Papers

Accurate autoregressive prediction of 3D turbulent flows remains challenging for neural PDE solvers, as small errors in fine-scale structures can accumulate rapidly over rollout. In this paper, we propose FlowRefiner, a flow matching-based iterative refinement framework for 3D turbulent flow simulation. The method replaces stochastic denoising refinement with deterministic ODE-based correction, uses a unified velocity-field regression objective across all refinement stages, and introduces a decoupled sigma schedule that fixes the noise range independently of refinement depth. These design choices yield stable and effective refinement in the small-noise regime. Experiments on large-scale 3D turbulence with rich multi-scale structures show that FlowRefiner achieves state-of-the-art autoregressive prediction accuracy and strong physical consistency. Although developed for turbulent flow simulation, the proposed framework is broadly applicable to iterative refinement problems in scientific modeling.

The plasma environment around Mars is highly variable because it is strongly influenced by the solar wind. Accurate identification of plasma regions around Mars is important for the community studying solar wind-Mars interactions, region-specific plasma processes, and atmospheric escape. In this study, we develop a machine-learning-based classifier to automatically identify three key plasma regions–solar wind, magnetosheath, and induced magnetosphere–using only ion omnidirectional energy spectra measured by the MAVEN Solar Wind Ion Analyzer (SWIA). Two neural network architectures are evaluated: a multilayer perceptron (MLP) and a convolutional neural network (CNN) that incorporates short temporal sequences. Our results show that the CNN can reliably distinguish the three plasma regions, whereas the MLP struggles to separate the solar wind and magnetosheath. Therefore, the CNN-based approach provides an efficient and accurate framework for large-scale plasma region identification at Mars and can be readily applied to future planetary missions.

Magnetohydrodynamic (MHD) turbulence is a ubiquitous dynamical state of astrophysical plasmas and a primary agent in the redistribution, dissipation, and conversion of energy into particle populations. Yet turbulence is still most often described in terms of cascades, spectra, and scale-to-scale transfer, while its role in producing localized sites of intense energization remains comparatively underemphasized. In this forward-looking review, aimed at a broad astrophysical readership, I argue that any physically complete picture of turbulent plasma heating and particle acceleration must place the self-consistent emergence of coherent structures at its center. Current sheets, vortical structures, magnetic flux ropes, shocklets, and confined reconnection sites are not secondary by-products of the turbulent cascade; they are its dynamically dominant dissipative and energizing elements, where electric fields intensify, dissipation becomes highly localized, and particles undergo repeated acceleration. Viewed in this way, strong turbulence provides a unifying framework that links large-scale plasma dynamics to the generation of suprathermal particles and non-thermal energy distributions in the solar atmosphere, the solar wind, shock environments, and a wide range of other cosmic plasmas. Rather than attempting an exhaustive survey of the literature, this article offers a selective and physically organized synthesis of the field, emphasizing the mechanisms, regimes, and open problems most relevant to the development of predictive theories of particle acceleration in turbulent plasmas. It also identifies the principal conceptual and computational challenges that must be overcome if the next generation of models is to connect multiscale plasma dynamics with observable energetic-particle signatures.

Buneman instability has been extensively studied, and related aspects, namely anomalous resistivity, have been explored in detail using analytical theory as well as numerical simulations based on Particle-in-Cell and Vlasov solvers. Most numerical studies have focused on understanding the nonlinear evolution of the instability. In the present study, the growth rate of the Buneman instability in the presence of thermal effects of the constituent species (i.e., ions and electrons) is investigated. It is observed that the growth rate differs significantly from that obtained using fluid models (both cold and warm) as well as from linearized kinetic models. While the well-known result of $(m/M)^{1/3}$ dependence of the maximum growth rate is recovered, it is shown that the maximum growth rate is essentially independent of the temperature ratio of the constituent species. It is further demonstrated numerically that the amplitude of ion density inhomogeneity self-consistently controls the transfer of electron beam energy into the bulk plasma temperature. In particular, as one moves from the cold to the warm plasma limit, the decrease in ion density inhomogeneity reduces the generation of sidebands and thus lowers the transfer efficiency.

Confocal Brillouin microscopy enables high-resolution mechanical imaging but has low acquisition speed, partly due to its pixel-by-pixel mapping strategy. Line-scanning Brillouin microscopy (LSBM) significantly improves imaging speed by utilizing a multiplexing approach. However, current method is limited to a single-stage virtually imaged phased array (VIPA) spectrometer with insufficient capability of suppressing noise. Consequently, an absorptive gas chamber is often used to help reject excessive elastically scattered light. This approach requires specific tunable laser sources whose frequencies (e.g., around 780 nm) are locked to the absorption line of the gas chamber. Here, we developed a multiplexed Brillouin spectrometer for LSBM that increased the noise suppression to 57 dB without using any gas chamber. This is achieved by cascading two VIPA etalons with parallel dispersion axes in the spectrometer, where the first VIPA acts as a band-pass filter and the second as spectrum analyzer. We demonstrated its performance by acquiring Brillouin images of bio-printed phantoms with an inverted co-axial LSBM. This gas-chamber-free approach can expand the implementation of LSBM to other wavelengths where Brillouin scattering is more efficient and commercial laser sources are readily available.

Possibilities are considered to simplify the computation of several statistical functions used to test statistical hypotheses when processing observations: the inverse normal distribution, the Student's t-distribution, and the criterion for rejecting outliers. For these three cases, simple approximation expressions are proposed for the quantiles of these statistical distributions, which are accurate enough for most practical applications.

Monolayer transition metal dichalcogenides (e.g., MoS2) exhibit exceptionally large optical nonlinearities for high-order nonlinear light generation (NLG), yet their inherent atomic thickness fundamentally limits light-matter interactions and thus conversion efficiency. Here, we overcome this intrinsic trade-off using a solution-processed bulk monolayer MoS2 (BM-MoS2) architecture composed of electronically decoupled MoS2 monolayers separated by organic interlayers. This layered superstructure preserves the exceptional intrinsic nonlinear susceptibility of monolayer MoS2 while enabling scalable interaction length. In the subwavelength regime, the NLG scales nearly quadratically with layer number (N1.8), confirming the constructive buildup of nonlinear fields across stacked monolayers. As a result, a 100-nm-thick BM-MoS2 thin film exhibits colossal high-order NLG, including four-wave mixing and high-harmonic generation. The generated nonlinear beam is directly visible to the naked eye and exhibits broad spectral tunability spanning approximately 1000 nm in the mid-IR, enabling mid-IR-to-visible upconversion spectroscopy for resolving molecular vibrational fingerprints. By uniting monolayer-scale nonlinear susceptibility with bulk interaction length and coherent field buildup, BM-MoS2 establishes a thin-film platform for ultra-compact and substrate-agnostic nonlinear photonic systems beyond the constraints of conventional single crystals.

The solvation free energy (SFE) of molecules and ions is a fundamental property governing their solvation behavior and solubility. Molecular simulations offer a route to compute SFEs using alchemical free energy methods, such as thermodynamic integration or free energy perturbation. However, these methods suffer from the infamous end-point singularity, which leads to numerical instability when atoms approach closely, a challenge that becomes particularly acute in ab initio and machine learning molecular dynamics simulations. Here, we introduce the bubble method to calculate the SFEs of molecules and ions from first principles. Our approach avoids the end-state problem in both ab initio and machine learning molecular dynamics simulations and is applicable to molecules and ions of arbitrary shape. When calculating the SFEs of ions using periodic density functional theory, we incorporate corrections for the neutralizing background charge, spurious interactions between periodic images, and the vacuum-water interface potential. To validate our method, we successfully computed the SFEs of methane, methanol, water, and sodium ions using classical, ab initio, and machine learning molecular dynamics simulations. Importantly, our method requires no experimental inputs or empirical data. This makes it particularly well-suited for studying systems under extreme conditions, such as high pressure-temperature environments or under nanoconfinement, situations where experimental investigations are challenging and classical force fields, typically parameterized under ambient conditions, may be unreliable.

Single photon emitters (SPEs) are integral parts of several quantum technology implementations. Over the past decade or so, monolayers of transition metal dichalcogenides (TMDCs) have emerged as one of the promising candidates for SPE platforms with attractive characteristics. To move ahead, it is necessary to understand the atomistic origin of SPEs in TMDCs - a topic which is highly debated with contradicting proposals. In this paper, we critically review these existing proposals to elucidate their origin. Further, we perform a critical trend analysis for different figures of merit of TMDC-based SPEs, and propose a characterization methodology to streamline the reporting process. Finally, we review several quantum technology implementations where solid state SPEs are being used, and identify the advancements required in TMDC-based SPEs for their successful adoption in these technologies.

Kinetic Alfven waves (KAWs) are fundamental to energy transport and small-scale structure formation in the turbulent, magnetized interstellar medium (ISM). While first-order Korteweg–de Vries (KdV) models describe weakly nonlinear KAW solitons, they fail in strongly inhomogeneous environments where higher-order effects become significant. We investigate higher-order "dressed" kinetic Alfven (KA) solitons in a structured ISM (warm ionized medium, H II regions, stellar-wind bubbles, supernova remnants). Using a multi-component fluid model with superthermal electrons, we derive an inhomogeneous KdV-type equation with cubic nonlinearity, nonlinear-dispersive cross terms, and fifth-order dispersion. The dressed soliton has a $\operatorname{sech}^2$ core decorated by higher-order corrections. We classify soliton morphologies across the Galactic plane as a function of electron suprathermality $\kappa_e$. Five classes ($\psi_{\rm I}$–$\psi_{\rm V}$) evolve non-monotonically with $\kappa_e$: strongly suprathermal ($\kappa_e=1.6$) favour negative double-hump ($\psi_{\rm III}$); intermediate $\kappa_e$ produce layered sequences of $\psi_{\rm II}$, $\psi_{\rm I}$, $\psi_{\rm IV}$, $\psi_{\rm V}$; near-Maxwellian ($\kappa_e=3.1$) revert to KdV-like $\psi_{\rm I}$. Localised $\psi_{\rm V}$ appear as a red ring around the SWB shell and a red core inside the SNR, showing embedded structures actively generate distinct morphologies. First-order KdV theory is insufficient; dressed solitons are the natural nonlinear states. The ISM morphology selects soliton class by modulating leading vs. higher-order terms. $\psi_{\rm V}$ features link macroscopic ISM structures to kinetic-scale fluctuations, offering candidates for extreme scattering events and pulsar scintillation. The non-monotonic $\kappa_e$ dependence can constrain electron suprathermality from observations.

In this work, we develop a theoretical description of the collective behavior of interacting dipolar planar rotors by using time independent perturbation theory and a small angle quadratic approximation. The ground state properties for both the ordered and disordered quantum phases of the system are directly calculated and analyzed. Time-independent perturbation theory is shown to be appropriate for the disordered phase. For the ordered phase, we construct a quadratic approximation based on the stable equilibrium configurations of the dipolar ordering; we show that the inclusion of the quartic terms from the expansion of the potential energy are essential to correct the shift in the energy spectrum due to quantization ambiguities. Numerical techniques such as Exact Diagonalization and Density Matrix Renormalization Group are used for the benchmark the quality of both approximations.

The LEOPs (Light-ERG-Oscillatory Potentials) dataset provides light-adapted (LA) electroretinogram (ERG) and Oscillatory Potentials (OPs) waveforms for typically developing Control, Autism Spectrum Disorder (ASD) and ASD + Attention Deficit Hyperactivity Disorder (ADHD) childhood and adolescent populations. The ERGs were recorded in the Right And Left eyes with skin electrodes using the handheld RETeval device at two sites in Australia and the United Kingdom. The LEOPs dataset includes 5309 single flash ERG and 4434 OPs waveforms as well as images selected from each participant showing the position of the skin electrode. The LEOPs dataset is constructed from recordings using a 9 step randomized flash series from $-0.37$ to $1.20$~$Td.s$, a 2 step at 113 and 446 $Td.s$ flash strengths (2500 Control, 1730 ASD and 451 ASD + ADHD samples), as well as the $85$~$Td.s$ (Light Adapted 3 $cd.s.m^{-2}$ (LA3)) equivalent International Society of Clinical Electrophysiology of Vision (ISCEV) Standard flash with 435 Control, 176 ASD and 37 ASD + ADHD waveform samples. Code for the stimulus is provided along with participant demographics, date and time of testing, and where available diagnostic scores for the ASD and ASD + ADHD groups, alongside iris color, electrode position with image files and time domain values for the ERG and summed values for the OPs. The repository contains excel file, exported JSON files on the patient level that are more suitable for machine learning tasks, images of electrode position for each recording and the protocol files for use with the RETeval.

Applications such as digital microfluidics and bio-diagnostics rely on droplet locomotion. A prominent example of such motion is durotaxis, a phenomenon that requires a stiffness gradient along a surface for the transport of liquids, cells, or other nano-objects. Using surfaces with varying properties in specific directions can be exploited as a universal concept for fluid transport with or without external energy supply. Changes in properties may refer to substrate patterns, Laplace pressure changes, wettability gradients, etc., leading to exciting phenomena, which can be employed in novel applications in various technologies. Here, we report on key results and progress in the area of directed droplet motion over the years, and we provide perspectives and implications for anticipated applications.

The dynamics of magnetization near a stable equilibrium in ferromagnetic nanomagnets are examined within the Landau–Lifshitz–Gilbert (LLG) framework. For a small angle precession, the dependence of ferromagnetic resonance (FMR) frequency, the damping constant and the resulting quality factor $Q$ of the resonance on the local curvature around the free-energy minimum is systematically analyzed. Special attention is devoted to the behavior of the FMR decay time in the vicinity of bifurcation points, where the number of metastable energy minima changes and the commonly used approximation for the quality factor $Q\simeq 1/2\alpha$ (where $\alpha$ denotes the Gilbert damping) fails.

Density functional theory with plane-wave basis sets is widely employed in computational materials science, including applications to isolated molecular systems. However, the inadequate description of electron correlation remains a fundamental limitation. Accurate correlation treatments based on many-body Hamiltonians require reliable representations of both occupied and virtual orbitals, yet virtual orbitals are often poorly described in conventional computational schemes, resulting in reduced accuracy. In this work, we introduce localized correlation-converged virtual orbitals (LCCVOs) as an efficient basis for constructing accurate many-body Hamiltonians in molecular systems. Using a substantially reduced number of orbitals, the LCCVO framework yields dissociation energies for singlet, doublet, and triplet molecules that are comparable to, and in many cases exceed, those obtained with high-level correlation-consistent basis sets such as cc-pVXZ (X = D, T, Q, 5). These results demonstrate the efficiency, scalability, and robustness of the LCCVO approach for high-accuracy quantum chemical calculations.

The Qinghai-Tibetan Plateau (QTP), Earth's "Third Pole", profoundly shapes the Asian monsoon and regional climate and exerts far-reaching influence on the global climate system. Yet its role in organizing planetary-scale climate interactions remains poorly quantified. Here we develop a climate network framework to explicitly resolve the planetary teleconnection architecture associated with the QTP across historical observations and future climate projections, with physical consistency assessed using Lagrangian trajectory diagnostics and targeted numerical experiments. We uncover a persistent and directional interaction structure linking the QTP with multiple major climate tipping elements. In particular, we identify a robust tripolar interaction mode coupling the QTP with both the Arctic and Antarctica through coherent atmospheric-oceanic pathways. Our findings establish the QTP as a critical planetary climate integrator, revealing a significant blind spot in current climate models and risk frameworks regarding cascading tipping dynamics in a warming world.

Biomolecular phase separation is typically attributed to the polymer physics of long, disordered chains. However, the underlying chemical grammar, i.e. the specific interactions between protein and RNA building blocks, remains poorly understood. We decouple those effects by screening the phase behavior of the complete dipeptide library in presence and absence of nucleic acids using full-atomistic molecular dynamics simulations. We demonstrate that (i) even these ultrashort units encode the instructions for spontaneous condensation, proving that phase separation is fundamentally rooted at a sub-polymeric level. (ii) Nucleic acids do not act as generic anionic glue but exert instead a base-specific regulatory logic. (iii) Individual nucleobases function as chemical tuners that dissolve, stabilize, or fluidize condensates based on their molecular identity. Overall, our minimal framework reveals that while polymer length enhances assembly, the core properties and regulatory control of condensates may be also governed by a fine-tuned chemical alphabet of peptides and nucleobases.

Tetraphenylpyrazine (TPP) and 2,3,4,5-tetraphenyl-1H-pyrrole (TePP) are closely related heterocycles bearing four phenyl substituents, whose structural similarity makes them a useful pair for comparing how intramolecular flexibility influences excited-state relaxation and emission in the gas phase and in the solid state. TPP is a prototypical solid-state luminescence enhancement (SLE) emitter, exhibiting a markedly increased quantum yield upon molecular aggregation. In contrast, TePP displays similar quantum yields in solution and solid state, characteristic of dual-state emission (DSE). This behaviour indicates that intramolecular rotations are already significantly hindered in the isolated-molecule regime, consistent with our previous observations for TPP and other solid-state emitters (Hernández-Rodríguez et al., ChemPhysChem, 2024, 25, e202400563). To unravel the excited-state dynamics underlying this contrasting behaviour, we performed mixed quantum-classical trajectory simulations on a single molecule of TPP and TePP employing the surface-hopping method. Twelve singlet states were included at the TD-B3LYP-D3/def2-SVP level, which were previously benchmarked against coupled cluster methods. Simulated observables such as gas phase ultrafast electron diffraction (GUED) and time-resolved fluorescence (TR-FL) signals allow us to dissect the distinct deactivation pathways operating in both systems in the gas phase, while also providing mechanistic insight into how these pathways are expected to evolve in solution and solid-state environments.

As the explosive growth of visual data increasingly strains the latency and energy limits of conventional electronic computing, optical analog computing has re-emerged as a disruptive paradigm for zero-power, speed-of-light information processing. Propelled by the unprecedented wave-manipulation capabilities of optical metasurfaces, this field is undergoing a rapid transition from macroscopic physical optics to ultra-compact, on-chip meta-processors. This Review examines the fundamental mechanisms of metasurface-empowered optical computing spanning Fourier-domain, nonlocal spatial-domain, and interferometric architectures that perform mathematical operations, with a particular focus on spatial differentiation and edge detection as representative computing tasks. By emphasizing recent breakthroughs, we highlight the evolution of meta-processors from static, linear regimes to dynamically reconfigurable, nonlinear, and quantum-assisted multidimensional platforms. We also envision how the synergy of AI-driven inverse design and the integration of analog meta-front-ends with optical neural networks will synergistically revolutionize next-generation intelligent machine vision.

We introduce the spatial disorder-generalized Langevin equation (SD-GLE), a data-driven method for constructing coarse-grained (CG) dynamics in heterogeneous systems. Unlike conventional CG approaches that rely on a mean-field potential, SD-GLE utilizes a variational Bayesian framework with a random field prior to explicitly disentangle static spatial disorder from viscoelastic friction. Numerical results demonstrate the limits of standard GLEs, whereas SD-GLE accurately extrapolates long-time dynamics to capture the anomalous diffusion crossover from short trajectories and recover the ensemble statistical properties inherent to the disordered nature of these systems.

We present a rigorous derivation and numerical validation of a universal projection-law factorization for quartic nonlinear coupling rates across physically distinct platforms. The central result is that observable Kerr-type interactions – self-Kerr, cross-Kerr, and cross-phase modulation – factorize into a dimensionless projection coefficient and an intrinsic quartic energy scale. This structure follows from canonical quantization of a quartic interaction projected onto a finite normal-mode basis. We state this result as a formal proposition with clearly enumerated assumptions, provide a proof sketch based on second quantization of the anharmonic potential, and enumerate the domain of validity. We then implement the scaling law as a lightweight computational toolkit (UEFT-Designer) with platform-specific kernels for superconducting circuits, photonic microcavities, and epsilon-near-zero (ENZ) structures. A complete, step-by-step worked example for a superconducting quarton device – with full uncertainty propagation – predicts a cross-Kerr rate $\chi/2\pi = 361\pm 13\,\mathrm{MHz}$, agreeing with the independently measured value of $366\pm 0.5\,\mathrm{MHz}$ \cite{Ye2025} to within 1.4%. A formal error-propagation analysis establishes that the dominant uncertainty is the Josephson-energy extraction uncertainty ($\sim\!2\%$), not the geometric projection factor. Cross-platform validation against five independent experiments spanning eight orders of magnitude in coupling strength confirms the universality of the factorization to within reported experimental uncertainties. The ghost-sector spectral correction introduced in earlier versions is reframed as an optional phenomenological self-energy model with no mandatory role in the validated scaling law.

In this work, we perform experiments with spherical polyacrylamide (PAAm) hydrogel drops/spheres, spanning a broad range of shear moduli and impact velocities on hydrophilic (plasma-treated glass) and hydrophobic (silane-coated) substrates, yielding an elastic number El variation of five orders of magnitude. Transient spreading morphology and impact force were simultaneously resolved using synchronized high-speed imaging and piezoelectric force sensing. At low elastic numbers ($El < 1$), impacting hydrogels exhibit a hybrid poroelastic response: a liquid-rich contact foot is expelled from the polymer network and spreads independently, while the bulk drop undergoes viscoelastic contact-line pinning into a pancake geometry at maximum deformation. At high elastic numbers ($El > 1$), contact foot spreading is suppressed, and deformation is accurately described by a neo-Hookean energy balance, yielding a maximum spreading factor independent of substrate wettability. Further, we show that the normalized peak impact force $F^*$ collapses to a constant value consistent with the Wagner limit for $El < 1$ and follows a power-law scaling $F^* \sim El^{0.38}$ for $El > 1$, in close agreement with both Hertzian and neo-Hookean predictions, and independent of substrate wettability. Furthermore, we highlight that post-impact retraction is suppressed across nearly the entire parameter space due to adsorbed polymer chains anchoring the receding gel network to the substrate, producing circumferential ridge instabilities; rebound occurs only when elastic restoring forces overcome the work of adhesion.

Ion trap quantum computers often store qubits on field-sensitive S_1/2 ground state Zeeman levels of the valence electron, such as in 40Ca+, 88Sr+, and 138Ba+ atomic systems. We experimentally synthesize magnetically insensitive qubit states in multiple metastable electronic D_3/2 Zeeman levels in such an atomic system. We demonstrate coherent operations within the D_3/2 manifold of 138Ba+, including coherent flopping between the synthesized qubit states, and our results agree with theory. Such an encoding may allow for more flexible use of atomic levels for photonic interfaces, and with a measured improvement in the qubit coherence time T2* by a factor of 3, this lays the foundation for further improvement for quantum computing and network applications.

Understanding the evolution of connectivity in spatiotemporal systems requires mathematical frameworks capable of encoding not only instantaneous interactions but also their cumulative causal structure. In this work, we introduce the Causal Edge Rees Algebra (CERA), a new algebraic construction associated with causal spatiotemporal graphs. Given a temporal filtration induced by causal constraints, we associate a sequence of edge ideals whose Rees algebra encodes the full history of connectivity evolution in a single graded object. This construction establishes a bridge between dynamic graph topology and commutative algebra. In particular, we show that successive quotients of the filtration capture the emergence of new structural connections, allowing the identification of critical edges responsible for the fusion of previously disconnected components. This leads to the definition of temporal bridge modules and to a bridge detection theorem, which relates the dimension of these modules to the reduction in the number of connected components over time. Unlike existing algebraic approaches in topological data analysis, which are primarily based on geometric filtrations, the proposed framework is driven by intrinsic causal constraints. As a result, the CERA captures not only topological features but also their temporal organization and mechanisms of coalescence. The theory provides a new algebraic perspective on causal network dynamics, connecting edge ideals, Rees algebras, and temporal graphs. Beyond its theoretical significance, the framework opens new directions for the analysis of spatiotemporal systems, including epidemic networks, transport systems, and information propagation processes.

This work investigates the use of resonance statistics for resonance evaluation to inform spin group assignment and an alternative fitting objective function beyond the commonly used chi-squared statistic. Resonance statistics -informed methods are applied to the automated resonance fitting framework, developed by N. Walton et al. In this automated framework, the utility of resonance statistics is largely unexplored. The new resonance statistics -informed spin group shuffling algorithm reduces spin group frequency bias seen in the base fitting algorithm. Although resonance statistics -informed optimization produces negligible changes in pointwise cross section agreement, it significantly improves consistency with Wigner level-spacing statistics and stabilizes the fitted resonance density in the presence of model imperfections.

Axions, hypothetical particles proposed to solve the strong CP problem and considered promising dark matter candidates, can be produced in the Sun and interact in detectors via couplings to photons, electrons, or nucleons. The CUORE and CUPID scintillating cryogenic calorimeters, originally developed to search for dark matter and neutrinoless double beta decay, are well suited to axion searches due to their excellent energy resolution, particle identification capability and low background. In order to search for high-energy solar axions at 5.5~MeV, a resolution study is carried out using calibration and background spectra, extrapolating the detector response in the hypothetical signal region. The analysis is based on exposures of 9.95~kg$\cdot$yr (CUPID-0 Phase~I) and 5.74~kg$\cdot$yr (CUPID-0 Phase~II). The energy resolution FWHM at 5.5~MeV is found to be $(39.8 \pm 2.1)$ keV for events fully contained in a single crystal, enabling axion searches with a background level lower than $10^{-3}$\,counts/(keV kg y).

Computational physics increasingly depends on large simulation datasets generated by software that remains under active development for many years. In such settings, reproducibility requires not only well documented data but also explicit links between code versions, simulation inputs, generated outputs, analysis steps, and published figures. Here, we present an integrated workflow for reproducible and FAIR-aligned simulation research in numerical physics. We describe how version control, code review, automated testing, structured logging, metadata-rich output, and standardized post-processing can be combined to support traceability from software development to publication. The presented concepts demonstrated for one particular simulation framework are broadly applicable to computational physics and other data-intensive areas of scientific computing.

Efficient and stable solution of partial differential equations (PDEs) is central to scientific and engineering applications, yet existing numerical solvers rely heavily on matrix based discretizations, while learning based methods require costly training and often suffer from limited generalization. In this work, we proposes a PDE energy driven framework that solves PDEs through physically constrained diffusion iterations, without relying on classical matrix based finite element assembly or data driven neural network training. The proposed method evolves arbitrary random initial fields through PDE energy driven implicit iterations combined with Gaussian smoothing, while strictly enforcing boundary conditions at each iteration. The proposed formulation is applied to representative one dimensional Poisson, Heat, and viscous Burgers equations, covering both steady state and transient problems. Numerical results demonstrate stable convergence to the unique physical solution from random initializations, with accurate resolution of sharp gradients and controlled Mean Squared Error (MSE) across a wide range of discretization parameters. Detailed comparisons with analytical solutions indicate that the framework achieves competitive accuracy and stability. Overall, the proposed framework provides a fast, flexible, and physically consistent alternative to traditional numerical solvers, offering a potential pathway for scalable PDE solutions in both research and engineering applications.

We present a Mixture-of-Experts-based foundation model applied to the GlueX DIRC detector at Jefferson Lab, demonstrating its utility as a unified framework for fast simulation, particle identification, and hit-level noise filtering of Cherenkov photons. By leveraging a single shared transformer backbone across all tasks, the approach eliminates the fragmentation of task-specific pipelines while maintaining competitive-and in several cases superior-performance relative to established methods. The model operates directly on low-level detector inputs, performing hit-by-hit autoregressive generation over split spatial and temporal vocabularies with continuous kinematic conditioning, and supports class-conditional generation of pions and kaons through its Mixture-of-Experts architecture. We benchmark against the standard geometrical reconstruction and prior deep learning methods across the full kinematic phase space of the GlueX DIRC, demonstrating that the foundation model framework transfers effectively to this detector without architectural modification. This work positions the foundation model as a practical and scalable alternative to the suite of task-specific models currently proposed for GlueX DIRC analysis.

We present KinetiDiff, a structure-based framework for de novo kinase inhibitor design that integrates a Geometry-Complete Diffusion Model with real-time AutoDock Vina gradient guidance. By injecting physics-based docking gradients into the diffusion denoising loop, KinetiDiff steers molecule generation toward high-affinity conformations for ACVR1 (ALK2), the causative kinase in Fibrodysplasia Ossificans Progressiva. From 10,000 diffusion samples, the framework produced 9,997 valid molecules. The best candidate achieved $-11.05$ kcal/mol (pKd = 8.10), a 19.2% improvement over the crystallographic reference. The top 100 candidates all exceed the reference, with 100% Lipinski compliance, median synthetic accessibility of 2.67, and internal diversity of 0.790. Systematic ablation across four guidance strategies–Vina-Direct (physics), HNN-Denovo (neural proxy), multi-objective, and unguided–demonstrates that real-time docking guidance dominates on all metrics. We evaluate HNN-Denovo as a computationally efficient alternative (60-fold speedup per step), revealing a domain-mismatch limitation (r = 0.224 correlation with Vina) that explains its inferior performance. These results establish gradient-guided geometric diffusion as a practical approach for generating potent, synthetically accessible inhibitors against rare-disease kinase targets.