mission statements, policies, steering groups, frameworks and workforce surveys. In the Australian and New Zealand medical physics community, DEI work has included reflecting on the impact of stereotypes, surveys on workforce experiences, and capturing workforce diversity metrics (including gender, nationality, age and professional background). These projects have been conducted with the support of the Australasian College of Physical Sciences and Engineering in Medicine (ACPSEM) and have contributed to the enhancement of DEI in the ACPSEM workforce. Most of this work has been focused on gender diversity, reflecting increasing involvement in the Australian and New Zealand workforce: women accounted for 41% of medical physics trainees and 32% of registered medical physicists in a 2020 survey. In 2021, the ACPSEM Professional Standards Board (PSB) incepted the creation of a DEI working group. This movement was supported not only by the ACPSEM Board but also the wider medical physics community, both nationally and inter-nationally. The establishment of this group will support best-practice governance, define the diversity metrics most relevant to our profession and use data collected on these metrics to effect positive change within the ACPSEM boards and committees as well as embedding DEI in policies and procedures for internal and external activities. The DEI working group will enhance the ACPSEM's reputation and ultimately ensure all members feel empowered, respected and represented.

This paper presents a neural surrogate approach for improving the accuracy of natural convection problems simulated with a Boussinesq flow model (incompressible flow with heat transfer). Our approach, based on Fourier neural operators, uses training data consisting of matched pairs of simulations run under the computationally cheaper yet less accurate Boussinesq flow model and a more computationally expensive and more accurate compressible flow model. In both cases, we implement our parallelized simulation codes based on an implicit monolithic mixed finite element method (FEM) approach using the open-source FEniCSx framework. Our implementations are validated against a commercial software package, COMSOL, as well as standard test problems from the literature. We include a careful discussion and analysis of data set generation and present learning results in two and three spatial dimensions. Using compressible flow results as high-fidelity reference solutions, our learning approach, with a single model evaluation per simulation, substantially improves the per-channel accuracy of Boussinesq predictions, with structural similarity (SSIM) close to unity across all flow variables and test distributions and corresponding mean-squared error reductions of one to nearly three orders of magnitude. All code and data is released as open-source.

We prove endpoint continuation criteria for stress-diffusion-free incompressible viscoelastic flows by working modulo pressure. In two space dimensions, the pressure-free part of any smooth spectral isotropic stress reduces to a single active deviatoric channel (q_1(a,|Y|^2)Y), where (C=aI+Y) and (\operatorname{tr}Y=0). This scalar quotient structure allows a weighted active-deviatoric energy to cancel the top-order coupling between polymer stretching and the divergence of the active stress. On compact conformation windows the resulting high-order estimate depends only on an endpoint velocity clock and a logarithmic conformation norm. For Oldroyd–B this gives continuation of strong two-dimensional solutions under (\nabla u\in L^1_tB^0_{\infty,1}), while for FENE-P it gives continuation under (\nabla u\in L^2_tB^0_{\infty,1}). In both models the compact conformation window and logarithmic bound are derived from the velocity clock and the model barriers, rather than imposed as independent hypotheses. The criteria are formulated in integer Sobolev strong-solution classes and do not assert Leray-type weak-solution or critical-space local well-posedness results. We also identify a static operator obstruction showing the functional necessity of the logarithmic threshold for the pressure-free stress map. In three dimensions the quotient contains an additional residual channel (q_2(Y^2)^\circ), so the exact scalar closure is intrinsically two-dimensional. On prescribed compact windows this residual can be absorbed by viscosity; for Oldroyd–B and FENE-P it vanishes because (q_2\equiv0).

Skin friction in wall-bounded turbulence is produced by intermittent near-wall motions, yet conventional coherent-structure definitions do not identify which individual events generate wall-shear stress, and thus friction. We train neural networks to predict the future velocity field and wall-shear-stress distribution in turbulent channel flow, and use SHAP-derived importance maps to identify the the input regions most influential for each prediction. The dominant events appear as paired objects: an upstream velocity-relevant region is associated with high-momentum motion toward the wall, while a downstream friction-relevant region marks the enhanced wall-shear-stress left in its wake. Tracking these pairs reveals a recurrent cycle of growth, streamwise elongation, decay, and occasional splitting into new wall-shear-producing events. These results redefine near-wall coherent structures not by what the flow looks like, but by what they do to wall-shear stress.

Autonomous microrobots navigating biological vasculature could enable targeted drug delivery and thrombolysis, yet training control policies for realistic environments remains an open challenge. Prior reinforcement learning (RL) studies of microrobotic navigation have been limited to idealized geometries that omit complex hydrodynamic flow fields, confined branching structures, and dense cellular obstacles found in vivo. Here, we develop a physically grounded simulation of a blood capillary network, incorporating realistic hydrodynamic flow fields, explicit red blood cell dynamics, and anatomically derived branching geometry, and train deep RL agents to navigate it via chemotaxis. We systematically map the physical limits of navigation across robot size and swimming speed, revealing a forbidden regime where Brownian motion and flow overcome propulsion. Successful agents independently discover multiple universal strategy types, including run-and-rotate and energy-efficient search-and-sit policies, regardless of robot parameters. Without retraining, these agents perform targeted blocking and unblocking of capillary flow, restoring throughput to healthy baseline levels. These results establish RL as a viable framework for developing autonomous microrobotic intervention strategies in complex biological environments.

Chemical potentials are among the most important properties that can be obtained from a molecular simulation since they define many technologically relevant collective properties. The chemical potential of a species in solution is obtained by computing the free energy change of adding that species into a bulk system, a calculation typically very expensive for systems such as electrolytes, due to the lack of phase space overlap between "not-inserted" and "inserted" states. Recently, normalizing flows have been introduced as a way to accelerate free energy computations by learning a bijective function, constructed to be as expressive as possible, that maps the configuration space of one Boltzmann distribution onto another. This expressivity makes them difficult to train, limiting their ability to be generated "on-the-fly" for any new system, and in practice these mappings have shown only modest sampling improvements for liquids. We address these issues by introducing a "minimal" normalizing flow (MNF). This is a trainable bijective mapping that is intentionally limited in expressivity, and instead applies low-dimensional, physically informed transformations. Useful MNFs can be trained in 1 minute of GPU time due to their simplicity and our introduction of a novel training strategy. We show how calculations of chemical potentials of Lennard-Jones particle systems can be accelerated by at least 10 times with a simple radial mapping. We also apply a radial and orientational mapping to ion solvation in water, showing that MNFs can increase the effective sample size by 3 times for charging free energy calculations and 8 times for calculating free energy changes due to force field perturbations. This provides the foundation for the development of physically-informed mappings that can accelerate complex free energy calculations while retaining low training costs.

Inverse design of nanophotonic structures is challenging due to the large design space, nonlinear structure-response relationships, and the high computational cost of iterative electromagnetic simulations. Existing deep-learning approaches typically rely on large precomputed datasets or libraries of optimized structures, which limits scalability to continuous and complex inverse-design tasks. We introduce a Dimension Expansion Network (DEN), a fully unsupervised, simulation-efficient framework for nanophotonic inverse design. DEN addresses the mismatch between low-dimensional design objectives and high-dimensional nanophotonic structures by transforming compact target parameters into structured, high-dimensional conditioning representations before inverse design. This improves target expressivity and conditioning quality for structure generation. The model is trained end-to-end using differentiable electromagnetic simulations, removing the need for any pre-generated dataset. We validate DEN on free-form metalens and asymmetric Y-splitter design problems. For metalens design, DEN achieves focal intensities comparable to adjoint-based optimization while reducing simulation cost by approximately 50% and generalizing across tens to thousands of focal targets within a shared focal region. For Y-splitter design, DEN accurately produces arbitrary power-splitting ratios using only 21 training targets and demonstrates robust broadband performance. Ablation studies and representation analyses show that dimension expansion enhances sensitivity to target variations, increases structural diversity, and reduces mode-collapse-like behavior. Overall, DEN provides a scalable conditioning strategy for inverse design with low-dimensional objectives, enabling efficient photonic design across large continuous target spaces.

Optical 3D scanning systems allow the acquisition of accurate models of patient anatomy, suitable for use in the design of simple 3D-printable patient-matched medical devices with 3D modelling software. This study developed and demonstrated the use of superficial brachytherapy surface mold design workflow that utilizes data from optical 3D surface scanning and enables a commercial brachytherapy treatment planning system to be used for catheter positioning and dose optimization steps. Synthetic CT images were generated from 14 optically scanned anatomical models of human participants. Models and skin textures ac-quired from the optical scans were imported into Autodesk Meshmixer, where the treatment area was delineated, and treatment and device volumes produced. 3D Slicer was used to convert the body, treatment and device volumes to DICOM CT and RTSTRUCT data. The synthetic CT data and contoured volumes were imported into Varian Eclipse, where catheters were designed, and dwell positions and times optimised for dose coverage of the treatment volume. The lack of in-ternal anatomy did not compromise dose calculations, due to clinical use of a TG43 based algorithm. Once 3D printed, molds can be imaged in-situ during CT simulation, and reconstructed, for clinical dose calculation and plan approval.

We present a scalable time-based molecular dynamics (TBMD) framework for simulating single-bubble sonoluminescence within a hybrid continuum-MD formulation. Unlike prior event-based approaches, which model gas dynamics through instantaneous hard-sphere collisions, the present method integrates continuous Lennard-Jones and damped shifted force Coulomb interactions at each timestep, enabling self-consistent tracking of ionization state and long-range electrostatics throughout the collapse. To bridge the gap between the physical particle count ($N_\mathrm{real}\sim 10^{10}$) and computationally tractable ensemble sizes, we introduce an ensemble particle (EP) scaling formalism that preserves temperature, pressure, and ionization statistics while reducing the simulated particle count by up to four orders of magnitude. Applying the framework to argon under standard single-bubble sonoluminescence driving conditions, we perform a systematic sweep over the ionization model and thermal accommodation coefficient $\alpha_t$, with ensemble sizes up to $N_\mathrm{ensem} = 10^8$ particles. The results establish that ionization is the dominant regulator of peak temperature, reducing $T_\mathrm{max}$ by approximately a factor of two relative to the non-ionizing baseline, while $\alpha_t$ primarily controls the spatially averaged temperature at the collapse minimum. Scalar observables at $N_\mathrm{ensem} = 10^8$, including peak temperature, minimum bubble radius, and maximum wall velocity, are assessed against prior studies to help validate the EP scaling formalism and our hybrid continuum-MD framework.

A new field solver has been implemented in the global electromagnetic total-$f$ gyrokinetic particle-in-cell code XGC to extend the code's capability to large-scale reduced MHD-type instabilities in tokamak plasma. While XGC's regular field solver is accurate at typical microturbulence scales of the order of the ion Larmor radius in tokamaks with arbitrary aspect ratio, a more accurate field solver is required for large-scale (i.e., low toroidal mode number) MHD-type modes such as internal kink, tearing and peeling modes. The higher accuracy of the new field solver is achieved by dropping the (large aspect ratio) assumption that the poloidal magnetic field is much smaller than the toroidal magnetic field, while its numerical complexity is controlled by using a spectral discretization in the toroidal direction. To cover the entire spectrum from large-scale MHD-type modes to small-scale microturbulence, the regular and the new field solver can be run alongside each other. This work details the derivation of the new field solver, analyzes the differences between the XGC's regular and new field solvers, and verifies the new field solver against analytic predictions and the gyrokinetic code ORB5 and the MHD code NIMROD.

As head-and-neck radiotherapy treatments grow more complex and precise, it becomes increasingly important to assess the anatomical separations that can be achieved using intra-oral stents. A series of twenty T2-weighted turbo spin echo magnetic resonance images (MRI) were acquired of one healthy participant, with a range of different wax and 3D printed intra-oral stents in situ. The resulting measurements showed that a 3D printed modular stent containing hard polylactic acid (PLA) and flexible thermoplastic polyurethane (TPU) components made the largest and most reproducible separation between the cheeks (70.8 +/- 0.3 mm), two hard PLA stents designed to exactly fit the participant's teeth produced the poorest positioning reproducibility (standard deviations of up to 3 mm between a range of landmarks measured in repeated images). Most stents were described as “comfortable” although the wax stents left small pieces of wax attached to the teeth after use. This MRI based comparison demonstrated that the materials and designs used for intra-oral stents can have substantial effects on the level of anatomical separation and positioning reproducibility that they produce.

Combinatorial optimization problems pose substantial computational challenges because their feasible solution spaces grow exponentially with problem size. This paper presents a GPU-accelerated augmented Population Annealing Monte Carlo (PAMC) framework for large-scale graph-partitioning problems, with emphasis on Max-Cut and Max-K-Cut. The proposed framework extends conventional PAMC by coupling population-based resampling with two stagnation-driven mechanisms: adaptive temperature control and energy-preserving nonlocal cluster moves. By using population-level optimization history as feedback, these mechanisms regulate the balance between exploration and refinement by reheating stalled populations and enabling collective transitions across locally confined regions of the solution space. Experiments on G-set benchmark instances show that the augmented PAMC framework achieves competitive or lower time-to-solution than reported state-of-the-art baselines on several large Max-Cut instances, while matching or improving solution quality under comparable runtime budgets. The solver also discovers a new best-known solution for the G63 Max-Cut instance and scales to a fully connected 100,000-spin Ising instance. For Max-3-Cut, the same framework establishes new best-known solutions on 36 G-set instances, demonstrating its applicability beyond binary Ising formulations. These results indicate that feedback-controlled population dynamics provide an effective and scalable strategy for steering stochastic search in large-scale combinatorial optimization.

Achieving both reconfigurability and stable output signals is a critical challenge in the development of integrated photonic circuits for large-scale optical quantum information processing. This has led to the creation of multimode photonic processors, also known as reconfigurable multimode interferometers, which have wide-ranging applications in quantum and classical information processing. However, maintaining phase stability in multi-port input signals remains a significant hurdle, particularly due to the phase instabilities introduced by active cooling systems and temperature drifts in the photonic processor. In this study, we propose theoretical models to simulate phase instability in photonic processors and validate them against experimental results. Two distinct modeling approaches were employed: a Brownian random walk and phase reconstruction based on experimentally observed oscillating harmonics. Additionally, we verified and applied our model to a specific application for input phase correction using self-feedback control within the photonic processor.

Tomographic microscopy enables three-dimensional internal imaging but often requires expensive optical or X-ray instrumentation. Here we present an ultra-low-cost continuous-wave diffusive tomography (CWDT) system for biological samples. The system uses a smartphone microscope, a white LED coupled into an optical fiber, 3D-printed micropositioners, and a physics-based forward model optimized with machine learning. We demonstrate full-color volumetric reconstructions from a tartrazine-cleared poplar section, a scattering phantom, fungal mycelium near an Arabidopsis root, and thick poplar branch imaging with an inserted side-emitting fiber. The current results are qualitative and exploratory, but they show that scanned fiber illumination and inexpensive hardware can produce useful three-dimensional reconstruction outputs for low-cost microscopy experiments.