Papers

This report documents BBun (Blunt Body unsteady), a low-order code for the unsteady potential flow around one or more interacting three-dimensional bodies. The bodies are represented by vortex rings (piecewise-constant doublets) with a Neumann boundary condition; free vortex sheets are shed from the trailing edges and, optionally, from the tips, and convected in time, with an implicit unsteady Kutta condition. The report describes the choices that make the discretization consistent and convergent (a single desingularized kernel per wake line, a bordered factorization of the singular Neumann problem, interior velocity conditions for the nearly singular mode of ring-shaped bodies, the detection of the separation lines from the grid connectivity) and the extensions to several bodies, box wings, symmetric configurations (image method), non-lifting bodies, rigidly rotating bodies (propellers), the unsteady Bernoulli equation, a wake-body collision treatment and a strip-wise integral boundary layer. The verification and validation are reported in full: potential flow around a sphere, sensitivity and grid convergence, the two-dimensional limit, an independent vortex-lattice method, the NACA 0015 wing of McAlister and Takahashi (loads and chordwise pressures), the NACA 0012 profile drag, the biplane of Knight and Noyes, Prandtl's best wing system, Wagner's function and blade-element momentum theory for a propeller. A complete aircraft with counter-rotating tractor propellers illustrates the use of the code.

Fast and accurate simulation of hemodynamic behavior within vascular networks is essential for numerous clinical applications. However, obtaining high-quality and computationally efficient flow measurements across complex vascular networks remains challenging. To address this, we first decompose the vascular network into a set of bifurcation units and then develop an operator network capable of mapping unit-specific parameters to the local solution fields of each bifurcation unit. By lumping the Windkessel-model outlet parameters and incorporating inlet boundary conditions from the solution of parent units, the flow and pressure fields can be rapidly approximated. Subsequently, operator-network-driven Schwarz waveform relaxation is applied across bifurcation units to correct discontinuities and improve numerical accuracy. On 7-segment and 55-segment arterial tree models, the proposed method achieves $13\times$ to $17\times$ wall-clock speedups over conventional 1D numerical simulation, with relative $L^2$ errors of 1% in both pressure and velocity. The resulting pulse wave velocity biomarkers agree with the conventional reference to within 1–2%, and the same trained operator generalizes to different tree-like 1D vascular network topologies.

Pickup trucks account for 14% of new light-duty vehicles produced in the United States, yet are among the least aerodynamic. Their open cargo bed adds a flow absent from existing automotive aerodynamics datasets such as DrivAerML and SHIFT-SUV: the shear layer leaving the cab roof passes over a recirculating bed flow before separating again at the tailgate. The resulting drag lowers fuel efficiency, raises emissions and limits the range of electric trucks. Scale-resolved Computational Fluid Dynamics (CFD) is too costly for broad design exploration; neural surrogates can predict flow features at a fraction of that cost, provided they are trained on large-scale, high-fidelity, domain-specific data. We introduce SHIFT-Truck, the first such dataset for pickup trucks. It comprises 1,000 Spalart-Allmaras delayed detached-eddy simulations (SA-DDES) of a reference pickup geometry morphed across 17 shape parameters. Each case is run on a mesh of about 100 million cells at a Reynolds number of $1.4 \times 10^7$ and released with time-averaged surface pressure, wall shear stress, volumetric pressure and velocity. The setup is verified by grid refinement and repeated runs, and checked against wind-tunnel measurements. We define geometry-grouped splits and benchmark four neural surrogates, DoMINO, GeoTransolver, AB-UPT and SMART, on surface and volume tracks. SHIFT-Truck also introduces controlled distribution shifts in the operating point, the input surface discretization and the vehicle archetype. Models with strong in-distribution performance can degrade substantially under these shifts: operating-condition changes expose failures to infer speed dependence, while tessellation and cross-vehicle shifts reveal markedly different robustness across architectures. SHIFT-Truck is thus a benchmark not only for surrogate accuracy but also for generalization across physical and numerical distributions.

Reliable multistep quantum lattice-Boltzmann evolution requires controlling computational faults. When ideal collision preserves encoded mass and momentum exactly, changes in those charges can provide syndromes for selected faults at the post-collision checkpoint. A fault that shifts one population then leaves a unique mass-momentum residual labeled by the lattice velocity. A coherent reference register stores the expected charges, so the same test remains valid while the physical fluid charges vary across the lattice. Single bit flips on population number registers occupy disjoint syndrome sectors and satisfy the Knill-Laflamme condition. A state-vector demonstration on eighteen data qubits of the two-dimensional nine-velocity lattice (D2Q9) recovers that single-bit family to double-precision roundoff. Conservation still leaves a fifteen-dimensional kinetic nullspace on the three-dimensional nineteen-velocity lattice (D3Q19). Exact multiple-relaxation-time (MRT) streaming analysis ranks those charge-preserving modes by the order at which they return to density or momentum. A classical D3Q19 decaying-flow simulation at moderate Reynolds number then injects about $1.75\times10^4$ selected single-population shifts per realization. Recovery of that alphabet returns the trajectory to floating-point roundoff. The quantum statements assume an ideal charge-preserving collision and a reliable reference. The result is an inner recovery map for a stated charge-changing alphabet together with a kinetic classification of the unresolved sector.

Ship structural vibrations contribute to noise, fatigue, and equipment damage, while dynamic-compliance topology optimization can produce pathological designs near resonance. This study extends neural-reparameterized topology optimization using a convolutional Kolmogorov-Arnold network (KATO) to forced-vibration design with active input power (AIP) as the objective. Applications include a 100 Hz engine-supporting deck panel and an 18 Hz thruster foundation frame. Helmholtz PDE filtering and Heaviside projection control feature sizes and manufacturing tolerance. Across both deck families, all eight optimized layouts reduce AIP relative to size-optimized references and, after finite-depth extrusion, also achieve lower static compliance. For unrestricted, manufacturing-aware, and stress-aware frame variants, KATO matches GCMMA in AIP within 0.5 dB while yielding 22-36x lower static compliance after matched-volume binary re-analysis. In a near-resonant 300 Hz case, both methods reduce initial AIP by more than 32 dB; KATO maintains a connected design, achieves 59x lower binary static compliance, and reduces maximum AIP over 1-500 Hz by 2.7 dB. KATO runs 6.4-10.4x faster than GCMMA for the implemented stress-aware formulations. The results demonstrate neural AIP-driven topology optimization as an efficient approach for designing connected, feature-size-controlled ship structures with improved forced-vibration performance.

Scientific foundation models are commonly evaluated after heterogeneous physical problems have already been translated into a compatible gridded, tokenized or symbolic representation. This leaves the scientific interface outside both the pretrained model and the audit of what is actually reused. We study the complementary setting in which boundary histories, sparse monitor records and loading histories retain their native inference classes and their outputs remain on Cartesian, latitude-longitude and unstructured domains. GEODE couples task-specific scientific interfaces to a shared routed library of wavelet operators. A single jointly pretrained model represents cavity flow, radiation dose and elastoplastic stress, then acquires a heat exchanger and a reactor subchannel by training a private interface containing 2.1% of its parameters. Earlier predictions remain unchanged by parameter isolation, whereas unrestricted fine-tuning degrades them by factors of 14-29. Crucially, preservation alone does not establish reuse: norm-matched randomized-library controls show that the contribution of pretrained computation is conditional on the task and data regime. A separate decomposition shows that full-field relative L2 error can substantially understate error relative to spatial variation when field level dominates the norm. Task-specific operators remain more accurate on three of the five problems. These results distinguish multi-task coverage, preservation and pretrained reuse as separate properties that must be tested independently when scientific foundation models span heterogeneous interfaces.

A droplet breaks up in tens of microseconds, and a recording captures perhaps a dozen frames. The states in between cannot be recovered without repeating the experiment, and simulating them is too costly to sweep an operating envelope. Yet they are present in the corpus as a whole: a campaign spanning a device's actuation range produces morphologies resembling those any single recording missed. Exploiting that requires a representation that is low-dimensional, invertible, and faithful to shape rather than to sampling. Proper orthogonal decomposition supplies the first two but measures distance in sampled coordinates; manifold learning supplies the third but no map back to a shape; elastic shape analysis supplies a shape metric but no reduced coordinates. SHROM composes all three. Interfaces are represented by their square-root velocity functions, a neighbour graph is built over that shape space, and an autoencoder is trained to reconstruct while penalising latents in which graph neighbours are not latent neighbours. The demonstration uses 301,539 inkjet droplet contours and four filmed break-up sequences. The graph term does not improve reconstruction. It determines whether position in the latent carries meaning: clustering the latent of an otherwise identical model recovers the shape-space partition at chance level (ARI = 0.063 +/- 0.059), and at 0.781 +/- 0.049 with the term active. Waveform parameters predict the full contour at R^2 = 0.878 +/- 0.026. Negative results are reported in the same terms. The graph metric proved immaterial across five choices, and interpolation error saturates at the reconstruction limit, so a plain autoencoder leads that task. The main limitation is interpolation across a topology change: no component of the regularised latent holds both a single-component and a post-break-up shape, whereas an unregularised autoencoder mixes them freely.

A key objective in Weyl-semimetal (WSM) research is to identify phases built from minimal configurations of Weyl points (WPs), which provide the simplest settings for investigating the intrinsic physics of chiral topological charges. Pang et al. [Phys. Rev. Res. 8, 033304 (2026)] recently established that crystallographically realizable WSMs can be decomposed into linear combinations of 16 minimal "irreducible Weyl molecules" (IWMs). A fundamental question remains unresolved: which of these non-decomposable configurations can themselves form standalone crystalline phases? Here we introduce irreducible Weyl semimetals (IWSMs), in which two bands crossing near the Fermi level form an elementary symmetry-compatible complex whose complete Weyl-node configuration is charge neutral and crystallographically indivisible. Combining all 1651 magnetic space groups with Weyl-orbit multiplicities and band-compatibility relations, we classify IWSMs formed by crystalline-symmetry-protected twofold WPs. Only ten of the sixteen primitive inventories admit one-copy realizations, comprising four Pair, four Split, and two Mixed charge-node architectures. Remarkably, this classification uncovers five unconventional and previously unrecognized topological phases: the charge-three Pair IWSM {3, -3}; the Split IWSMs {3, -1, -1, -1}, and {4, -1, -1, -1, -1}; and the Mixed IWSMs {3, 1, -2, -2} and {3, 3, -2, -2, -2}. For all ten classes, symmetry-constrained lattice models verify the complete node inventories, Chern charges, and surface chiral-flow incidence. Our results establish crystalline irreducibility as a phase-level organizing principle beyond individual Weyl nodes and provide a symmetry-resolved framework for identifying minimal Weyl complexes in electronic, phononic, and photonic systems.

Magnetohydrodynamics (MHD) is central to plasma modeling in astrophysics, space science, fusion, and engineering, but resolving multiscale MHD dynamics is computationally expensive. Machine-learning surrogates enable fast inference by learning reusable solution operators, yet existing models require separate training for each physical regime, limiting generalization across varying parameter settings. We introduce PHASE, a PHysics-Adaptive Scalable operator with residual Error correction, designed to model incompressible MHD across varying physical parameters with a single model. PHASE combines transfer learning, regime-aware adaptation, physics-centered learning, and residual refinement to improve both physical fidelity and generalization across MHD regimes. Together, these improvements achieve state-of-the-art prediction accuracy on two-dimensional MHD turbulence by reducing relative $L_2$ errors on physical fields by more than an order of magnitude compared to prior MHD neural-operator baselines. Moreover, PHASE generalizes successfully to unseen parameter values without retraining, demonstrating the cross-regime adaptability expected from operator learning. We evaluate PHASE beyond point-wise prediction errors using derived physical fields, spectral analysis, and distribution statistics, consistently observing improved physical fidelity. We further show that our framework can accurately simulate MHD instabilities by testing it on the Kelvin–Helmholtz instability, demonstrating the robustness of our method.

Flat band formation and nontrivial topology are central manifestations of kagome electronic structure, yet they are commonly discussed in idealized lattice geometries. In distorted kagome materials, structural deformation reorganizes electronic propagation pathways, but a microscopic understanding of how such distortions govern flat band dispersion and band topology is still lacking. Using a distorted kagome tight-binding model with rotated angle dependent long-range hopping and intrinsic spin-orbit coupling, we show that distortion reconstructs both the dispersion and topology of the kagome band manifold. The flat band descendant develops distinct bandwidth regimes associated with a redistribution of its extrema in momentum space. Simultaneously, symmetry-related band inversions generate quantized Chern number transfer whose parity is fixed by the multiplicity of the touching points, thereby determining the gap-resolved Z2 topology. The accompanying Berry curvature evolution produces characteristic anomalous Hall and Nernst responses. These results identify geometric deformation as a common microscopic origin of flat band and topological reconstruction in kagome systems.

We demonstrate how the population annealing Monte Carlo simulation method can be used to compute the ground-state entropy to high precision. This approach is tested for the classical antiferromagnetic Ising model on the Shastry-Sutherland lattice with diagonal couplings chosen twice as strong as the nearest-neighbor interactions. In the absence of an external magnetic field, the model is known to be disordered at all temperatures, with a macroscopically degenerate ground state. We find the ground-state entropy to be $0.458\,777\,78(10)$ per site, which is in good agreement with the estimate of a recent study using the corner transfer matrix renormalization group method. The sampled ground-state configurations are analyzed by considering the bond configurations, the local-energy configurations, and the spin-spin correlations. While all observations are consistent with the disordered nature of the ground state, the numerical data suggest a power-law decay of the size distribution of clusters of sites with local energies different from the ground-state level.

Near 57 keV, destructive interference makes argon-40 almost transparent to neutrons. The 0.4% of argon-36 and argon-38 in natural argon then supply over 90% of its opacity, amplified 230-fold. Published transmission data rule out the argon-36 evaluation FLUKA uses by default, even with every other isotope made transparent, and bound its cross section in the window to 2-11 b. In a DUNE-scale detector, switching to the data-consistent evaluations moves the fiducial fraction of argon-41 calibration captures by 9-13% while the total yield changes by only 2%: the effect is on where captures occur, not how many.

Bellissard's framework uses covariant observables to describe aperiodic solids. We examine how butterfly-like spectra in twisted bilayers can be related to bulk gap labels and topological response. A bulk gap permits a spectral projector and an integrated density of states. A topological response requires an additional pairing of that projector. In an illustrative finite honeycomb bilayer, an exact second-moment identity isolates the interlayer contribution to spectral variance. Angle-resolved spectra, finite state counts, an uncoupled reference, and size and broadening checks show how coupling redistributes spectral weight without assigning a bulk gap label. We then examine the requirements for atomistic electronic-structure calculations, including nonorthogonal orbitals, projected densities of states, and basis functions that move during relative sliding. The resulting framework specifies the steps needed to connect atomistic spectra with bulk state counting and topological response.

Accurate quasiparticle electronic structures are the foundation for understanding excited-state properties of materials and explaining optoelectronic, quantum, and transport phenomena. First-principles GW calculations nevertheless remain computationally intensive for large or configurationally complex systems. Here we introduce DeepH-GW, a deep-learning framework that predicts an effective GW quasiparticle Hamiltonian directly from atomic structure. Building on the local, equivariant message-passing architecture of DeepH, DeepH-GW is trained on high-fidelity plane-wave GW calculations through a real-space Hamiltonian-reconstruction interface. This approach combines the systematic accuracy and broad chemical applicability of plane-wave methods with linear-scaling neural-network inference. Across the systems examined, DeepH-GW reproduces quasiparticle band structures with errors on the order of a few meV. Moreover, we show that, despite the intrinsic nonlocality of many-body interactions, DeepH-GW exhibits strong cross-scale transferability from relatively small training structures to substantially larger supercells. We demonstrate that the framework can accurately capture GW-level electron-phonon band-gap renormalization through predictions in supercells with thermal displacements, illustrating the power of the approach. DeepH-GW therefore provides a practical route toward large-scale many-body simulations and foundation models for excited-state electronic structure.

The rapid adoption of generative artificial intelligence (GenAI) in higher education has introduced a critical pedagogical paradox: while these systems possess extraordinary capacity for information retrieval and synthesis, their default operational mode of supplying immediate, unprompted answers actively undermines the cognitive processes upon which genuine scientific understanding is built. This paper presents NuPaD (Nuclear & Particle Physics – Deep Learning Tutor), a novel pedagogical framework designed for the graduate-level subatomic physics curriculum. For such advanced courses, instruction naturally shifts toward inquiry-driven, problem-based learning, making it an ideal environment to use GenAI to explore complex, open-ended physical questions rather than merely querying established facts. The framework consists of three tightly coupled and easy-to-use components: a primary agent instruction file that enforces a structured problem-based learning protocol, a purpose-built textbook optimized for precise parsing by privacy-preserving local GenAI, and a concise companion file that bridges the knowledge gap between the textbook and GenAI, alongside a comprehensive dynamic skill set for specialized tasks. We explain in detail the architectural principles of the modular NuPaD framework, the design philosophy of the Markdown-native textbook format, and the underlying GenAI regulation principles. By redefining the interaction loop between student and model, this framework transforms GenAI from a passive answer engine into an active, personalized tutor, ensuring that it accelerates rather than bypasses the development of deep learning and scientific reasoning.

Oscillator Potts machines (OPMs) provide a physics-inspired, energy-minimization framework for solving combinatorial optimization problems described by the $q$-state Potts Hamiltonian. Although OPMs may be viewed as multistate extensions of oscillator Ising machines (OIMs), here, we show that they exhibit dynamical properties absent in the binary case. Specifically, we derive a configuration-dependent local-stability condition for a recently proposed multiharmonic OPM formulation and show that configurations with the same Potts energy need not be dynamically equivalent. In particular, for $q\geq4$, permutations of the Potts labels can alter the Jacobian spectrum and, consequently, the regularization strength required to locally stabilize a given Potts configuration. Thus, different phase encodings of the same Potts solution can exhibit different local stability properties despite having identical Potts energies.

The asthenosphere's mechanical weakness enables plate tectonics, but its origin is debated. Partial melt has been proposed to cause this softening, yet recent studies suggest that the measured viscosity minimum in basaltic melts, an essential control on melt mobility, is an experimental artifact. Using quantum mechanics-based, machine learning-accelerated molecular dynamics, we extend simulation timescales by more than a factor of 1000 and achieve percent-level precision. We show that basaltic melt exhibits a robust viscosity minimum (approximately 20% below 1-bar values) at approximately 3 GPa, driven by pressure-induced reorganization of aluminum coordination that facilitates shear relaxation while silicon-oxygen polyhedra remain structurally rigid. Our results reveal a depth-dependent rheological transition: melt mobility peaks below approximately 150 km, promoting efficient extraction, but declines sharply during ascent, causing melt to stagnate beneath the lithosphere. This mechanism provides a physical basis for the dual seismic signatures of a melt-depleted deep asthenosphere and a melt-enriched layer near the lithosphere-asthenosphere boundary.

Recovering complete physical fields from sparse observations is challenging because the measurements may not uniquely determine the underlying state. Diffusion-based PDE solvers address this problem through iterative sampling whereas neural operators provide deterministic one-pass predictions. We propose SCOPE (Sparse-Context Observability-aware Predictive Embeddings) to recover complete PDE fields from sparse observations by coupling full-field latent prediction with physical reconstruction. A shared decoder reconstructs fields from both predicted and complete-view representations so that representation learning is guided by both physical recovery and latent matching. We derive a quadratic risk decomposition at fixed teacher-decoder pairs showing why optimal latent prediction need not yield optimal field reconstruction. We also establish sufficient conditions for decoder improvements on complete inputs to transfer to recovery from partial observations. Experiments across five PDE settings show that SCOPE outperforms mask-aware neural operators on all ten forward and inverse tasks and achieves lower errors than those reported for diffusion-based solvers including DiffusionPDE and FunDPS. Decoder-only adaptation further improves recovery without retraining the backbone while retaining deterministic single-pass inference.

Physical-field reconstruction and forecasting depend on both measurement density and spatial layout, yet evaluation under a single observation pattern does not characterize performance when that pattern changes. We introduce PDE-OBS, an integrated benchmarking platform spanning numerical data generation, model training, and inference and evaluation under varying observation conditions. It combines 560,000 fields and trajectories from seven partial differential equation families with configurable observation operators and seven adapted baseline methods for stationary reconstruction and short-horizon forecasting. Separating observation construction from physical records allows users to specify parameterized patterns and deterministic mixtures for training and testing while preserving prediction targets and data splits. The evaluation protocol uses references trained for each test pattern to compare models on identical test observations and targets, alongside equal-count groups for spatial-layout comparisons. On a 14,000-record subset, we evaluate 441 trained models under nine test patterns, yielding 3,969 evaluations. Mean cross-pattern error exceeds mean matched-pattern error in all 49 PDE-method pairs, and this finding persists in a configuration-matched subset of 117 models. Denser test observations do not consistently reduce error for a fixed model. Mixed-pattern training on five completed pairs reduces large single-pattern transfer errors, although destination-trained references usually remain more accurate. Together, the benchmark and findings support systematic evaluation of observation-pattern sensitivity and provide a reusable workflow for developing methods under changing measurement conditions. Code: https://github.com/ru1ch3n/PDE-OBS.

Quantized tensor-train (QTT) compression is incorporated into a full-vector three-dimensional scattered-field finite-difference time-domain (FDTD) formulation on uniform Yee grids. All six electromagnetic-field components, material-dependent update coefficients, equivalent-current sources, and staggered finite-difference operators are represented in compatible QTT form. Gaussian regularization of voxelized material interfaces is used to reduce the coefficient ranks generated by abrupt dielectric and conductivity transitions. The formulation is evaluated for an anatomically heterogeneous human-head model and a homogeneous dielectric sphere on grids containing up to $512^3$ spatial cells. The reported results show that interface smoothing substantially reduces material-coefficient ranks and that the TT–FDTD solution reproduces the full-grid transient fields with pointwise absolute errors on the order of $10^{-4}$ in the examined slices. Compared with conventional FDTD, the tensor representation greatly reduces storage at fine discretizations, although tensor contractions and recompression introduce additional per-step computational cost. These results demonstrate the feasibility and memory–time tradeoff of QTT-accelerated three-dimensional FDTD for large structured-grid simulations.

Buoyant plumes in stratified ambients arise in wildfire smoke transport, ventilation, and pollutant dispersion, where many-query prediction requires models that stay accurate and stable as the dynamics transition from free rise to internal-wave trapping. Operator Inference (OpInf) learns polynomial reduced models from snapshots, and the Boussinesq plume depends affinely on the squared buoyancy frequency $N^2$. However, this compatibility masks two stability gaps: a single affine operator family can distort the per-parameter dynamics into unstable spectra, and for quadratic OpInf models, finite-time blowup is governed by a cubic energy injection channel from the inferred quadratic operator. To address these gaps, we develop cyclic-skew regime-bridged OpInf (CR-OpInf). We derive an affine-inadequacy diagnostic that links the departure from a shared affine family to destabilizing spectral drift before integration. In place of the shared affine fit, we interpolate per-parameter operators with a Gaussian kernel. We also extend the cyclic-skew quadratic constraint to the buoyancy-source-forced parametric setting, closing the cubic energy channel and isolating this closure as the boundedness mechanism. We further introduce a regime-bridging basis split that resolves free rise and trapped intrusion structures while preserving the cyclic-skew structure under convex interpolation. Applied to an unconfined Boussinesq plume and a viscous Burgers problem spanning a smooth-to-shock transition, CR-OpInf remains bounded for five training horizons, avoids the divergence of affine and unconstrained variants, tracks the plume rise, and recovers stratification-dependent rise scaling at moderate horizons. To our knowledge, this is the first parametric OpInf study of an unconfined stratified plume.

Damage prediction in particulate elastomeric composites is challenging due to the strong material heterogeneity, nearly incompressible behavior of the matrix, and the presence of localized stress concentrations arising from matrix-inclusion interactions. In the present work, a computational framework is developed for damage propagation analysis of particulate elastomeric composite structures. The effective constitutive response of the heterogeneous material is first determined using the second-order homogenization method. The resulting homogenized response is subsequently used to identify equivalent hyperelastic material parameters for structural simulations. A three-parameter isotropic damage model is incorporated to capture damage-induced softening. A mixed displacement-pressure finite element formulation, is used to alleviate volumetric locking associated with nearly incompressible property of elastomers. The proposed framework is validated through full-scale simulations and homogenized response involving composites with varying inclusion volume fractions. Numerical investigations demonstrate that the homogenized models is able to reproduce the global force-displacement response within 2% error and damage evolution characteristics of the heterogeneous composites while significantly reducing the computational cost.

The environmental transport of microplastic particles has raised concerns because of their potential ecological and human-health impacts. A particular challenge for experiments on microplastic transport in soils is the fact that soil is a highly complex and non-transparent porous medium. Thus, our knowledge of transport mechanisms of microplastics in soils and related ecological implications is still limited and computational simulations offer an additional possibility to obtain important insights into the physical transport behaviour of microplastics. Yet, direct particle-resolved simulations in realistic soil structures for macroscopic sample sizes remain a computational challenge. We introduce an efficient pore-network model, minD-PNM, that predicts particle breakthrough curves in water-saturated soils using experimental X-ray micro-computed tomography ($\mu$CT) pore geometries as the only input. The model combines an approximately $t^{-3}$ pore-scale transit time distribution with a minimum-dissipation partition of multi-inlet-multi-outlet pores, and propagates these distributions through the pore network to predict breakthrough curves. We benchmark minD-PNM against lattice Boltzmann and immersed boundary simulations for small systems, and against microplastic column experiments in quartz sediments for macroscopic sample sizes. In both cases, the predicted breakthrough curves agree well with the corresponding reference data. Because it requires only geometric information, minD-PNM scales to macroscopic sample volumes offering a practical tool for contaminant-hydrology studies of particulate pollutants.

MnBi$_2$Se$_4$, MnSb$_2$Se$_4$, and MnSb$_2$Te$4$ are layered magnetic quantum materials of interest for spintronic and topological electronic applications, where control of magnetic order at the atomic scale is essential. These materials are particularly sensitive to strain. We investigate their strain-dependent magnetism in the monolayer limit using first-principles calculations across the full two-dimensional strain space spanned by independent variations of the two in-plane strain components. Our results show that the Mn-projected local moments depend primarily on the volumetric strain, whereas the magnetic ground state, exchange interactions, magnetocrystalline anisotropy, and ordering temperature show pronounced directional and composition-dependent responses. We also observe strain-induced competition between the phases, with MnBi$_2$Se$_4$, and MnSb$_2$Se$_4$ monolayers exhibiting a more diverse range of competing magnetic states, while MnSb$_2$Te$_4$ monolayer remains ferromagnetic over a larger portion of strain space. MnSb$_2$Te$_4$ also exhibits a substantially larger out-of-plane magnetocrystalline anisotropy, while showing a weaker dependence on strain than the Se-based materials. The calculated magnetic ordering temperature maps further highlight the role of two-dimensional strain to tune thermal magnetic stability in these materials. These results establish magnetoelastic trends across this family of Mn-based chalcogenide monolayers.

We analyze the moment hierarchy of a Callaway lattice Boltzmann model for phonon hydrodynamics and derive exact steady-state solutions for a driven planar channel with diffuse walls. The collision operator decomposes the population space into conserved, heat-flux, and fast kinetic sectors, whose coupling through streaming determines the transport response. For the two-dimensional, eight-velocity (D2Q8) lattice Boltzmann model, we derive a fully discrete closure that relates the heat flux to transverse kinetic moments. The higher-order transverse kinetic moment contains both a second-difference term and an explicit contribution from the imposed energy drop. Diffuse-wall population constraints then determine the heat-flux and kinetic-moment profiles without fitted parameters. The resulting solution reproduces the numerical bulk moment amplitudes and wall slip, while an exact wall relation separates the contribution of the higher-order moment from deviations from the leading gradient approximation. Finite-wave-number analysis further resolves the collision-sector mixing associated with longitudinal propagation. These results provide a unified description of collision relaxation, the moment hierarchy, and boundary response within the specified discrete model.

We present a statistical evidence of a temporal correlation between ionospheric storm-enhanced density (SED) and plasmaspheric plumes during magnetic storms. We identified SED plumes in 75 storms between 2010-2024 with sufficient total electron content (TEC) coverage. We also identified plasmaspheric plumes during these storms using a plasmapause asymmetry (PPA) index derived from the machine-learning-based DEN3D model. We found that all SED plumes coincided with plasmaspheric plumes. The start and end times of both phenomena exhibit near-zero lag within a half-hour delay, confirming their synchronized evolution. For the first time, superposed epoch analyses reveal that plume onsets align with solar wind driving and geomagnetic activity, including peaks in the electric field, coupling function, auroral electrojet indices, and the asymmetric ring current index, all of which facilitate plume formation. This work provides timely new insights into magnetosphere-ionosphere coupling processes and demonstrates the utility of machine-learning-based diagnostics for space physics/weather.

We propose a crack-geometry-based model for permeability evolution in rocks under dynamic loading. Accurate representation of permeability is important for fluid extraction and containment in geological formations, but remains challenging because low-porosity brittle rocks are highly sensitive to changes in pore geometry and connectivity, while dynamic permeability measurements are limited. Rock void space is represented as an evolving network of penny-shaped cracks with prescribed orientations. Permeability changes are described through crack microvariables including aperture, length, and crack distance. The model accounts for crack opening and closure, fracture-energy-driven propagation, plastic-strain-driven nucleation, coalescence after full connectivity is reached, and strain-rate-dependent fracture toughening. The evolving crack network directly modifies connectivity, fluid conductance, and preferential flow paths. The formulation is implemented in the GeoDyn hydrocode and evaluated under tension, shear, compression, and symmetric impact loading. Results show a strong relation between permeability and crack length, with crack propagation playing a major role in permeability enhancement. Permeability growth can nevertheless be limited by poor connectivity and crack closure. The model captures changes of several orders of magnitude depending on stress state, strain rate, crack orientation, and connectivity, consistent with observed behavior of brittle rocks under high strain-rate loading.

Grain growth and fragmentation through dust collisions in protoplanetary disks are strongly coupled with dust dynamics. This is because the spatial and velocity distributions of dust set the collision rates and outcomes, while dust grain sizes set the aerodynamic coupling with gas. However, existing dust evolution codes often rely on prescribed dust velocities and are mostly restricted to one or two spatial dimensions. In this work, we present GameDev, a dust evolution model accelerated by graphics processing units (GPUs) that couples independently evolved three-dimensional dust dynamics with Monte Carlo collisional evolution of dust. In GameDev, we model dust as Lagrangian representative particles in a prescribed gas disk. We integrate particle trajectories with the staggered semi-analytic method, which remains accurate for both tightly and weakly coupled dust grains. We estimate collision rates from each particle's nearest neighbors rather than on a grid, preventing physically close particles from being excluded from collision sampling because they lie across a grid boundary. Most importantly, we sample collision events against neighborhoods that remain frozen over adaptive intervals, enabling massive parallelization across particles. These implementations make GameDev an accurate and efficient tool for studying dust evolution in protoplanetary disks. GameDev supports both NVIDIA CUDA and AMD ROCm platforms, provides a smaller-scale standalone Eulerian dust fluid model, and is publicly available on GitHub.

Solving large ensembles of small, independent ordinary differential equations (ODEs) is a common task in computational science and engineering, arising for example in Bayesian parameter estimation, Monte Carlo uncertainty quantification, and the integration of uncoupled physical systems such as the linearised Einstein-Boltzmann equations of cosmology. Modern graphics processing units (GPUs) with thousands of cores are well suited to such parallel workloads, but realising this potential requires careful attention to GPU-specific architectural constraints. We present modax, a Python library of GPU-accelerated ODE solvers specifically optimised for solving large ensembles of low-dimensional, independent problems. Our solvers are compatible with the JAX ecosystem but are written in a low-level, thread-based programming paradigm better suited to ensembles of highly divergent trajectories than their pure-JAX counterparts. Benchmarks on the Lorenz, Robertson and van der Pol lattice systems compare modax against diffrax, DiffEqGPU$.$jl and torchdiffeq across ensemble size, dimensionality, and trajectory divergence. Our explicit Tsit5 solver achieves 30x speed-up over diffrax on Lorenz systems, and our implicit Rodas5P solver achieves over 700x speed-up on Robertson systems, demonstrating the superiority of linearly implicit Rosenbrock-Wanner methods on GPUs. Our implicit solver scales well to higher dimensions thanks to the use of sparsity-exploiting techniques and data structures. We conclude by applying modax to three examples from the field of cosmology - Bayesian parameter estimation from primordial abundances, Monte Carlo uncertainty quantification of the global 21cm signal, and the computation of uncoupled Fourier modes in the primordial power spectrum - and demonstrate significant efficiency improvements in our ability to model physical phenomena described by low-dimensional (<200D) ODEs.

Density functional perturbation theory (DFPT) is an established framework for the computation of derivatives in plane-wave density functional theory (DFT). We present an implementation of DFPT for exchange-correlation (XC) functionals $E_\mathrm{xc}(\rho,\tau)$ that incorporate an explicit dependence on both the density $\rho$ and the kinetic energy density $\tau$. This covers the popular class of semilocal meta-generalized gradient approximations (meta-GGAs) as well as broader nonlocal parametrizations. We sidestep the derivation of cumbersome XC second energy derivative expressions by recasting these derivatives as a Jacobian-vector product of the XC potentials, which we evaluate with algorithmic differentiation (AD) techniques. Integration with our previously developed AD-DFPT framework [N. F. Schmitz et al., npj Comput. Mater. 12, 6 (2026)] provides access to derivatives of arbitrary ground state quantities with respect to arbitrary perturbations. We employ AD-DFPT to compute a range of response properties for ZnO and BaTiO3, and find that the recent r2SCAN01 meta-GGA functional generally outperforms LDA and PBE. Finally, we showcase the optimization of a neural-network meta-GGA to self-consistently reproduce hybrid-DFT reference densities of bulk silicon, using AD-DFPT gradients. Overall, these results establish AD-DFPT as a versatile route for computing DFT derivatives at the meta-GGA level, be they common response properties or the unusual derivatives required for the gradient-based training of novel XC functionals.