Papers

Piston-driven shock waves in dusty plasma monolayers have been observed experimentally and studied using molecular dynamics (MD) simulations. However, previous MD simulations were restricted to strictly two-dimensional geometries with periodic or reflecting boundaries and therefore could not capture the out-of-plane buckling of microparticles observed experimentally under shock compression. We present three-dimensional MD simulations of a piston-driven shock in a two-dimensional dusty plasma. The model incorporates finite-harmonic vertical confinement, fixed boundaries, Epstein drag and a microparticle size distribution matched to previous experiments. The simulations recover the experimentally observed linear scaling between the shock and piston Mach numbers and, to the best of our knowledge, reproduce, for the first time in MD, shock-induced buckling of the monolayer. These results bridge a long-standing gap between laboratory observations and simulations of two-dimensional dusty plasma shocks, and provide a validated framework for investigating out-of-plane and wake-mediated shock physics in strongly coupled systems.

First, we present a new theory of "modular natives." A modular native is a basic building block that is born modular, e.g., a solar cell. The theory predicts that using modular natives in building things reduces complexity and improves predictability, resulting in better outcomes and faster scale-up. Second, we test the theory on the largest dataset of its kind. We find, at a high level of statistical significance, that modular natives operate under a fundamentally different risk regime than other project types, with finite and predictable risk, in contrast to non-natives that have infinite and unpredictable risk. The findings help explain why modularity is key to successful building while bespokeness often leads to failure. Third, we relate our findings to economic and geopolitical development, arguing that China understands modular natives and scale-up better than any other geography and that this is key to China's swiftly growing dominance in renewables, batteries, EVs, robots, etc. We argue that China's mastery of modularity and scale-up is a major innovation in its own right, among the greatest and most impactful in human history, falsifying the common notion that China cannot innovate. Business and government outside China ignore these findings at their peril. Finally, we spell out policy and practice implications and identify areas for further research.

Densities and excess molar volumes ($V_{\text{m}}^{\text{E}}$) at (293.15-303.15) K and excess molar enthalpies ($H_{\text{m}}^{\text{E}}$) at 298.15 K are reported for 2-undecanone + heptane, or + octane, or + decane, or + dodecane, or + tetradecane mixtures at 95 kPa. Densities and $H_{\text{m}}^{\text{E}}$ were measured using, respectively, a densitometer Anton Paar DMA 602 and a Tian-Calvet micro-calorimeter. $H_{\text{m}}^{\text{E}}$ results are positive, indicating that interactions between like molecules are dominant. Both $H_{\text{m}}^{\text{E}}$ and $V_{\text{m}}^{\text{E}}$ increase in line with $n$ (number of C atoms of the alkane), which reveals that the increase of $V_{\text{m}}^{\text{E}}$ can be ascribed to an increased interactional contribution. Nevertheless, systems with $n$ = 7,8 show negative $V_{\text{m}}^{\text{E}}$ values, which reveals that $V_{\text{m}}^{\text{E}}$ is determined mainly by structural effects. Isochoric excess molar internal energies ($U_{V\text{m}}^{\text{E}}$) at 298.15 K have been obtained from the present $H_{\text{m}}^{\text{E}}$ and $V_{\text{m}}^{\text{E}}$ data. At equimolar composition, $U_{V\text{m}}^{\text{E}}$is nearly constant for $n$ = 7-10, and then slightly increases. This has been explained in terms of a possible folding of 2-undecanone. From the comparison of $U_{V\text{m}}^{\text{E}}$ results for similar systems involving n-alkanoates, it is shown that folding is more likely in solutions with these compounds.

The deterministic unified gas-kinetic scheme (UGKS) provides a multiscale framework for nonequilibrium gas dynamics, but its high-dimensional phase-space discretization leads to severe memory pressure and communication overhead, especially on large unstructured meshes. This paper presents a GPGPU-oriented UGKS with velocity-block pipelining and full phase-space MPI decomposition. In the proposed formulation, the discrete velocity space is partitioned into fixed-size velocity blocks for accelerator execution, while MPI ranks are organized into coupled physical-space and velocity-space communicators. As a result, each rank stores and advances only a local physical subdomain together with a contiguous subset of velocity blocks, and macroscopic moments are recovered through lightweight reductions over the velocity-space communicator. To improve concurrency and reduce exposed communication cost, a triple-buffered pipeline is further developed to overlap microscopic reconstruction, physical-halo exchange, nonequilibrium flux evaluation, and the first-stage distribution update during the local velocity-block sweep. The implementation targets SIMT-based GPGPU accelerators through a portable device-runtime abstraction. Numerical experiments demonstrate that the $P_v=8$ configuration achieves a $33.4$–$35.4\times$ strong-scaling speedup on 64 nodes, while an Orion-like capsule simulation reaches approximately $1.33\times10^{11}$ phase-space degrees of freedom on 4096 GPGPU accelerators. These results indicate that the proposed method preserves the original UGKS flux construction and two-stage time discretization, while substantially reducing microscopic storage per rank and improving the scalability of large unstructured phase-space simulations.

Optical skyrmions, which are the topologically protected quasiparticles and characterized by the nontrivial polarization textures, have emerged as a promising candidate due to their potential applications in optical communication, data storage, and particle manipulation. In this article, we propose and experimentally demonstrate an efficient and tunable approach for the dynamic transformation of generalized optical skyrmionic textures through the interaction of structured vector vortex beams with chiral media. By controlling the enantiomeric excess of an optically active material, we achieve on-demand conversion among Bloch, Neel or any intermediate skyrmionic states, extending also to optical bimerons. The topological conservation of the skyrmion number proves its robustness towards even higher-order textures. While maintaining a common path and stable setup, the proposed methodology provides an efficient and cost-effective approach towards the flexible manipulation of the topological textures, paving the way towards the understanding of topological transformation and engineering optical skyrmions for information processing or particle manipulation.

Splitting an ultrashort drive pulse into a weak leading pulse and a strong main pulse is known to raise the energy of protons accelerated by the target-normal-sheath-acceleration (TNSA) mechanism, because the leading pulseforms a preplasma that increases the absorption of the main pulse. The allocation of energy between the two pulses and their temporal separation are coupled control parameters, and under a fixed total energy they have not been optimized jointly in a systematic way. We address this problem with two-dimensional particle-in-cell simulations driven by Bayesian optimization. Treating the prepulse energy fraction $r$ and the interpulse delay $\Delta t$ as free parameters under a fixed total energy, a campaign of 32 simulations, of which 16 are Sobol-initialized and 16 adaptively selected, locates an optimum at $r\approx0.07$ and $\Delta t\approx234$~fs. The proton cutoff energy increases from 7.7~MeV for the single pulse to 17.7~MeV at the optimum, a gain of about 130%. The optimum is asymmetric with only about 7% of the energy in the leading pulse. At the optimum the laser absorption rises from 4.84% to 20.09%, the bulk hot-electron temperature from 1.20 to 1.94~MeV, and the time-integrated rear sheath field by a factor of about 1.7. The optimum lies on a broad plateau in $\Delta t$, which relaxes the timing tolerance required in an experiment.

Determining the spatial distribution of heat-producing elements (HPEs) within the Earth is critical for understanding the planet's thermal and chemical evolution. A central debate is whether the deep mantle, particularly the Large Low-Velocity Provinces (LLVPs), retains anomalous, radiogenically enriched reservoirs. While mapping surface variations in geoneutrino flux offers a direct probe of Earth's internal radioactivity, current continental-located detectors measure only the angle-integrated flux. This limitation creates a fundamental parameter degeneracy, rendering it impossible to distinguish a chemically homogeneous mantle from a heterogeneous one. In this study, we quantify the potential of directional geoneutrino detection to overcome this limitation. By evaluating realistic LLVP geometries under the experimental framework of the proposed Ocean Bottom Detector (OBD), we demonstrate that resolving the incoming direction of geoneutrinos can successfully break the non-uniqueness inherent in rate-only measurements. These results indicate that future directional geoneutrino measurements could help determine whether LLVPs host enhanced HPE abundances and assess their contribution to Earth's radiogenic heat budget. Such measurements would provide a new observational constraint on the chemical heterogeneity of the deep mantle and its role in Earth's long-term thermal evolution.

Second-order nonlinear processes involving counter-propagating light generation have been explored for quantum applications and optical parametric oscillators. However, realizing these processes on integrated photonic platforms such as thin-film lithium niobate (TFLN) remains challenging because of the extremely short quasi-phase matching (QPM) periods required, which are difficult to achieve with standard fabrication workflows. In this work, we achieve 1425 nm periodicity on 800 nm-thick X-cut TFLN via poling prior to etching. We present the first integrated demonstration of backward-wave difference-frequency generation (BWDFG), using a pump near 775 nm and a counter-propagating signal near 1980 nm, and thereby combine the high nonlinear efficiency of QPM with favorable dispersion characteristics. Within the same waveguide, we demonstrate backward-wave second-harmonic generation (BWSHG), where the pump and second-harmonic are counter-propagating, together with BWDFG. Idler generation spans from 1244 nm to 1290 nm and simulations predict extended coverage up to ~2200 nm. This process provides broad spectral tunability while remaining tolerant of fabrication-induced dimensional offsets, addressing a key challenge for the TFLN platform.

The conventional Floquet engineering scheme synthesizes a given target Hamiltonian with a deterministic temporal periodic driving field. In this work, we introduce the stochastic Floquet engineering scheme that can synthesize an arbitrary non-Hermitian target Hamiltonian using a time-periodic driving field with noisy amplitude. Our method is rooted in the Hermitian dynamics taking noise as a valuable quantum resource with no need for loss or gain in prior. We apply our method to engineer a cavity Hamiltonian with dissipative coupling between Fock states, and to prepare a given quantum state from a generally arbitrary quantum state. The stochastic Floqut engineering also provides a way to generate non-unitary quantum gates, which take advantage in certain tasks compared to unitary quantum computing, without the need for ancillae or state-dependent updating.

Addressing the critical trade-off between initial Coulombic efficiency (ICE) and reversible capacity in hard carbon anodes for Na-ion batteries (NIBs), we introduce a novel coupling strategy that combines carbon layer orientation reconstruction with closed-pore construction to produce hard carbon with an ultra-low specific surface area. We demonstrate that the nanographite domains within the hard carbon precursor undergo entropy-driven orientation reconstruction through the synergistic regulation of heteroatom doping and medium-temperature carbonization. This process not only increases interlayer spacing and promotes structural disorder but also enables the formation of dense, closed pores and ultramicropores at domain boundaries via confined atomic migration, while simultaneously encapsulating surface open pores within internal closed ones. Due to this unique pore architecture, our hard carbon exhibits an ultra-low specific surface area of 1.89 m2 g-1 with a markedly higher proportion of closed pores. As a result, our hard carbon achieves a remarkable reversible capacity of 342.3 mAh g-1 at 20 mA g-1, with an exceptional ICE of 90.4% and a dominant plateau capacity of 262.3 mAh g-1 (76.6%) for NIBs. We believe this coupling strategy provides a new paradigm for the structural engineering of high-ICE anode materials in advanced NIBs.

Compression and heat release interact intermittently in high-speed reacting shear layers, and whole-field averages can obscure their coupling. We examine this coupling in a supersonic reacting hydrogen–air temporal mixing layer using time-resolved mid-plane slices from a three-dimensional direct numerical simulation. A fixed dilatation threshold identifies connected compression events, and exothermic heat release and mixture-fraction-gradient activity are then conditioned on the evolving event population. The record separates into startup ($t^*<5$), transition ($5\le t^*<20$), and developed ($t^*\ge 20$) regimes, with the developed regime carrying the persistent compression–chemistry interaction. In this regime, compression appears as a population of intermittent events with no single structure dominating the field. Stronger exothermic response is associated with larger maximum-event area, larger event count, greater compression–heat-release overlap, and smaller distance from compression to the most exothermic regions. Scalar-gradient amplification peaks near zero lag relative to compression-area excursions, whereas the strongest exothermic response precedes peak compression coverage by $\Delta t^*\approx -0.85$. These results show that compression organizes chemistry most clearly through event population, overlap, proximity, and lag, providing an event-level description of compression–chemistry coupling in an open supersonic reacting shear layer.

Compressibility and chemical reactions in reacting flows provide source mechanisms for pressure fluctuations whose signatures depend on the flow, source distribution, and acoustic environment. Bounded flows can sustain strong feedback and narrowband tones, whereas boundary-free flows more often exhibit broadband source activity distributed across frequency. Near-field combustion-noise source dynamics are examined in a supersonic reacting hydrogen-air temporal mixing layer using high-fidelity time-resolved direct numerical simulation data. Pressure, heat-release, and dilatation fields are used to identify how localized reacting structures, compressive disturbances, broadband spectral content, and burst-driven temporal organization interact. The results show weak pressure–heat-release coherence concentrated within selected low-frequency bands, with no collapse onto a single dominant mode. Combustion intermittency modulates the near-field pressure response within these bands and is accompanied by burst-driven amplitude modulation and transient trajectory sensitivity, while the overall dynamics remain broadband and bounded. A planar source-radiation-potential projection further shows moderate low-frequency angular bias associated with the heat-release source distribution. Information-theoretic measures indicate that pressure fluctuations share more statistical structure with heat release than with dilatation. The analysis characterizes source-side organization and near-field pressure response in the sampled DNS of a reacting shear layer, providing a basis for subsequent observer-based or acoustic-analogy radiation calculations.

Particle-based Fokker–Planck (FP) models provide an efficient kinetic alternative to direct simulation Monte Carlo (DSMC) in slip and early transitional gas flow regimes, but advanced cubic-FP closures require repeated cell-wise moment evaluation and small dense linear solves. This work develops and validates a GPU-native neural surrogate that replaces the deterministic cubic-FP closure calculation inside the particle simulation loop. The trained weights are evaluated directly with batched CuPy operations, avoiding CPU–GPU transfers during online deployment. The validation emphasizes quantitative evidence: component-level runtime profiles, break-even cost analysis including offline costs, conservation and stability diagnostics, particle-per-cell sensitivity, a direct time-averaged coefficient audit, and covariance-based entropy-proxy fidelity checks. The Couette case is retained as a compact, dimensionless verification problem, while the main internal-flow validation is a 2D lid-driven cavity tested by complete simulation conditions, including unseen moderately rarefied cases at nominal $Kn=0.5$ and $Kn=1.0$. For the hypersonic cylinder, a particle-moment covariance-based entropy-fidelity audit is performed on the front stagnation line and in the cell-centered near-wall gas layer. The same deployed neural $C/\Gamma$ closure used for the cylinder flow fields closely reproduces the equilibrium and Gaussian kinetic entropy profiles over the reported front-line and near-wall gas bins; these profiles are used as a relatively exact-FP/ML-FP audit. The study establishes GPU-native learned closure as a practical route to accelerating cubic-FP rarefied-flow solvers, delivering substantial online speedups while retaining the macroscopic, high-order, and entropy-proxy structure of the reference kinetic model.

Quantum illumination provides a quantum advantage in detecting weakly reflecting objects embedded in a noisy environment, even when environmental noise destroys most of the initial entanglement. We investigate this advantage using Haar-random probe states constrained to symmetry-resolved subspaces. Employing tools from quantum channel discrimination and asymptotic hypothesis testing, we derive the discrimination exponents associated with Haar-random probe ensembles and identify the role of symmetry in determining their performance. We show that typical states drawn from fixed-charge sectors achieve the same asymptotic quantum-illumination advantage as maximally entangled probes. In particular, we show that the effective thermal-noise suppression and the corresponding Chernoff exponent are governed by the dimension of the accessible symmetry sector. Our results reveal that the operational resource underlying quantum illumination can be generalized from fine-tuned structure of a specific probe state to the existence of a large symmetry-protected correlation subspace. These findings establish a direct connection between quantum illumination, symmetry-resolved typicality, and quantum channel discrimination, and demonstrate that near-optimal quantum hypothesis testing resources can emerge naturally from generic many-body quantum states constrained by conservation laws.

Block copolymers self-assemble into ordered nanostructures whose geometry is governed by a competition between interfacial energy and chain conformational entropy. While this competition produces a rich sequence of morphologies, topologically complex “frustrated” phases, such as the primitive cubic network and single networks, incur severe packing penalties and are difficult to access in neutral systems. Here we show that ions functionalized at the termini of one block in an AB diblock copolymer melt introduce a qualitatively new stabilization mechanism. Strong ion correlations drive chain-end association and generate a curvature preference toward the charged domain; the resulting tendency of end-localized ion clusters to adopt compact, curved geometries selectively favors the highly frustrated single-primitive-cubic network ($Pm\bar{3}m$) and single-gyroid network ($I$4$_1$32) over the classical phases, in a region of parameter space with a segregation strength lying below the order-disorder transition of the neutral system. Free energy decomposition reveals that the electrostatic energy, arising almost entirely from beyond-mean-field ion correlations, becomes increasingly negative with increasing interfacial curvature. In the primitive cubic network, pronounced local segregation of ions into the cylindrical struts generates compact curved clusters whose correlation energy gain more than offsets the enhanced packing frustration, so the very geometry that is the source of packing frustration in neutral systems becomes the source of its stability here. Increasing ion size weakens correlations and suppresses the network phases, consistent with experimental observations. Our results establish curvature-selective end-group association as a general principle for accessing frustrated topologies in block copolymer systems.

Understanding how higher-order social structures shape epidemic spreading requires models that couple group interactions with adaptive behavior. We introduce an adaptive simplicial susceptible-infected-susceptible (s-SIS) model on d-uniform hypergraphs, where both node states and hyperedge activity co-evolve in response to local infection pressure. Hyperedges represent group interactions of fixed size and dynamically reduce their activity through a feedback mechanism in highly infected environments. Within this framework, we design two classes of hyperedge-level interventions: (i) risk-driven immunization, combining spontaneous, activity-based isolation with targeted deactivation guided by hyperedge infection pressure, and (ii) structural rewiring, which reconstructs group structures either randomly or via degree-preferential attachment. By extending the microscopic Markov chain approximation to higher-order interactions, we derive analytical conditions for the existence and stability of both endemic and disease-free stationary states. Our analysis shows that adaptive hyperedge feedback can induce discontinuous phase transitions, nonlinear epidemic thresholds, and bistable regimes in which sufficiently high initial prevalence drives the system to a disease-free equilibrium. Extensive Monte Carlo simulations support the theory and confirm that targeted immunization and degree-preferential rewiring substantially suppress epidemic prevalence, outperforming random strategies. These results demonstrate that higher-order interactions and adaptive group-level responses fundamentally reshape epidemic bifurcations and suggest principles for designing effective intervention policies in complex social systems.

A central challenge in mesh-free space–time approximation for time-dependent partial differential equations is to represent evolving temporal scales while keeping residual minimization computationally tractable. Random feature methods simplify this algebraic problem by freezing nonlinear trial functions and fitting only a linear readout, but standard static space–time activations provide no explicit relaxation-scale mechanism, making temporal-scale resolution a finite-dimensional bottleneck in stiff, dispersive, or multi-scale regimes. We introduce liquid random feature methods (L-RFM), which replace static temporal activations by closed-form liquid time-constant responses with sampled relaxation scales. The resulting frozen features form temporally structured local or global trial spaces with analytic space–time derivatives for residual least-squares assembly. A density theorem proves density of the deterministic trial spaces in the continuous space–time function class, and a temporal-rank calculation clarifies the role of sampled relaxation scales. Ablation and finite-feature tests identify the liquid temporal response as the primary source of the observed accuracy improvement. Across stiff reaction–diffusion, nonlinear transport, dispersive, complex-valued, and multidimensional benchmarks, L-RFM improves finite-feature accuracy in regimes where temporal-scale representation controls the approximation. By embedding relaxation scales directly into frozen trial functions, L-RFM provides a route to high-accuracy continuous space–time surrogates for evolutionary PDEs while preserving the simplicity of linear least-squares solvers.

In this work, we present a simple, exact, and fully analytical method for generating light sheets parallel to the propagation direction, with amplitude and phase envelopes structured on demand. We validate the approach theoretically and experimentally by imprinting images onto light sheets, and we compare the theoretical performance with that obtained using an alternative strategy based on arrays of Frozen Waves (FWs). In this context, the proposed method provides a more direct and flexible control of the field envelopes on the light sheets, resulting in higher-fidelity reconstructions than those achieved with FW-based approaches. The method thus offers a versatile framework for structured light-sheet generation, with potential applications in optical manipulation, microscopy, and 3D holographic imaging.

Adjoint formulations of magnetohydrodynamic (MHD) equilibrium are presented for sensitivity analysis and optimization-based equilibrium calculations. We derive the adjoint of the three-dimensional equilibrium equations under fixed-boundary conditions and analyse its axisymmetric reduction. The reduced operator coincides with the perturbed Grad-Shafranov operator, thereby making explicit the connection between the threedimensional adjoint construction and the axisymmetric adjoint problem. The resulting framework provides a basis for relating adjoint structures in both axisymmetric and fully three-dimensional ideal MHD equilibrium.

Trapped-ion quantum simulators realize programmable spin models through phonon-mediated interactions. For Hamiltonians with noncommuting terms, however, the same phonon bus generates intrinsic spin-phonon errors that strongly distort the target dynamics. Because these errors are governed by the full time history of the spin-dependent phonon motion, they survive standard loop-closing control and limit simulation accuracy. Using a sequence of frame transformations, we isolate the residual error dynamics and show that this intrinsic error can be strongly suppressed while preserving programmable Ising couplings. Full spin-boson simulations of multi-ion chains demonstrate orders-of-magnitude lower error than both constant-drive and conventional loop-closing protocols. These results remove a central precision barrier in trapped-ion analog quantum simulation and enable accurate programmable simulation of noncommuting many-body Hamiltonians and dynamical protocols.

Thin wall treatments modify high-speed boundary-layer instability through the pressure they admit or absorb at the wall. This paper develops a unified admittance formulation for such effects on trapped compressible Rayleigh modes. For a simple rigid-wall eigenpair, we prove the spectral sensitivity law \[ c(A)=c_0+KA+\mathcal O(|A|^2), \qquad \delta\sigma=\alpha\Imag(KA)+\mathcal O(|A|^2), \] where \(A\) is the wall admittance and \(K\) is an explicit functional of the rigid-wall eigenfunction. The formula separates wall physics from outer-mode physics and yields a phase criterion for stabilisation. Matched asymptotics show that viscous and thermal wall layers, blind-pore coatings and shallow non-separating roughness all reduce to this same boundary condition, with additive leading admittances. Mach-4.5 computations validate the sensitivity coefficient and demonstrate porous damping, viscous-wall damping and sign-changing reactive roughness effects.

Simulating strongly correlated molecular systems on near-term quantum hardware remains challenging because current hardware offers limited quantum volume and moderate-fidelity qubits. One way to address this challenge is through bootstrap embedding (BE). Bootstrap embedding partitions molecules into smaller fragments that are then embedded in the “bath” of other fragments iteratively. Bootstrap embedding is appealing for quantum simulation because fragmentation reduces the qubit requirements of each fragment problem. In this work, we develop a quantum bootstrap embedding (QBE) workflow that uses variational quantum eigensolver (VQE) fragment solvers and examines the algorithmic choices that determine the success of the overall VQE-QBE procedure. To improve efficiency, we introduce FastAdaptVQE, a sparse-matrix-accelerated form of the adaptive variational quantum eigensolver (ADAPT-VQE) that replaces symbolic commutator evaluation with direct statevector calculations, and MatrixFreeAdaptVQE, a matrix-free extension that removes the sparse-matrix memory bottleneck that arises for larger fragments. We also explore modifying the ADAPT-VQE operator-selection step by replacing the purely greedy choice with a lookahead strategy. Benchmarks on H$_4$, F$_2$, and selected geometries of H$_6$ reach chemical accuracy, within 1 kcal/mol of BE results obtained with a full configuration interaction (FCI) solver. For $n$-butane, density-matching residuals converged, but energy errors remained outside chemical accuracy. These results show that combining QBE with VQE can accurately compute molecular energies, while density convergence alone does not ensure energy accuracy. This work lays the foundation for extending these energy calculations to larger molecular systems and quantum materials on near-term quantum hardware.

Real-time reconstruction of magnetohydrodynamic equilibria is essential for plasma shaping, stability assessment and feedback control in magnetic confinement fusion. However, Grad-Shafranov equilibrium calculations remain largely device-specific and iterative, limiting their use in latency-constrained control settings. Existing neural approaches can accelerate individual equilibrium predictions, but they do not generally provide reusable models across changing plasma boundaries or tokamak geometries. Here we show that equilibrium reconstruction can be recast as a cross-device operator learning problem. We develop a domain-specific neural operator framework that maps geometry and profile parameters directly to the poloidal flux field, replacing repeated solve-on-demand computation with amortized operator inference. Using the analytically tractable Solov'ev family as a controlled Grad-Shafranov testbed, we generate equilibria across eight geometrically distinct tokamak-like configurations and benchmark five neural operator architectures under four transfer-learning strategies. Single-geometry pretraining gives poor transfer to unseen devices, whereas multi-geometry pretraining enables data-efficient adaptation. The Wavelet Neural Operator gives the strongest cross-geometry performance, reaching mean relative L2 errors below 4% with 100 labelled target equilibria and below 2% with full fine-tuning. The predicted magnetic fields satisfy the divergence-free constraint to numerical precision, and four architectures achieve millisecond or sub-millisecond inference. These results identify neural operator pretraining as a route towards reusable, real-time equilibrium inference across fusion device configurations.

Rare events govern many important molecular processes but remain difficult to characterize within accessible simulation timescales. This challenge has motivated machine-learning methods to construct low-dimensional collective variables for specific objectives, including state discrimination, slow-mode identification, and committor-function approximation. Here we introduce SelfTICA, a self-supervised framework that uses contrastive learning on time-lagged configurations to learn a latent representation of the slow modes governing relevant transitions. Once learned, this representation is frozen and reused across downstream tasks, including collective variables construction, enhanced sampling, free-energy estimation, and committor learning. Compared with direct slow-mode optimization, SelfTICA improves training stability and provides collective variables even from limited and exploratory trajectories, accelerating rare-event sampling and enabling accurate free-energy estimation. The dynamical information encoded during pretraining also accelerates committor learning and enables characterization of transition-state regions. These results show that contrastive learning of slow dynamical representations provides a common foundation for rare-event sampling and mechanistic analysis.

In this work, we extend the method of averaged Lagrangian to the study of the general second-order (non-conservative) extended Korteweg–de Vries equation, known as the eKdV equation. Building on the framework introduced in [18], we construct a master (augmented) Lagrangian, modeled on Luke's Lagrangian, that incorporates the governing constraints at the appropriate asymptotic orders via the method of Lagrange multipliers. Averaging the resulting Euler-Lagrange equations in the traveling wave setting yields the existence of a (single) solitary wave solution with a $\operatorname{sech}^2$ profile. Explicit second-order formulas are obtained for the height of the solitary wave, together with the solitary wave velocity and inverse width, in terms of a fixed amplitude parameter. A key feature of the derived expressions is their asymptotic reduction to the classical KdV results when the first-order terms are retained. To assess the robustness and utility of the variational solitonic solutions, the derived formulas are subsequently applied, via the dispersive shock equal amplitude approximation method, to estimate the height and velocity of the leading solitary wave edge of dispersive shock waves governed by the eKdV Riemann problem. Theoretical predictions for the relevant wave parameters in both the eKdV solitary wave and dispersive shock wave problems are compared with direct numerical simulations and found to be in strong agreement.

The global food system contributes to tens of millions of years of human life lost every year through unhealthy diets, and accounts for roughly a third of greenhouse gas (GHG) emissions. Researchers have thus often sought opportunities to simultaneously redress these large impacts on human health and climate; "dietary shifts" are frequently identified as key to both improving health and reducing land-related GHG emissions. Here we develop a new, spatially-explicit, detailed model of the global food system, and use it to highlight enormous and low-cost opportunities to both improve dietary health and reduce food system emissions. However, we find that health and climate goals are surprisingly independent; for example, reducing red meat intake benefits climate, but may only modestly improve health outcomes. Similarly, there is very large potential both to achieve healthier diets that do not lead to emissions reductions and to reduce emissions without dietary change. Our results highlight both the potential for, and the separability of, food-related policies that advance public health and climate goals.

Direct dark matter detection experiments require large-scale photon sensing arrays with hundreds of thousands of synchronized readout channels. Silicon photomultipliers (SiPMs) have emerged as a leading candidate for these detectors due to their high integration density, low bias voltage, and superior radiopurity. However, existing digital SiPM readout architectures struggle to simultaneously preserve nanosecond-level temporal resolution for sparse scintillation events and sustain data integrity during high-intensity photon bursts. We propose DarkFlow, a hierarchical digital SiPM architecture that features local data aggregation, compact relative-time encoding, consumer-driven backpressure, and occupancy-aware eDRAM burst buffering within a unified dataflow framework. We show that DarkFlow maintains ultra-low packet loss at billion-photon event rates, where conventional architectures can exceed 80% data loss. Besides, the occupancy-aware refresh achieves a 2.14x improvement in effective refresh rate over conventional global refresh. Hardware evaluation in GlobalFoundries 22nm node confirms that the digital readout datapath accounts for less than 0.86% of the detector area and complies with the strict power budget in liquid argon environments.

The DART impact on Dimorphos produced a large population of low-velocity ejecta (v < vesc), likely containing most of the excavated mass, whose early fate remains poorly constrained. We investigate the first 22 h evolution of ejecta launched at 1-9 cm/s and assess how orbital dynamics and surface transport after first contact shape the surface distribution of the re-accreted ejecta blanket. We use RAVEL, a model that couples three-dimensional orbital dynamics in the Didymos-Dimorphos system with surface transport on a digital terrain model, including detachment, re-impact, rebound, and frictional sliding. Re-accretion is rapid and asymmetric: more than 99% of the re-accreted mass returns to Dimorphos within 5 h. The slowest ejecta remain concentrated near the DART crater and dominate the primary ejecta blanket, whereas faster particles undergo orbital transport and preferentially populate antipodal and trailing regions. Surface motion strongly modifies the first-contact pattern, and the DART-derived rough terrain model produces elongated, topographically channelled depositional streaks dominated by the slowest ejecta, giving rise to a ray-like pattern. These results provide testable predictions for ESA's Hera mission and link ejecta-blanket morphology to orbital dynamics, re-accretion, and surface topography.

Characterizing the Marine Atmospheric Boundary Layer (MABL) requires understanding the coupling between ocean waves and the turbulent atmospheric boundary layer above them. This coupling controls momentum exchange between the atmosphere and the ocean; it is of practical importance in the global climate, flow of ocean currents, ocean engineering, and offshore wind energy. Computational study of the MABL is complex because it must resolve the coupled physics of waves and turbulence over a wide range of spatial and temporal scales. This study expands on approaches for representing dynamic, local waves in Large Eddy Simulations (LES) of the MABL by developing a subfilter wave drag model to be local and scale-invariant. It explores the effects of different wave parameters (significant wave height and peak frequency of the wave energy spectrum) on the resulting momentum flux beyond monotonic relationships between surface stress through friction velocity $u_\ast$ and wind velocity above the surface $U_{10}$. Results are compared to field data and in a discussion on how representation of the MABL and associated momentum flux need to account for both wind and wave effects.

We investigate the propagation of cylindrical waves in a square network of acoustic waveguides. We establish, both theoretically and experimentally, the anisotropic dispersion relation governing wave propagation in the network, and demonstrate excellent agreement between experimental measurements and theoretical predictions. Owing to this anisotropic band structure, each propagation direction exhibits distinct dispersive properties. Consequently, the network supports anisotropic cylindrical waves at both low- and high-amplitudes, with waveforms that vary markedly with direction: from nearly dispersionless pulses to Airy-like wave packets in the linear regime, and from sharp shock-like fronts to smooth solitary-like profiles in the nonlinear regime. The theoretical results are further corroborated by numerical simulations based on the two-dimensional Westervelt equation.