Papers

Self-similar dynamics are often described in terms of a single asymptotic scaling regime. Here we experimentally investigate the post-breakup recovery of a hydrodynamic interface over substantially wider temporal and spatial ranges than previously accessible. Beyond the previously identified scaling behavior, we find four successive self-similar regimes characterized by the $R_{\beta}$-scaling with $\beta=1/6$, 1/2, 1, and 2, connected by three distinct crossovers. All four regimes exhibit robust collapse near the interface tip, while geometrical dependence becomes detectable farther from the singular region. This systematic variation across space and scaling regimes is consistent with scale separation and enhanced universality closer to the breakup singularity. The characteristic scales associated with the different $R_{\beta}$-scalings become comparable at the common geometric scale $z_m \simeq R$. These findings demonstrate that self-similar dynamics can undergo multiple successive crossovers among distinct scaling regimes rather than directly approaching a single asymptotic self-similar state.

We present molecular dynamics simulation results for an ultracold $^{40}$Ca plasma in a constant, homogeneous magnetic field. The magnetic field induces a significant spatial separation of charges. Furthermore, a significant fraction of the electrons escapes from the region bounded by the ions, in contrast to the case of plasma expansion into a vacuum. This leads to the formation of a quasineutral plasma core containing the remaining electrons and surrounded by an outer, thick ion shell, where the ion density drops sharply. Anisotropy in the ion kinetic energy is also observed, depending on the direction of ion motion relative to the magnetic field lines. The simulation results are in agreement with experimental data on ion dynamics.

Artificially generated muon beams, with tunable parameters, are highly valuable in imaging objects that are opaque to any other scanning technique. Lower-energy muon beams are currently produced at accelerator facilities, while higher energies have only recently been observed as a result of the interaction of laser-wakefield-accelerated electrons and solid targets. This work focuses on using muography imaging detectors to measure the profile of a muon beam generated via the Bethe-Heitler process and to perform muon imaging in an experimental setup extending up to 42 meters from the muon source. By comparing the data with Monte Carlo simulations, we confirm that the measured beam profile and object imaging are consistent with artificially produced muons of energy of a few GeV. To our knowledge, this represents the first demonstration of imaging dominated by an artificial, laser-driven muon beam.

In scientific machine learning, $\Delta$-learning trains models on residual errors relative to physical baselines, assuming that more accurate baselines with smaller residual scales inherently improve downstream performance. Here, we demonstrate that residual scale alone is an insufficient heuristic for learnability. Evaluating molecular graph neural networks on total energy targets, we show that complex local descriptor baselines can yield small residual targets that are disproportionately rough within architecture-informed proxy spaces and harder to learn relative to their scale. Conversely, semi-empirical baseline reduces both scale and normalized roughness, improving in-domain and out-of-domain prediction. We introduce scale-normalized graph Dirichlet roughness ($D_{\text{IQR}}$) as a pre-training diagnostic for residual learnability and establish baseline complementarity as a core target-design principle, elevating target space formulation alongside model architecture as a key axis for scientific machine learning.

A new simulation approach (J. Chem. Phys. 163, 164113), based on the seamless combination of the electron transfer at the electrode/electrolyte interface with the charge and mass transport in electrolyte solutions, holds the promise to advance the comprehensive modeling of basic electrochemical devices such as electrolytic cells and batteries. The underlying model also includes a fully consistent treatment of structural, thermodynamic and statistical properties of the electrical double-layer at non-ideal electron-conducting interfaces. As a result, subtle aspects such as Tafel and Butler-Volmer equations, the overpotential concept, as well as (stationary) non-equilibrium features such as the entropy production, arise naturally from the model, with a minimum of ad-hoc assumptions and elaborations of the simulation results. We illustrate these aspects for a simple model of electrochemical interface, and discuss further improvements of the method meant to enhance its ability to model systems and phenomena of interest for electrochemistry. One such improvement, concerning the effect of fluctuating electric fields on the electron transfer rate, is explicitly developed.

In preparation for the commissioning of Linac2 (formerly referred to as the {PIP-II} linac) at Fermilab, we are developing high-level applications that automate Orbit Response Matrix (ORM) measurement and beam-based localization of quadrupole magnetic centers. Both procedures are designed to run in a low-amplitude mode compatible with otherwise regular operation. The ORM is measured by exciting multiple dipole correctors simultaneously at low amplitude, each at a distinct frequency, and applying a Discrete Fourier Transform to the recorded BPM signals to extract per-element transfer function with quantified noise. One application of the measured ORM is to predict the corrector coefficients that form local orbit perturbations (bumps). To center the trajectory in a quadrupole, such a bump is created around it, the quadrupole current is oscillated, and the responses of the downstream BPMs are recorded and analyzed. Repeating this at several beam positions inside the quadrupole – each set by the bump – the dependence of the BPM signal on beam position provides information about the quadrupole center. The applications are being tested at the Fermilab 400\,MeV Linac during regular production runs, and the lessons learned are fed back into their improvement.

Diffusion models have shown strong potential for kilometer-scale precipitation downscaling, but their performance in geographically unseen regions and event regimes remains insufficiently understood. Building on the wavelet diffusion model (WDM) framework, this study evaluates cross-region and cross-event generalization. Six 3 x 3 deg U.S. regions represent convective, winter, tropical, and atmospheric-river precipitation regimes. Low-resolution inputs are generated by block averaging NOAA Multi-Radar/Multi-Sensor (MRMS) composite reflectivity fields. A WDM trained only on Oklahoma (OK) samples and a WDM trained on all six regions are compared with nearest-neighbor and Bicubic interpolation. Model performance is evaluated using three metric families that measure image-domain reconstruction, spectral and distributional fidelity, and bin-wise precipitation detection. The OK-trained WDM remains competitive outside OK. Although the all-region WDM delivers the best and most consistent overall image-domain and detection performance, its gains are uneven across precipitation intensities. Bin-wise critical success index (CSI) over 5-dBZ reflectivity bins shows that WDM improvements concentrate in localized higher-reflectivity structures, which image-domain metrics partly obscure. In addition, the performance differences among samples are strongly associated with the spatial organization of the precipitation field, quantified by Moran's I as the spatial autocorrelation of each reflectivity bin. The sample-level Moran's I-CSI correlation stratified by sample intensity reaches 0.901 in all six regions, including regions unseen during training. Overall, these findings support future efforts to transfer downscaling models to regions with limited local training data and to generate globally consistent, high-resolution precipitation products.

We present a survey of electron distributions measured by THEMIS within Earth's equatorial magnetosphere at $L$ values $\le$ 14. Particular attention is focused on the spatial distribution and occurrence of magnetic field-aligned and perpendicular anisotropies in electron distribution functions and how these depend on solar wind dynamic pressure and magnetospheric activity. Using 161,300 3-s resolution electron measurements from THEMIS A and D, we find that field-aligned anisotropies are most prominent at low energies (30–200 eV) and occur over nearly all $L$ and MLT, with the largest values concentrated in the dayside/postnoon plasmasphere, while perpendicular anisotropies dominate at higher energies (1–30 keV) and are largest and most probable in a narrower band from $L \sim$ 5–10 with a pronounced dayside/prenoon bias. A localized region near dawn at $4\lesssim L\lesssim 8$–9 is found to be typically devoid of field-aligned electrons over the entire 30–200~eV band. Both anisotropy types shift toward lower $L$ shell and become more probable under elevated solar wind dynamic pressure and Kp, and their spatial and energy dependence is largely complementary: perpendicular anisotropies coincide spatially with regions of quasi-parallel chorus generation, while field-aligned anisotropies coincide with oblique chorus and time domain structure (TDS) generation regions. In addition to being a useful diagnostic for electron population types, our results provide a useful basis for comparison with global distributions of wave modes which arise from these anisotropies, and offer an anisotropy-based observational framework for interpreting their dependence on magnetospheric activity.

Chimera states, characterized by the coexistence of coherent and incoherent dynamics in networks of coupled oscillators, are among the most intriguing collective phenomena in nonlinear systems and are strongly shaped by the underlying topology and initial conditions. Their analysis in large-scale networks remains computationally demanding and motivates the development of coarse-graining strategies that reduce network size while retaining the essential dynamical features of chimera behavior. In this Letter, we investigate whether equitable-partition-based quotient graphs can preserve chimera patterns under substantial reductions of network size. Using nonlocally coupled FitzHugh-Nagumo (FHN) oscillators, we exploit the fact that, for an equitable partition, the full-network dynamics restricted to the associated cluster-synchronous subspace coincide exactly with the quotient dynamics. We ask whether this structural exactness is sufficient to preserve chimera signatures after the network size reduction. Our results show that this is not necessarily the case: agreement between quotient and full-network chimera dynamics depends on the equitable partition, and exact quotient dynamics do not necessarily guarantee the transverse stability of the corresponding full-network trajectory. Importantly, when the cluster-synchronous solution becomes transversely unstable, the full network may depart from the exact quotient trajectory while remaining in a macroscopic chimera regime. Therefore, structural exactness and dynamical preservation are distinct requirements, and transverse stability provides a complementary criterion for assessing quotient-based coarse-graining of chimera states.

Previous work built AI-based surrogates for a nonlinear gyrokinetic simulation code with the goal of using them for fast, direct calculations of turbulent ion heat flux in stellarator design optimizations and scenario planning for experiments. These AI surrogates were trained on data from >200k nonlinear, adiabatic electron gyrokinetic simulations with the gyrokinetic flux-tube code GX, using a wide range of stellarator magnetic configurations ($\sim$23k), positions in the plasma, and gradient scale lengths. In this paper, we demonstrate the use of the AI-based turbulence surrogate in the optimization of stellarator magnetic equilibrium and to speed up stellarator transport solvers. Due to its speed ($\sim$ms), the AI-based surrogate enables previously unattainable optimization objectives, such as full radial profiles of ion turbulent heat flux, or directly optimizing to maximize the turbulent critical gradient at multiple locations across the plasma. These direct calculations provide a potentially more accurate optimization target and reduce reliance on ad-hoc heuristics that may not accurately capture the variation of turbulent transport with magnetic configuration. By including the AI-based surrogate for turbulent heat flux in a transport solver, we can quickly postprocess and confirm the improved ion temperature resulting from the optimized equilibrium. Finally, we demonstrate the use of AI agents with strong reasoning AI models to automate the outer loop, exploring many objective and hyperparameter configurations with this AI-based turbulence surrogate to discover improved turbulence optimized magnetic equilibria.

The transition state barrier is a foundational concept in chemical kinetics, yet its physical origin is seldom investigated. In this study we analyze the barrier to hydrogen atom transfer in collinear H$_2$+H$^{\bullet}$ on its Born$-$Oppenheimer surface. We extend upon the energy component analysis of chemical bonds to develop an electronic kinetic energy-based interpretation of the transition state barrier. This approach reveals that Pauli exclusion is necessary for the existence of the energy barrier to H$_2$+H$^{\bullet}$ atom transfer, which is consistent with a complementary analysis using absolutely-localized molecular orbitals.

The axion coupling to electromagnetism may leave signatures of uncertain nature in astrophysical spectra; one is thought to be a single line centered at the axion frequency $\omega = \sqrt{\left(m_a c^2/\hbar\right)^2 + c^2 k_a^2}$, with $m_a c^2/\hbar$ the axion Compton frequency and $\hbar k_a$ its momentum. If a parametric plasma instability is excited, this signature is transformed into a set of lines, which may be proportionally spaced and linearly polarized. A Crab pulsar signal at a frequency near recent cosmological predictions for the axion Compton frequency has some qualitative similarity to this signature, though this similarity is not evidence that the signal is caused by axions.

We show that an isotropic, lossless dipolar particle, locally embedded in a homogeneous, lossless host, cannot experience axial optical torque under any illumination, in any photonic environment, whatever its own chirality–a general identity extending the sphere-in-free-space result to arbitrary chiral and substrate-coupled environments. Shape anisotropy is the essential escape route, and the resulting torque is not generic: closed-form, radiative-reaction-dressed polarizabilities for a chiral dielectric ellipsoid show its loss-free "optical spanner" torque changing by an order of magnitude between enantiomers (\kappa \to -\kappa), a mechanical response impossible for any sphere. Above a high-index or lossy substrate like gold or silicon, standing-wave enhancement, or simply a larger particle at a near-contact gap, brings it beyond optical-torque-wrench thresholds.

Quantum geometry governs a wide array of physical observables in topological quantum materials. In its ideal limit, quantum geometry yields exact bounds and analytical results for a growing class of observables. However, a comparable framework for the shift current remains elusive because it depends on geometric relations between multiple bands. In this work, we show that the shift current is proportional to the cyclotron shift, the displacement of the cyclotron center induced by Landau-level mixing. This mechanism yields a universal scaling law: the integrated weight and the peak magnitude scale as $l^{i+1-n}$ and $l^{2i+1-n}$ in the magnetic length or moiré period $l$, where $n$ is the momentum order of the symmetry-breaking perturbation and $\Delta E \propto l^{-i}$ is the level spacing. We verify the scaling law in exactly solvable chiral-$N$ Landau levels under a uniform magnetic field and in Schrödinger and Dirac moiré skyrmion crystals, which capture the flat Chern bands of twisted MoTe$_2$ and twisted bilayer graphene. For a Schrödinger flat band with a distorted skyrmion texture, the integrated weight grows linearly with the moiré period and the resonant peak grows cubically.

This work presents a consistent formulation of body-force and heat-source terms within a double-distribution-function lattice Boltzmann framework for simulating compressible flows of generic fluids. The proposed approach extends a recently developed kinetic framework for non-ideal compressible fluid dynamics by incorporating external forcing and volumetric heating through shifted quasi-equilibrium states. The standard lattice Boltzmann discretization is applied with a second-order accurate integration along characteristics and product-form equilibria on nearest-neighbor lattices are employed, supplemented by necessary correction terms. This formulation ensures the consistent recovery of the Navier-Stokes-Fourier equations, including the Korteweg stress tensor, across arbitrary equations of state, while maintaining independently controllable thermodynamic and transport coefficients. The methodology is rigorously validated and applied for two different flow regimes, with ideal-gas compressible flows and non-ideal/multiphase compressible flows, respectively, where a broad hierarchy of benchmarks is employed, including non-classical shock tubes, thermal Couette flows, and force-driven Poiseuille and Womersley flows. The framework's ability to capture complex thermodynamic processes using forcing and heating is further demonstrated through Rayleigh and Fanno flows, Joule-Thomson effects, and throttling processes. Furthermore, the model is validated for multiphase phenomena, including interface consistency and liquid-vapor co-existence. The results demonstrate excellent agreement with analytical solutions and reference data, while spatio-temporal grid-refinement studies confirm the expected second-order accuracy of the scheme. This establishes the model as a robust and efficient foundation for simulating highly compressible flows of generic fluids in the presence of numerical and physical s...

We report on a new beam current monitor system based on work originally done to develop timing planes for future collider detectors. In contrast to sensors based on wall currents or toroid inductance, we make use of electric field induced by the electromagnetic vector potential of the beam current as it passes near or through a transmission line (TL), arranged with its dominant TEM or TE10 fields aligned with the current, thus directly coupling to TL modes. For resonantly extracted proton pulses currently being commissioned at the Fermilab Delivery Ring, we utilized a TEM parallel-plate TL geometry, since the requirements included measuring the structure of the bunch over $\sim{}200~\rm{ns}$ time scales. We present the design and results of simulated and realized sensors now deployed at Fermilab for future use as pulse-to-pulse proton intensity monitors at the Mu2e experiment. In addition, inspired by this and prior work, we investigate the use of broadband mm-wave dual-ridge waveguides as a means of measuring substructure of nanosecond bunches down to the tens of picosecond level for future use at the Long Baseline Neutrino Facility (LBNF).

Particle shower simulation is a major computational cost in high-energy physics. Monte Carlo methods such as Geant4 are accurate but expensive, while most machine learning surrogates are tied to specific detector geometries and require retraining for each design change. We study cross-geometry transfer learning with CaloClouds II, a generative model that produces point clouds rather than voxels and can project onto arbitrary detector readouts. We pre-train on photon showers in the International Large Detector (ILD) and adapt to electron showers in the cylindrical CaloChallenge Dataset 3. With only 100 target showers, fine-tuning reduces the geometric mean Wasserstein distance to Geant4 by about 51% over training from scratch. Bias-only fine-tuning (BitFit) stays within 5% of full fine-tuning while updating only 17% of the diffusion network parameters. We also examine model collapse in CaloClouds II by retraining its normalising flow and diffusion model on its own generated showers across successive generations.

A general physics-based model for the distribution of shear stress and surface pressure is developed for laminar flow over spheres. Although integral coefficients have been studied extensively for spheres, they do not capture the local surface distributions that govern phenomena such as heat transfer, phase change, and mass loss in multiphase and particle-laden flows. The model depends on the flow regime of the sphere ($Re_\infty$, $M_\infty$, $Kn_\infty$), local gas properties ($\gamma$, $\omega$, $Pr$), and surface conditions ($T_w/T_0$, $\sigma_t$, $\sigma_n$). The formulation incorporates boundary-layer scaling, flow separation, shock-wave physics, velocity-slip effects, and high-speed rarefaction, while recovering the known asymptotic behavior in the creeping-flow, hypersonic, and free-molecular limits. The proposed model is validated in the continuum and rarefied regimes using CFD and DSMC simulations from literature. In the continuum regime, it is compared with CFD simulations of flow over a sphere in air under subsonic, supersonic, and hypersonic conditions with varying surface temperatures. The model captures the CFD distributions very well over a wide range of $Re_\infty$, $M_\infty$, and $T_w/T_0$. In the rarefied regime, it is compared with DSMC simulations of flow over a sphere in a monatomic gas under subsonic and supersonic conditions with fully diffuse and partial surface accommodation. It shows good agreement with the simulated shear stress and surface pressure distributions over a wide range of $M_\infty$ and $Kn_\infty$, while accurately capturing the effects of surface accommodation around spherical bodies.

Scientific instruments require accurate calibration to convert detector signals into reliable physical observables. Conventional calibration procedures typically rely on dedicated calibration measurements, analytical response models or labelled reference data, limiting their ability to adapt to changing operating conditions and detector aging. We present a physics-informed self-supervised learning framework that jointly performs wire calibration and interaction position reconstruction in Multi-Wire Parallel Plate Avalanche Counters (MWPPACs) without requiring labelled position measurements or dedicated calibration runs. The method formulates detector calibration as a latent optimization problem in which global wire gains and event-wise interaction positions are estimated simultaneously using supervision derived exclusively from detector geometry and charge-energy consistency constraints. A detector-independent neural network reconstructs sub-wire interaction positions from local charge distributions, eliminating the need to assume analytical induction profiles by learning the detector response directly from experimental data. The end-to-end differentiable framework enables continuous detector self-calibration while improving the uniformity and accuracy of position reconstruction. Experimental evaluation on the entrance MWPPAC tracking detectors of the VAMOS++ magnetic spectrometer demonstrates stable convergence, improved spatial homogeneity and enhanced position resolution. Beyond the detector studied, the method establishes a general framework for physics-informed self-supervised calibration of scientific instruments and is a step toward autonomous intelligent instrumentation capable of continuous adaptation during operation. In this paradigm, detector calibration is no longer a prerequisite for an experiment but an integral part of the measurement process itself.

Machine-learning (ML) emulators for atmospheric processes have advanced rapidly in recent years, transforming weather forecasting. Although early ML ocean forecasting models now exist, they remain less developed than their atmospheric counterparts. Unlike the atmosphere, much of the ocean's kinetic energy resides in mesoscale eddies whose characteristic spatial scales are approximately an order of magnitude smaller than those of comparable atmospheric features. Moreover, complex coastlines, narrow straits, and ice-covered seas make boundary representation a central challenge that atmospheric models do not face. Consequently, numerical ocean simulations commonly use locally refined or even completely unstructured meshes. However, their data-driven counterparts have so far been built around latitude-longitude grids. We present HClimRep-Ocean, an ocean emulator that operates directly on the native unstructured mesh of FESOM2. The emulator is trained on a 209-year AWI-CM3 control integration and is run without atmospheric forcing, receiving the atmospheric state only at initialisation time, which isolates the predictability carried by the ocean state itself. Skill is strongly field-dependent: for currents, HClimRep-Ocean outperforms every reference at 30 day forecast, whereas for temperature and salinity a damped-anomaly persistence forecast remains the more accurate estimator. This behaviour is physically interpretable: current variability is largely geostrophic and internally generated, whereas sea-surface temperature and salinity fluctuations are driven by atmospheric forcing through weather state. Evaluated independently on the OceanBench benchmark, a reanalysis-trained variant of HClimRep-Ocean achieves the lowest RMSE against GLORYS reanalysis among all assessed systems, confirming the competitiveness of the native-mesh approach.

Fast-neutron scintillators must efficiently convert neutron energy into detectable light while enabling selectivity between neutrons and the {\gamma}-ray background. Here, we report a synthesis strategy for layered two-dimensional (2D) lead halide perovskite scintillators with independent control of the macroscopic detector geometry and microscopic phase composition. Our synthesis approach produces mm-thick, large-area scintillators with a deliberately engineered multi-quantum-well energy landscape, creating an effective spectral separation while retaining nanosecond emission dynamics. Our scintillators exhibit an energy resolution of 5.4% at 662 keV under 137Cs irradiation. Under deuterium-deuterium fusion neutron irradiation, we directly demonstrate neutron and {\gamma}-ray event separation by pulse shape discrimination, with a figure of merit of 3.51. Taken together, these results establish multi-quantum-well 2D perovskites as a scalable materials platform that combines fast-neutron sensitivity and selectivity within a controllable detector form factor.

Astronomers increasingly fit their models with gradient-based methods, such as Hamiltonian Monte Carlo, which need the derivatives of the model with respect to its parameters. In many analyses a prediction requires solving a small system of ordinary differential equations (ODEs) for thousands to millions of parameter sets, and this ensemble of integrations often sets the cost of the analysis. We introduce to the astronomical community GRADSOLVE, a JAX library that moves this computation to graphics processing units (GPUs): it integrates each member of the ensemble in its own GPU thread with its own step size and returns the derivatives with respect to the parameters in the same pass. We measure the speed-up it provides in three examples from different fields of astronomy, stellar orbits in the Galactic potential, the expansion history of a dark-energy cosmology and the spin precession of binary black holes, with every code held to the same accuracy requirement. For a million trajectories GRADSOLVE runs 8.5 to 1500 times faster than the serial CPU code of each example running on all 128 cores of a CPU, and several orders of magnitude faster than on one core. On the same GPU it is also 11 to 15 times faster than DIFFRAX, the state-of-the-art ODE library in JAX, in all three examples. With GRADSOLVE a million such integrations take seconds or less on one GPU, so gradient-based analyses that need ensembles of this size become routine. The code is publicly available at https://github.com/ECLIPSE-AI4Science/gradsolve.

Snapshot hyperspectral imaging maps a spatial–spectral datacube onto a two-dimensional detector, with recoverability depending on the datacube sampling regime, the scene prior, and the optical encoding. We study this co-dependence with algorithmic recovery and optical design optimization in a differentiable metasurface model by varying the spatial–spectral ratio and comparing average-coherence, mutual-information, and end-to-end objectives. For compressed imaging with three bands, optimization of single-parameter dielectric pillars produces lens-like designs with overlapping wavelength-dependent point-spread functions across the objectives tested. End-to-end reconstruction can mask this collapse on restricted training distributions, showing that reconstruction fidelity depends jointly on the optical encoding and scene prior. For simple nano-pillar meta-atom geometries, the optimized singular-value spectrum deteriorates as the datacube becomes more spectrally dominated, whereas a relaxed model with independent complex transmission at each wavelength reduces this dependence. These results identify restricted wavelength-dependent transmission control in the pillar library as an important contributor to the observed spectral collapse. This motivates an illustrative local super-pixel spectrometer model in which spectral encoding is studied separately from global image formation under a plane-wave illumination approximation.

This work introduces a spectral transfer formalism for nonlinear synchronisation dynamics to resolve how internal dynamical activity redistributes across network scales, moving beyond traditional global observables like the Kuramoto order parameter. By projecting Kuramoto phase dynamics onto the eigenbasis of the graph Laplacian, we construct a time-dependent transfer matrix and derive novel network metrics, including the spectral flux, which quantifies directional redistribution between structural dynamical scales rather than physical transport across nodes. Numerical simulations on modular and hierarchical networks reveal highly structured, intermittent spectral transfer episodes characterised by forward and inverse spectral cascades, directional reversals, and complex interaction geometries. Crucially, we demonstrate a clear separation between macroscopic coherence and microscopic spectral interactions, showing that highly organised, fluctuating internal redistribution dynamics persist even beneath nearly stationary and featureless global synchronisation states. This framework extends conventional synchronisation analysis and establishes a multiscale approach for interpreting network dynamics as an evolving process of spectral reorganisation.

Mechanical characterization of subsurface rock formations typically requires standardized cylindrical core specimens, which are often unavailable from fractured or unconventional reservoir sequences. This study uses a Discrete Element Method (DEM) framework calibrated to Sulphur Mountain Formation siltstone to investigate how fragment geometry governs mechanical response in irregular rock particles, which are direct analogues of drill cuttings. Nineteen specimens (18 procedurally generated irregular geometries and one reference Brazilian disk), normalized to a 10 mm bounding sphere radius, were tested under quasi-static (Brazilian-type) and dynamic (Short Impact Load Cell) loading modes. Four mechanical outputs of failure force (Ff), apparent strength ({\sigma}f), apparent stiffness (Ka), and stiffness (E) were regressed against seven retained shape descriptors. Results show that force-based quantities are strongly controlled by the surface-area-to-volume (SA/V) ratio under static loading (Adj. R2 = 0.69), while area-normalized quantities ({\sigma}f and E) are geometrically insensitive. Dynamic loading amplifies geometric sensitivity for failure force and introduces an independent role for surface concavity depth. These findings establish a preliminary quantitative framework for interpreting mechanical measurements from irregular rock fragments when a standardized core is unavailable.

This study examines the extent to which physics graduate teaching assistants (TAs) are aware of introductory physics student thinking and the types of challenges introductory students commonly have with energy and momentum concepts, which is important for implementing active learning methodologies and supporting diverse learners in physics courses. We present findings from a TA professional development course and discuss an approach to investigate TAs' pedagogical content knowledge, specifically their ability to recognize introductory student conceptual difficulties. We investigated 70 first-year graduate TAs' ability to identify common introductory physics student difficulties on the Energy and Momentum Conceptual Survey (EMCS). The TAs participated in a professional development course that emphasized reflection on introductory student thinking patterns to promote evidence-based pedagogical practices. TAs predicted the most common incorrect answers introductory students would select after lecture-based instruction, then compared their predictions with actual data from introductory students followed by a class discussion. Results reveal gaps between TAs' perceptions and introductory student thinking, with TAs performing poorly on many of the analyzed questions. For example, TAs consistently overestimated that introductory physics students would use novice-like thinking in many situations posed in EMCS problems, i.e., they expected introductory physics students to make more novice-like errors than they did. These findings have important implications for effective instructional design, e.g., physics TAs who misunderstand introductory physics student capabilities may inadvertently create barriers to learning by spending valuable class time on either over-scaffolding or under-challenging introductory physics learners and not spending time on pedagogical issues that are important to address.

Hybrid particle-in-cell simulations are widely used to study kinetic processes in collisionless astrophysical and space plasmas, yet the high computational cost of large-scale three-dimensional runs has largely confined production studies to two dimensions or restricted domains. To address this challenge, we present a performance-portable GPU implementation of the hybrid particle-in-cell code dHybridR. The implementation combines OpenMP target offloading with specialized SYCL kernels for the most computationally expensive operations, while preserving a unified CPU-GPU codebase that supports Intel, AMD, and NVIDIA GPUs. On the exascale supercomputers Aurora and Frontier, dHybridR achieves weak scaling efficiencies of $86\%$ to $97\%$ across 49,152 accelerators, and at 256 particles per cell, its full-node GPU throughput is up to $32\times$ that of the vectorized CPU implementation at approximately $90\%$ less energy per particle-update. To our knowledge, no other hybrid particle-in-cell code has reported GPU performance at this scale, leaving dHybridR uniquely positioned to exploit exascale systems. These advances substantially lower the computational barrier to large-scale three-dimensional hybrid-kinetic simulations of collisionless plasmas.

Topological photonic crystals support robust, disorder-resilient light transport protected by their band-structure topology, yet their design remains confined to a small set of symmetry-defined templates. The surrounding freeform design space, where optimal performance and new functionality may reside, is difficult to access because of its high dimensionality and costly full-wave simulation needed for evaluating each candidate. % Here we report generative inverse design of freeform valley photonic crystals. A label-free diffusion prior learns the distribution of three-fold rotationally symmetric geometries from 7,689 unlabeled designs that cost 0.15~s each to produce, while a convolutional surrogate is trained on 1,666 full-wave band-gap simulations costing 4.28~min each, exploiting a 1,700-fold cost asymmetry. Across nine target band gaps up to $225~$meV, we generate and verify by full-wave simulation 270 valley photonic crystal designs, reaching a mean absolute error of $1.4$–$6.7$~meV within the labeled range and retaining below $5.1\%$ fractional error at targets $25\%$ beyond its upper bound, while a label-conditioned diffusion model trained on the same label data performs substantially worse. Beyond inverse design, the trained model functions as an instrument for discovering structure–property relations. At a target band gap where the labeled set contains only five designs, it generates hundreds, resolving geometric trends that are statistically inaccessible in the training data and yielding interpretable rules for large-gap valley photonic crystals. Our results establish surrogate-guided diffusion as a label-efficient route to freeform topological-photonic design, and as a means of extracting design principles where physical intuition and simulation labels are both scarce.

This paper provides a detailed account of the origins, objectives, design, and implementation of the Milestone-Based Fusion Development Program, a public-private-partnership (PPP) program launched by the U.S. Department of Energy's Office of Science in September 2022. The purpose of this program is multi-faceted, with policy, scientific and technological (S&T), and commercialization objectives. Tactically, the program supports privately funded fusion companies in closing S&T gaps toward delivering preliminary engineering designs of their demonstration fusion plants (aka "fusion pilot plants"). The program is a key element of a shift in U.S. fusion strategy, initiated in 2022, to accelerate fusion commercialization by leveraging PPPs. The design of the Milestone Program was underpinned by its authorizing legislation and the U.S. National Academies report Bringing Fusion to the U.S. Grid (2021), and drew inspiration from other PPP programs discussed in this paper. Finally, the paper provides the authors' perspectives and reflections on the strengths/weaknesses of the program as implemented, implementation barriers, and lessons learned, with the intent to inform future PPP programs in fusion and other energy/technology disciplines.

We investigate the linear growth rate of tearing instability in a resistive force-free current sheet using the linearized fully-compressible magnetohydrodynamics (MHD) equations. Our results show that, as the plasma beta ($\beta$), which is proportional to the ratio of thermal pressure to magnetic pressure, increases, the dispersion relation approaches that of the incompressible tearing mode. As $\beta$ decreases, the growth rate increases but this increase remains finite even in the limit $\beta \rightarrow 0$. The relative increase in the growth rate from $\beta = +\infty$ to $\beta=0$ depends on the Lundquist number, with lower Lundquist numbers showing a more pronounced increase. These findings indicate that, while compressibility has a negligible effect on the growth rate of tearing instability in high Lundquist number regimes, its effect becomes significant at low Lundquist numbers. This suggests that compressibility may influence the critical Lundquist number for the transition from laminar to plasmoid-dominated magnetic reconnection. Moreover, in low-$\beta$ regimes ($\beta < 1$), the generated density perturbation can be substantial, comparable to or even larger than the magnetic field perturbation, even though the growth rate is only slightly modified by the compressibility.