Applied Physics
Multiscale Mechanical Response of 3D-Printed Diamondiynes: From Movable Interlocked Lattices to Architected Metamaterials
Anitesh Kumar Singh, Rodrigo A. F. Alves, Tapas Pal et al. (+8 more)
Diamondynes are a recently synthesized three-dimensional carbon allotrope, with interlocked and movable sublattices that introduce deformation modes not present in standard architected materials. Here, we report the first multiscale mechanical assessment of Diamondiyne-derived architectures by combining quasi-static compression of 3D-printed specimens with reactive molecular dynamics simulations of the corresponding atomic-scale models. We generate four geometries (3F, 2F-SY, 4F, and 2F-USY). All structures resulted in lower density in the range of 0.20-0.38 g.cm^-3. Experiments indicate that the symmetric two-sublattice structure (2F-SY) delivers the best performance, reaching a specific yield strength of 5.91 MPa.g^-1cm^-3 and a specific energy absorption of 279 J.g^-1, whereas 2F-USY architecture yielded the lowest values, with 0.77 MPa.g^-1.cm^-3 and 16 J.g^-1. The 4F geometry provided a specific energy absorption of 254 J.g^-1. The structures deformed through
geometric collapse and strut buckling, which was due to diagonal shear in 2F-USY and progressive compaction in 2F-SY and 3F. Molecular dynamics simulations also confirmed these experimental trends and revealed strong directional anisotropy due to the arrangement of interlocked sublattices, with a stiffness of 24.1 GPa along the z-direction in the case of 4F architecture. Overall, Diamondiyne-derived architectures display geometry-dominated mechanical behavior and serve as a promising platform for lightweight, energy-absorbing metamaterials.
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All authors Anitesh Kumar Singh, Rodrigo A. F. Alves, Tapas Pal, Sarmistha Bora, Hugo X. Rodrigues, Emanuel J. A. dos Santos, Camila de L. Ribeiro, Alysson M. A. Silva, Luiz A. Ribeiro Júnior, Douglas S. Galvão, Chandra Sekhar Tiwary
Computational Physics
diffpy.morph: Python tools for model independent comparisons between sets of 1D functions
Andrew Yang, Christopher L. Farrow, Pavol Juhás et al. (+22 more)
diffpy$.$morph addresses a need to gain scientific insights from 1D scientific spectra in model independent ways. A powerful approach for this is to take differences between pairs of spectra and look for meaningful changes that might indicate underlying chemical, structural, or other modifications. The challenge is that the difference curve may contain uninteresting differences such as experimental inconsistencies and benign physical changes such as the effects of thermal expansion. diffpy$.$morph allows researchers to apply simple transformations, or "morphs", to one of the
datasets to remove the unwanted differences revealing, when they are present, non-trivial differences. diffpy$.$morph is an open-source Python package available on the Python Package Index and conda-forge. Here, we describe its functionality and apply it to solve a range of experimental challenges on diffraction and PDF data from x-rays and
neutrons, though we note that it may be applied to any 1D function in principle.
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All authors Andrew Yang, Christopher L. Farrow, Pavol Juhás, Luis Kitsu Iglesias, Chia-Hao Liu, Samuel D. Marks, Vivian R. K. Wall, Joshua Safin, Sean M. Drewry, Caden Myers, Dillon F. Hanlon, Nicholas Leonard, Cedomir Petrovic, Ahhyun Jeong, Dmitri V. Talapin, Linda F. Nazar, Haidong Zhou, Samuel W. Teitelbaum, Tim B. van Driel, Soham Banerjee, Emil S. Bozin, Michael F. Toney, Katharine Page, Naomi S. Ginsberg, Simon J. L. Billinge
Computational Physics
DISCOVER: A Physics-Informed, GPU-Accelerated Symbolic Regression Framework
Udaykumar Gajera, Mohsen Sotoudeh, Kanchan Sarkar et al. (+1 more)
Symbolic Regression (SR) enables the discovery of interpretable mathematical relationships from experimental and simulation data. These relationships are often coined descriptors which are defined as a fundamental materials property that is directly correlated to a desired or undesired functional property of the material. Although established approaches such as Sure Independence Screening and Sparsifying Operator (SISSO) have successfully identified low-dimensional descriptors within large feature spaces many existing SR tools integrate poorly with modern Python workflows, offer limited control over the symbolic search space, or struggle with the computational demands of large-scale studies. This paper introduces DISCOVER (Data-Informed Symbolic Combination of Operators for Variable Equation Regression), an open-source symbolic regression package developed to address these challenges through a modular, physics-motivated design. DISCOVER allows users to guide the symbolic search using domain knowledge, constrain the feature space explicitly, and take advantage of optional GPU acceleration to improve computational efficiency in data-intensive workflows, enabling reproducible and scalable SR workflows. The software is intended for applications in computational physics, computational chemistry, and materials science, where interpretability, physical consistency, and execution time are especially important, and it complements general-purpose SR frameworks by emphasizing the discovery of physically meaningful models.
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All authors Udaykumar Gajera, Mohsen Sotoudeh, Kanchan Sarkar, Axel Groß
General Physics
Cosmological evolution of collisionless relativistic gases as dark matter
Francisco X. Linares Cedeño, Ulises Nucamendi and Olivier Sarbach
We study a phenomenological dark matter model described as a collisionless relativistic kinetic gas in a spatially flat Friedmann-Lemaître-Robertson-Walker universe. After normalization to the observed present-day dark matter abundance, the model is fully specified by a single dimensionless parameter $\beta$, interpreted as the present particle velocity in units of the speed of light. The resulting energy density, pressure, and sound speed admit closed analytic expressions, interpolating between a radiation-like regime at early times and cold dark matter at late times. We implement the model in a modified version of the Boltzmann code CLASS and confront it with Planck 2018 CMB data. We find that sufficiently small values of $\beta$ are observationally indistinguishable from $\Lambda$CDM, while larger values inducing relativistic effects at early times are constrained. These results establish the consistency of the relativistic kinetic gas scenario with current cosmological observations.
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Geophysics
A General-Purpose Diversified 2D Seismic Image Dataset from NAMSS
Lucas de Magalhães Araujo, Otávio Oliveira Napoli, Sandra Avila et al. (+1 more)
We introduce the Unicamp-NAMSS
dataset, a large, diverse, and geographically distributed collection of migrated 2D seismic sections designed to support modern machine learning research in geophysics. We constructed the
dataset from the National Archive of Marine Seismic Surveys (NAMSS), which contains decades of publicly available marine seismic data acquired across multiple regions, acquisition conditions, and geological settings. After a comprehensive collection and filtering process, we obtained 2588 cleaned and standardized seismic sections from 122 survey areas, covering a wide range of vertical and horizontal sampling characteristics. To ensure reliable experimentation, we balanced the
dataset so that no survey dominates the distribution, and partitioned it into non-overlapping macro-regions for training, validation, and testing. This region-disjoint split allows robust evaluation of generalization to unseen geological and acquisition conditions. We validated the
dataset through quantitative and
embedding-space analyses using both convolutional and
transformer-based models. These analyses showed that Unicamp-NAMSS exhibits substantial variability within and across regions, while maintaining coherent structure across acquisition macro-region and survey types. Comparisons with widely used interpretation
datasets (Parihaka and F3 Block) further demonstrated that Unicamp-NAMSS covers a broader portion of the seismic appearance space, making it a strong candidate for machine learning model pretraining. The
dataset, therefore, provides a valuable resource for machine learning tasks, including self-supervised representation learning, transfer learning, benchmarking supervised tasks such as super-resolution or attribute prediction, and studying domain adaptation in seismic interpretation.
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All authors Lucas de Magalhães Araujo, Otávio Oliveira Napoli, Sandra Avila, Edson Borin
Fluid Dynamics · Cross-listed from cs.LG
Hypersonic Flow Control: Generalized Deep Reinforcement Learning for Hypersonic Intake Unstart Control under Uncertainty
Trishit Mondal and Ameya D. Jagtap
The hypersonic unstart phenomenon poses a major challenge to reliable air-breathing propulsion at Mach 5 and above, where strong shock-boundary-layer interactions and rapid pressure fluctuations can destabilize inlet operation. Here, we demonstrate a deep reinforcement learning (DRL)- based active flow control strategy to control unstart in a canonical two-dimensional hypersonic inlet at Mach 5 and Reynolds number $5\times 10^6$. The in-house CFD solver enables high-fidelity simulations with adaptive mesh refinement, resolving key flow features, including shock motion, boundary-layer dynamics, and flow separation, that are essential for learning physically consistent control policies suitable for real-time deployment. The DRL controller robustly stabilizes the inlet over a wide range of back pressures representative of varying combustion chamber conditions. It further generalizes to previously unseen scenarios, including different back-pressure levels, Reynolds numbers, and sensor configurations, while operating with noisy measurements, thereby demonstrating strong zero-shot generalization. Control remains robust in the presence of noisy sensor measurements, and a minimal, optimally selected sensor set achieves comparable performance, enabling practical implementation. These results establish a data-driven approach for real-time hypersonic flow control under realistic operational uncertainties.
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Physics and Society
How Much of the United States Can Still Host New Hyperscale Data Centers? A Constraint-Based Feasibility Analysis
Milan Janosov
The rapid expansion of hyperscale data centers, primarily driven by cloud computing and generative AI is placing growing pressure on electricity systems, land, and
climate-sensitive infrastructure. While existing maps document where data centers are currently located, a major unanswered question remains: where can hyperscale data centers still be built under present-day physical, infrastructural, and environmental constraints? Here we address this question, focusing on the United States, using a national-scale, constraint-first geospatial framework that infers feasibility from revealed hyperscale siting patterns rather than from demand forecasts or optimization assumptions. By combining power-grid adjacency, environmental limits, land-use constraints, and climatic constraints within a uniform hexagonal spatial system, we estimate the feasible hyperscale hosting capacity. Our presented approaches converge on a limited feasible land envelope, implying a substantial contraction relative to naive land-availability assumptions. Based on observed build-out patterns, we estimate that total physically feasible U.S. hyperscale capacity lies in the tens of gigawatts rather than the hundreds. The results of this piece are intended to support national-scale
reasoning about infrastructure feasibility under modern constraints.
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Computational Physics · Cross-listed from cs.LG
The "Robert Boulton" Singularity: Semantic Tunneling and Manifold Unfolding in Recursive AI
Pengyue Hou
The stability of generative artificial intelligence trained on recursive synthetic data is conventionally monitored via Perplexity (PPL). We demonstrate that PPL is a deceptive metric in context-stabilized regimes (L=128). Using a rigorous sliding-window protocol (N=1500), we identify a novel failure mode termed "Semantic Tunneling." While the Baseline model maintains high grammatical fluency (PPL approx. 83.9), it suffers a catastrophic loss of semantic diversity, converging within seven generations to a single, low-
entropy narrative attractor: the "Robert Boulton" Singularity. This phenomenon represents a total collapse of the latent
manifold (Global Effective Rank 3.62 -> 2.22), where the model discards diverse world knowledge to optimize for statistically safe syntactic templates. To address this, we apply the Multi-Scale Negative Coupled Information Systems (MNCIS) framework recently established in Hou (2026) [arXiv:2601.11594]. We demonstrate that Adaptive Spectral Negative Coupling (ASNC) acts as a topological operator that actively induces "
Manifold Unfolding." MNCIS forces the model to expand its effective rank from the anisotropic baseline of 3.62 to a hyper-diverse state of 5.35, effectively constructing an "Artificial
Manifold" that resists the gravitational pull of semantic attractors and preserves the long-tail distribution of the training data.
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Geophysics
Physics-informed deep learning links geodetic data and fault friction
Rikuto Fukushima, Masayuki Kano, Kazuro Hirahara et al. (+1 more)
Fault slip modeling, based on laboratory-derived friction laws, has significantly enhanced our understanding of fault mechanics. Agreement between model predictions and observations supports the hypothesis that observed slip diversity, including fast earthquakes and slow transient slips (Slow Slip Events; SSEs), originates from frictional heterogeneity. However, quantitative assessments of frictional heterogeneity from geodetic observations while fully incorporating fault mechanics are lacking due to the difficulties of high-dimensional optimization. In this study, we aim to address this gap using Physics-Informed Neural Networks (PINNs) to link frictional heterogeneity with geodetic observations. PINNs employ a neural network to represent the spatially variable frictional properties, making their estimation feasible. Targeting the 2010 Bungo SSE in southwest Japan, our estimation reveals heterogeneous friction coinciding with localized SSE nucleation in southwest Shikoku, and subsequent westward propagation. The calculated fault slip of SSE successfully reproduces the spatio-temporal pattern of observed surface displacements. This PINN-based inversion provides a mechanically consistent fault slip model validated through quantitative comparison with observations. Furthermore, we forecast the future fault slip
evolution, demonstrating the importance of assimilating observations spanning multiple SSE cycles. Our results demonstrate the potential of PINN for advancing understanding of fault mechanics and enabling physics-based fault slip
forecasting.
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All authors Rikuto Fukushima, Masayuki Kano, Kazuro Hirahara, Makiko Ohtani
Space Physics
$1/f$ Noise in Synthetic and Solar Wind Data: Superposition Principles
Jiaming Wang, Francesco Pecora, Rohit Chhiber et al. (+3 more)
The interplanetary
magnetic field exhibits a distinctive $1/f$ spectral density from frequencies of around $\unit[10^{-6}]{Hz}$ to around $\unit[10^{-4}]{Hz}$, ranging from harmonics of the
solar rotation to the reciprocal of the
turbulence correlation time in the spacecraft frame. Various theories have been proposed to explain its origin, typically invoking either processes in the lower corona or in the
solar interior, or local interplanetary dynamics. Here, we investigate the superposition principle that underlies explanations of the
solar/coronal types, which in principle can generate the full observed range of $1/f$ noise. Using synthetic time series with scale-invariant or lognormal distributions of correlation times, we examine the efficacy of several superposition approaches in generating a $1/f$ regime. The persistence of $1/f$ spectrum is further illustrated with decade-long in situ
magnetic field measurements from the ACE spacecraft. Together, these results help explain the ubiquity of $1/f$ noise under the unavoidable superposition inherent in long-duration heliospheric data.
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All authors Jiaming Wang, Francesco Pecora, Rohit Chhiber, Rayta A. Pradata, Subash Adhikari, William H. Matthaeus
Computational Physics · Cross-listed from cs.DC
A Data-Informed Local Subspaces Method for Error-Bounded Lossy Compression of Large-Scale Scientific Datasets
Arshan Khan, Rohit Deshmukh and Ben O'Neill
The growing volume of scientific simulation data presents a significant challenge for storage and transfer. Error-bounded lossy compression has emerged as a critical solution for mitigating these challenges, providing a means to reduce data size while ensuring that reconstructed data remains valid for scientific analysis. In this paper, we present a data-driven scientific data compressor, called Discontinuous Data-informed Local Subspaces (Discontinuous DLS), to improve compression-to-error ratios over data-agnostic compressors. This error-bounded compressor leverages localized spatial and temporal subspaces, informed by the underlying data structure, to enhance compression efficiency and preserve key features. The presented technique is flexible and applicable to a wide range of scientific data, including fluid dynamics, environmental simulations, and other high-dimensional, time-dependent
datasets. We describe the core principles of the method and demonstrate its ability to significantly reduce storage requirements without compromising critical data fidelity. The technique is implemented in a distributed computing environment using MPI, and its performance is evaluated against state-of-the-art error-bounded compression methods in terms of compression ratio and reconstruction accuracy. This study highlights discontinuous DLS as a promising approach for large-scale scientific data compression in high-performance computing environments, providing a robust solution for managing the growing data demands of modern scientific simulations.
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Optics
Fabrication of high-Q defect-free optical nanofiber photonic crystal resonators
Tomofumi Tanaka, Takahiro Suzuki, Owen Mao et al. (+3 more)
We demonstrate the fabrication of defect-free optical-nanofiber photonic-
crystal Fabry-Perot resonators with quality factors exceeding 10^7 using single-shot femtosecond
laser ablation. An investigation of the nonlinear optical properties reveals that thermo-optic effects dominate within the entire cavity bandwidth, even when interrogating with pulses one order of magnitude shorter than the 6.6 us thermal cutoff time. The combination of high-Q and small mode volume of these resonators could facilitate the creation of high-speed
quantum nodes for cavity QED based
quantum computing and networking, as well as low-power in-line fiber optical switches.
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All authors Tomofumi Tanaka, Takahiro Suzuki, Owen Mao, Samuel K. Ruddell, Karen E. Webb, Takao Aoki
Chemical Physics
Aufbau Suppressed Coupled Cluster Theory for Doubly Excited States
Qasim Javed, Harrison Tuckman and Eric Neuscamman
We generalize the Aufbau suppressed coupled cluster formalism into the realm of doubly excited states by deriving, implementing, and testing a wave function initialization strategy that allows the zeroth order wave function to match the largest configurations of a doubly excited reference wave function while maintaining the method's overall asymptotic cost parity with ground state singles and doubles theory. Starting from state-averaged complete active space self consistent field references, this approach produces highly accurate excitation energies for states dominated by a single doubly excited determinant, as well as states in glyoxal and similar molecules where two different doubly excited determinants have large weights. Typical excitation energy errors in both types of states are on the order of 0.15 eV, with the largest observed error being 0.3 eV. These errors stand in stark contrast to equation of motion methods, where typical errors are 4 to 6 eV at the singles and doubles level and 0.4 to 0.8 eV at the full triples level. It remains an open question how best to generalize the Aufbau suppression approach into an even wider variety of multi-configurational double excitations, but these early results offer strong motivation for further investigation.
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Optics
Lateral shearing optical diffraction tomography of brain organoid with reduced spatial coherence
Pawel Goclowski, Julianna Winnik, Vishesh Dubey et al. (+6 more)
Optical diffraction
tomography (ODT) is a powerful technique for quantitative, label-free reconstruction of the three-dimensional refractive index (RI) distribution of biological samples. While ODT is well established for imaging thin, weakly scattering samples, it encounters significant challenges when applied to heterogeneous, strongly scattering thick samples such as tissues and organoids. In this work, a novel common-path interferometric approach to ODT is presented, specifically designed for the RI reconstruction of heterogeneous and highly scattering samples at high temporal stability. The proposed technique, termed lateral shearing (LS)-ODT, incorporates partial lateral shearing off-axis interferometry to suppress the effects of multiple scattering, similar to the mechanism in differential interference contrast (DIC) microscopy, which is widely used for imaging thick specimens. Additionally, the LS-ODT system uses dynamic speckle illumination to enhance both spatial phase and RI sensitivity compared to
laser-based ODT systems. The effectiveness of this method is demonstrated through experiments on a
cell phantom. Its robustness and accuracy are further validated across a wide range of samples, including mouse kidney tissue sections and
brain organoids derived from human induced pluripotent stem
cells (iPSCs), in both thin and thick sections. Furthermore, correlative fluorescence and RI
tomography of the organoids highlights the potential of LS-ODT to enhance and support a broad spectrum of biomedical studies, particularly in the field of histology.
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All authors Pawel Goclowski, Julianna Winnik, Vishesh Dubey, Piotr Zdankowski, Maciej Trusiak, Ujjwal Neogi, Mukesh Varshney, Balpreet S. Ahluwalia, Azeem Ahmad
Computational Physics · Cross-listed from cs.LG
Techno-economic optimization of a heat-pipe microreactor, part II: multi-objective optimization analysis
Paul Seurin and Dean Price
Heat-pipe microreactors (HPMRs) are compact and transportable
nuclear power systems exhibiting inherent safety, well-suited for deployment in remote regions where access is limited and reliance on costly fossil fuels is prevalent. In prior work, we developed a design optimization framework that incorporates techno-economic considerations through surrogate modeling and reinforcement learning (RL)-based optimization, focusing solely on minimizing the levelized cost of electricity (LCOE) by using a bottom-up cost estimation approach. In this study, we extend that framework to a multi-objective optimization that uses the Pareto Envelope Augmented with Reinforcement Learning (PEARL)
algorithm. The objectives include minimizing both the rod-integrated peaking factor ($F_{\Delta h}$) and LCOE – subject to safety and operational constraints. We evaluate three cost scenarios: (1) a high-cost axial and drum reflectors, (2) a low-cost axial reflector, and (3) low-cost axial and drum reflectors. Our findings indicate that reducing the solid moderator radius, pin pitch, and drum coating angle – all while increasing the fuel height – effectively lowers $F_{\Delta h}$. Across all three scenarios, four key strategies consistently emerged for optimizing LCOE: (1) minimizing the axial reflector contribution when costly, (2) reducing control drum reliance, (3) substituting expensive tri-structural isotropic (TRISO) fuel with axial reflector material priced at the level of graphite, and (4) maximizing fuel burnup. While PEARL demonstrates promise in navigating trade-offs across diverse design scenarios, discrepancies between surrogate model predictions and full-order simulations remain. Further improvements are anticipated through constraint relaxation and surrogate development, constituting an ongoing area of investigation.
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Atomic Physics · Cross-listed from quant-ph
Comment on "Determining angle of arrival of radio-frequency fields using subwavelength, amplitude-only measurements of standing waves in a Rydberg atom sensor"
M. Chilcott and N. Kjærgaard
We discuss the consequence of excluding allowed RF-transition between substates of a field-dressed Rydberg
manifold when predicting the spectrum that will be observed if the dressed system is probed in an optical EIT scheme.
Fluid Dynamics
Explainable deep learning reveals the physical mechanisms behind the turbulent kinetic energy equation
Francisco Alcántara-Ávila, Andrés Cremades, Sergio Hoyas et al. (+1 more)
In this work, we investigate the physical mechanisms governing turbulent kinetic energy transport using explainable deep learning (XDL). An XDL model based on SHapley Additive exPlanations (SHAP) is used to identify and percolate high-importance structures for the
evolution of the turbulent kinetic energy budget terms of a turbulent channel flow at a friction Reynolds number of $Re_\tau = 125$. The results show that the important structures are predominantly located in the near-wall region and are more frequently associated with sweep-type events. In the viscous layer, the SHAP structures relevant for production and viscous
diffusion are almost entirely contained within those relevant for dissipation, revealing a clear hierarchical organization of near-wall
turbulence. In the outer layer, this hierarchical organization breaks down and only velocity-pressure-gradient correlation and turbulent transport SHAP structures remain, with a moderate spatial coincidence of approximately $60\%$. Finally, we show that none of the coherent structures classically studied in
turbulence are capable of representing the mechanisms behind the various terms of the turbulent kinetic energy budget throughout the channel. These results reveal dissipation as the dominant organizing mechanism of near-wall
turbulence, constraining production and viscous
diffusion within a single structural hierarchy that breaks down in the outer layer.
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All authors Francisco Alcántara-Ávila, Andrés Cremades, Sergio Hoyas, Ricardo Vinuesa
Geophysics
Evolving beyond collapse: An adaptive particle batch smoother for cryospheric data assimilation
Kristoffer Aalstad, Esteban Alonso-González, Norbert Pirk et al. (+3 more)
We present a new adaptive particle-based data assimilation scheme for cryospheric applications that leverages promising developments in importance sampling. The proposed approach seeks to combine some of the advantages of two widely used classes of schemes: particle methods and iterative ensemble Kalman methods. Specifically, it extends the PBS that is commonly used in cryospheric data assimilation, with the AMIS
algorithm. This adaptive formulation transforms the PBS into an iterative scheme with improved resilience against ensemble collapse and the ability to implement early-stopping strategies. As such, computational cost is automatically adapted to the complexity of the problem at hand, even down to the grid-
cell and water year level in distributed multiyear simulations. In homage to the schemes that it builds on, we coin this new
algorithm the Adaptive Particle Batch Smoother (AdaPBS) and we test it across a range of scenarios. First, we conducted an intercomparison of some of the most commonly used cryospheric data assimilation
algorithms using MCMC simulation as a costly gold-standard
benchmark in a simplified temperature index model assimilating snow depth observations. We further evaluated AdaPBS by assimilating snow depth observations from the ESMSnowMIP project at 6 different sites spanning 3 continents, using an ensemble of simulations generated with the more complex FSM2. Our results demonstrate that AdaPBS is a robust and reliable tool, outperforming or at least matching the performance of other commonly used
algorithms and successfully handling complex cases with dense observational
datasets. All experiments were carried out using the open-source MuSA toolbox, which now includes AdaPBS and MCMC among the growing list of available cryospheric data assimilation methods.
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All authors Kristoffer Aalstad, Esteban Alonso-González, Norbert Pirk, Sebastian Westermann, Clarissa Willmes, Ruitang Yang
Fluid Dynamics
Mechanisms of particle entrainment in confined gas-particle systems under moving boundaries
Arata Hashimoto, Ryosuke Mitani, Toshiki Imatani et al. (+1 more)
Particle entrainment in confined gas-particle systems driven by moving boundaries is central to many industrial and natural processes, including pharmaceutical manufacturing, food processing, and chemical engineering. Although often termed a "suction effect," its physical origin remains unclear, especially under unsteady flow, strong particle interactions, and transient force networks. Here we study suction-induced entrainment in a prototypical confined system using high-fidelity coupled CFD-DEM simulations resolving unsteady gas flow and discrete particle motion with moving boundaries. By decomposing the forces on individual particles, we show that suction is not purely pressure-driven, but results from the combined action of pressure-gradient and unsteady drag forces generated by boundary-accelerated flow. Despite the heterogeneous and transient force fields, the final entrained mass is found to be governed primarily by a single energetic measure: the mechanical work performed on the particle assembly in the entrainment direction during boundary motion, rather than by peak instantaneous forces. Varying boundary kinematics demonstrates that changes in displacement or velocity history control entrainment mainly by modifying the duration over which fluid-particle forces perform work. These results reveal an organizing principle for suction-driven entrainment and establish a work-based framework for boundary-induced particle transport in confined gas-particle systems.
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All authors Arata Hashimoto, Ryosuke Mitani, Toshiki Imatani, Mikio Sakai
Computational Physics
Two-Step Diffusion: Fast Sampling and Reliable Prediction for 3D Keller–Segel and KPP Equations in Fluid Flows
Zhenda Shen, Zhongjian Wang, Jack Xin et al. (+1 more)
We study fast and reliable generative transport for the 3D KS (Keller-Segel) and KPP (Kolmogorov-Petrovsky-Piskunov) equations in the presence of fluid flows with the goal to approximate the map between initial and terminal distributions for a range of physical parameters $\sigma$ under the Wasserstein metric. To minimize the inaccuracy of direct Wasserstein solver, we propose a two-stage pipeline that retains one-step efficiency while reinstating an explicit $W_2$ objective where it is tractable. In Stage I, a Meanflow-style regressor yields a deterministic, one-step global transport that moves particles close to their terminal states. In Stage II, we freeze this initializer and train a near-identity corrector (Deep Particle, DP) that directly minimizes a mini-batch $W_2$ objective using warm-started optimal transport couplings computed on the Meanflow outputs. Crucially, after the one-step transport (from Stage I) concentrating mass on the approximated correct support, the induced geometry stabilizes high-dimensional $W_2$ computation of the direct Wasserstein solver. We validate our construction in the 3D KS and KPP equations subject to fluid flows with ordered and chaotic streamlines.
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All authors Zhenda Shen, Zhongjian Wang, Jack Xin, Zhiwen Zhang
Fluid Dynamics
Learning Differentiable Weak-Form Corrections to Accelerate Finite Element Simulations
Junoh Jung and Emil Constantinescu
We present a differentiable weak-form learning approach for accelerating finite element simulations. Rather than introducing black-box source terms in the strong form of the governing equations, we augment the momentum equation directly in the variational (weak) form with parameterized bilinear operators. The coefficients of these operators are learned from high-resolution simulations so that unresolved small-scale dynamics can be represented on coarse grids. Applying the correction at the weak-form level aligns the learned model with the finite element discretization, preserving key numerical structure and better respecting the fundamental properties of incompressible flow. In the same setting, the approach yields solutions that are more accurate and more stable over long time horizons than comparable strong-form corrections. We implement the proposed method in the Firedrake finite element solver and evaluate it on
benchmark problems, including the one-dimensional convection-
diffusion equation and the two-dimensional incompressible Navier-Stokes equations. End-to-end differentiable training is enabled by coupling PyTorch with the Firedrake adjoint framework. Across these tests, the learned variational operators improve long-term accuracy while reducing computational cost. Overall, our results suggest that weak-form learning provides a principled, structure-preserving route to accurate and stable coarse-grid simulations of incompressible flows.
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Applied Physics · Cross-listed from eess.SP
Electromagnetically Consistent Bounds on Information Transfer in Real-World RIS-Parametrized Wireless Channels
Albert Salmi, Ville Viikari and Philipp del Hougne
A reconfigurable intelligent surface (RIS) endows a wireless channel with programmability that can be leveraged to optimize wireless information transfer. While many works study
algorithms for optimizing such a programmable channel, relatively little is known about fundamental bounds on the achievable information transfer. In particular, non-trivial bounds that are both electromagnetically consistent (e.g., aware of mutual coupling) and in line with realistic hardware constraints (e.g., few-bit-programmable, potentially lossy loads) are missing. Here, based on a rigorous multiport network model of a single-input single-output (SISO) channel parametrized by 1-bit-programmable RIS elements, we apply a semidefinite relaxation (SDR) to derive a fundamental bound on the achievable SISO channel gain enhancement. A bound on the maximum achievable rate of information transfer at a given noise level follows directly from Shannon's
theorem. We apply our bound to several numerical and experimental examples of different RIS-parametrized
radio environments. Compared to electromagnetically consistent
benchmark bounding strategies (a norm-inequality bound and, where applicable, a relaxation to an idealized beyond-diagonal load network for which a global solution exists), we consistently observe that our SDR-based bound is notably tighter. We reach at least 64 % (but often 100 %) of our SDR-based bound with standard discrete optimization techniques. The applicability of our bound to concrete experimental systems makes it valuable to inform wireless practitioners, e.g., to evaluate RIS hardware design choices and
algorithms to optimize the RIS configuration. Our work contributes to the development of an electromagnetic information theory for RIS-parametrized channels as well as other programmable wave systems such as dynamic metasurface
antennas or real-life beyond-diagonal RISs.
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Fluid Dynamics
A Practical Computational Hemolysis Model Incorporating Biophysical Properties of the Red Blood Cell Membrane
Nico Dirkes and Marek Behr
Purpose: Hemolysis is a key issue in the design of blood-handling medical devices. Computational prediction of this phenomenon is challenging due to the complex multiscale nature of blood. As a result, conventional approaches often fail to predict hemolysis accurately, commonly showing deviations of multiple orders of magnitude compared to experimental data. More accurate models are typically computationally expensive and thus impractical for real-world applications. This work aims to fill this gap by presenting accurate yet simple and efficient computational hemolysis models. Methods: Hemolysis modeling relies on two key components: a red blood
cell model and a hemoglobin release model. In this work, we compare three red blood
cell models: a common stress-based model (Bludszuweit), a simple strain-based model based on the Kelvin-Voigt constitutive law, and a more complex tensor-based model (TTM). Further, we compare two hemoglobin release models: the widely used power-law approach and a biophysical pore formation model. Results: We evaluate these models in two
benchmark cases: the FDA blood pump and the FDA nozzle. In both
benchmarks, the simple strain-based model combined with the pore formation model achieves absolute predictions of hemolysis within the standard deviation of experimental measurements. In contrast, stress-based power law models deviate by several orders of magnitude. Conclusion: The strain-based pore modeling approach takes into account the biophysical properties of red blood
cell membranes, in particular their viscoelastic deformation behavior and hemoglobin release through membrane pores. This leads to significantly improved hemolysis predictions in a framework that can easily be integrated into common CFD workflows.
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Chemical Physics · Cross-listed from cond-mat.mtrl-sci
Global Plane Waves From Local Gaussians: Periodic Charge Densities in a Blink
Jonas Elsborg, Felix Ærtebjerg, Luca Thiede et al. (+3 more)
We introduce ELECTRAFI, a fast, end-to-end differentiable model for predicting periodic charge densities in crystalline materials. ELECTRAFI constructs anisotropic Gaussians in real space and exploits their closed-form
Fourier transforms to analytically evaluate plane-wave coefficients via the Poisson summation formula. This formulation delegates non-local and periodic behavior to analytic transforms, enabling reconstruction of the full periodic charge density with a single inverse FFT. By avoiding explicit real-space grid probing, periodic image summation, and spherical harmonic expansions, ELECTRAFI matches or exceeds state-of-the-art accuracy across periodic
benchmarks while being up to $633 \times$ faster than the strongest competing method, reconstructing
crystal charge densities in a fraction of a second. When used to initialize DFT calculations, ELECTRAFI reduces total DFT compute cost by up to ~20%, whereas slower charge density models negate savings due to high inference times. Our results show that accuracy and inference cost jointly determine end-to-end DFT speedups, and motivate our focus on efficiency.
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All authors Jonas Elsborg, Felix Ærtebjerg, Luca Thiede, Alán Aspuru-Guzik, Tejs Vegge, Arghya Bhowmik
Biological Physics
Controlled drug delivery from chitosan-coated heparin-loaded nanopores anodically grown on nitinol shape-memory alloy
M. Moradi, E. Salahinejad, E. Sharifi et al. (+1 more)
Nitinol (NiTi shape-
memory alloy) is an interesting candidate in various medical applications like dental, orthopedic, and cardiovascular devices, owing to its unique mechanical behaviors and proper biocompatibility. The aim of this work is the local controlled delivery of a cardiovascular drug, heparin, loaded onto nitinol treated by electrochemical anodizing and chitosan coating. In this regard, the structure, wettability, drug release kinetics, and
cell cytocompatibility of the specimens were analyzed in vitro. The two-stage anodizing process successfully developed a regular nanoporous layer of Ni-Ti-O on nitinol, which considerably decreased the sessile water contact angle and induced hydrophilicity. The application of the chitosan coatings controlled the release of heparin mainly by a diffusional mechanism, where the drug release mechanisms were evaluated by the Higuchi, first-order, zero-order, and Korsmeyer-Pepass models. Human umbilical cord endothelial
cells (HUVECs) viability assay also showed the non-cytotoxicity of the samples, so that the best performance was found for the chitosan-coated samples. It is concluded that the designed drug delivery systems are promising for cardiovascular, particularly stent applications.
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All authors M. Moradi, E. Salahinejad, E. Sharifi, L. Tayebi
Plasma Physics
Detecting Solenoidal Plasma Turbulence via Laser Polarization Rotation
Kenan Qu and Nathaniel J. Fisch
Recent theoretical studies suggest that solenoidal
turbulence can significantly enhance fusion reactivity, yet no standard diagnostic exists to directly measure these solenoidal flows in high-energy-density
plasmas, nor to distinguish between solenoidal and compressional
turbulence. We propose a method that directly diagnoses the energy and spatial structure of this rotational
turbulence using the cross-polarization scattering of a probe
laser. By coupling to the
plasma vorticity, the scattering generates a cross-polarized signal proportional to the turbulent vorticity, effectively acting as a calorimeter for shear flows. We identify a diffractive scattering signature analogous to “Debye-Scherrer ring” that reveals the eddy size distribution. We show that this technique is applicable to National Ignition Facility (NIF) implosion conditions and other high-energy-density scenarios.
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Accelerator Physics
Laboratory Tests of Laser Control of Electron Beams for Future Colliders
Claire Munting, Peter Kicsiny, Edoardo Barbi et al. (+3 more)
Laser-driven Compton backscattering (CBS) has been proposed as method for controlling the intensity of colliding bunches in the FCC-ee so as to avoid the flip-flop instability caused by intensity asymmetry in colliding bunches.
Laser-based collimation has also been proposed as an indestructible collimator for high-intensity electron beams. We have initiated a laboratory-based test program of these concepts with the E344 experiment at FACET-II. In this paper, we describe simulations of
laser-beam interactions at FACET-II and the relevant scaling for FCC-ee. We also describe the experimental setup and diagnostics that will be used to make the measurements at FACET-II.
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All authors Claire Munting, Peter Kicsiny, Edoardo Barbi, Noe Gonzalez, Spencer Gessner, Illya Drebot
Physics and Society
Modeling Two-Scale Rank Distributions via Redistribution Dynamics or an Analytic Derivation of the Beta Rank Function
Oscar Fontanelli and Wentian Li
Beta Rank Function (BRF) is a two-sided distribution characterized by a smooth peak and double powerlaw decay, widely used to model empirical data exhibiting deviations from pure power laws. In this paper, we introduce a novel two-step generative process that produces data exactly following the BRF distribution. The first step involves any mechanism generating a power-law distribution, while the second step applies a regressive redistribution process that reallocates resources from poorer to richer entities, thereby amplifying inequality. This approach represents the first analytic derivation of an exact BRF distribution from a generative mechanism. We validate the model through applications to income and urban population distributions. Beyond exact generation, this framework offers new insights into the systemic origins of deviations from power laws frequently observed in complex systems, linking rank distributions to underlying feedback and redistribution dynamics.
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Optics · Cross-listed from math-ph
Non-Hermitian Fabry-Pérot Resonances
Habib Ammari, Erik Orvehed Hiltunen, Bowen Li et al. (+4 more)
We characterise non-Hermitian Fabry-Pérot resonances in high-contrast resonator systems and study the properties of their associated resonant modes from continuous differential models. We consider two non-Hermitian effects: the exceptional point degeneracy and the skin effect induced by imaginary gauge potentials. Using the propagation
matrix formalism, we characterise these two non-Hermitian effects beyond the subwavelength regime. This analysis allows us to (i) establish the existence of exceptional points purely from radiation conditions and to (ii) prove that the non-Hermitian skin effect applies uniformly across resonant modes, yielding broadband edge localisation.
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All authors Habib Ammari, Erik Orvehed Hiltunen, Bowen Li, Ping Liu, Jiayu Qiu, Yingjie Shao, Alexander Uhlmann
Fluid Dynamics
Numerical simulations of simultaneous pair-drop impacts and their energetics
Ziyao Zhang, Alfonso A.Castrejon-Pita and Wouter Mostert
We present three-dimensional direct numerical simulations of the simultaneous impact of two identical drops on an hydrophobic substrate, varying the relative strength of capillary and viscous effects respectively through Weber and Reynolds numbers of impact. The interaction between the two drops is characterized by the appearance of a lamella arising from the collision of the two droplets' spreading rims. We examine the width, the height, and the general morphological
evolution of the central sheet; the numerical data is validated against prior experiments and used to guide the development of an energetic model for the maximum elevation of the central sheet. In particular, the rise of the central sheet resembles the spreading behaviour single-drop impacts, especially at high Weber and Reynolds numbers. This fact can be used to estimate scalings in the capillary- and viscous-dominated regimes, which can be used to collapse the trajectories. These insights provide a route for a more complete understanding of the dynamics for the central rising sheet, and anticipate the detailed study of its fragmentation characteristics
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