General Physics
A Braak-Inspired Spectral Determinant Approach to a Dirac Bound-State Problem
Saber Zarrinkamar
We develop a parity-resolved spectral-connection construction for a one-dimensional Dirac particle in an attractive soft-core Coulomb potential. The square-root dependence of the potential produces algebraic branch points in the coordinate plane. We uniformize these branch points by introducing \(t=\sqrt{x^2+\beta^2}\), while retaining the full-line \(Z_2\)
symmetry generated by \(\Pi=\sigma_z P_x\). In each parity sector the resulting problem is formulated as a global connection problem between a solution selected at the physical origin and the decaying solution at infinity. This defines parity-resolved spectral connection functions whose zeros determine bound-state energies. We further construct a quasi-exact Bethe-ansatz sector directly from the same Dirac equations and use its
polynomial solutions as independent
benchmarks of the spectral condition. The comparison clarifies the distinction between local quasi-exact solvability and the global spectral determinant. The construction is inspired by the role of discrete
symmetry and global analyticity in the
quantum Rabi model, without identifying the present spectral function with Braak's \(G\)-function.
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Physics and Society
Strategic Governance of AI Models in Earth Science
Makoto Kelp, Amirhossein Arzani, Patricia Castellanos et al. (+6 more)
AI foundation models pretrained on weather and
climate data are increasingly fine-tuned to Earth science tasks well beyond weather forecasting. Their development and adoption are outpacing the scientific community's ability to evaluate them. These models are judged almost entirely by
benchmark skill metrics, which measure how closely a forecast reproduces a reference product but not whether a model represents the physical processes governing the system it predicts. Forecast skill and physical reliability are therefore distinct properties. The distinction is most consequential under the nonstationary conditions of a changing
climate for which these models were never trained. We identify five priorities for the physical evaluation of AI models in Earth science from task-specific emulators to foundation models, spanning training data, fine-tuning, behavioral testing, mechanistic interpretability, and output validation. We recommend three activities for the coming decade: 1) open AI-ready evaluation
datasets, 2) a shared reporting standard for physics-based evaluation, and 3) a dedicated research program on the safety of these models.
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All authors Makoto Kelp, Amirhossein Arzani, Patricia Castellanos, Paul Griffiths, Ivan Higuera-Mendieta, Manuel Perez-Carrasco, Viral Shah, Patrick Obin Sturm, James Weber
Atomic Physics
On hyperfine structure of the rotationally excited $1 s_{\mu} n\ell_{e}$-states in helium muonic atoms
Alexei M. Frolov
The new approach is develop to determine the hyperfine structure of the rotationally excited $1 s_{\mu} n \ell_{e}$-states in the three-body helium muonic ${}^{3}$He $\mu \; e$ and ${}^{4}$He $\mu \; e$ atoms. The formulas derived in this study allow one to evaluate the hyperfine structure splittings for an arbitrary rotationally excited (bound) $1 s_{\mu} n \ell_{e}$-state in the three-body helium muonic atoms. Similar formulas have never been produced in earlier studies and this explains why these hyperfine structure splittings are still unknown for these bound states.
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Geophysics
Quantum spin regulates the catalytic cycle beyond binding energy
K. Regenauer-Lieb, W. Li, Z. Xia et al. (+7 more)
Conventional catalytic descriptors evaluate symmetric, dissipative responses ($\mathcal{D}$), remaining blind to the antisymmetric, non-dissipative circulation ($\mathcal{L}$) associated with cyclic catalyst turnover. We address this by applying an augmented Onsager operator, $\mathcal{A} = \mathcal{D} + \mathcal{L}$, to catalytic interfaces. Through Onsager–Casimir parity rules, we show that electron
spin governs this non-dissipative circulation within the reduced state space, as antisymmetric coupling necessitates a time-reversal-odd coordinate. We map these multi-physical couplings (Thermo-Hydro-Mechanical-Chemical-Electrical-
Spin) using a Hasse diagram and introduce a basis-invariant Gateway number to quantify circulation relative to dissipation, thereby expanding standard one-dimensional volcano plots into a two-dimensional activity surface. Using metal–nitrogen single-atom sites on doped carbon as a model, published electronic-structure calculations supply the static response quantities from which both operator components can be constructed. Finally, we propose an explicitly falsifiable experimental test utilising
magnetic field modulation near a
spin-crossover to probe the effect of
quantum spin on this non-dissipative regime.
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All authors K. Regenauer-Lieb, W. Li, Z. Xia, Y. Jiao, Y. Liu, Y. Chen, Q. Xie, Q. P. Sun, B. Hamblin, V. Calo
Chemical Physics · Cross-listed from cs.LG
DISSOLVR: An Interpretable and Fast Framework for Aqueous and Organic Solubility Prediction
Vansh Ramani, Har Ashish Arora, Dhairya Kuchhal et al. (+2 more)
High-fidelity solubility prediction is fundamental to pharmaceutical development and environmental partitioning, where accurate modeling must couple molecular structure with thermodynamic behavior across diverse chemical environments. However, recent advancements have been dominated by deep learning architectures that often sacrifice physical interpretability for predictive power. We challenge this trend by showing that state-of-the-art performance does not require such non-transparent architectures. To address this, we introduce DISSOLVR, a transparent framework for molecular solubility prediction. In addition, we perform a comprehensive literature review and a benchmarking study against various methods. We show that DISSOLVR approaches the aleatoric limit of experimental uncertainty and achieves OOD generalization through structural invariance, derived by mapping molecules to physically-grounded descriptors. Then, we present an LLM-assisted post-hoc explanation pipeline that bridges the gap between symbolic model artifacts and chemically grounded narratives. Finally, a comparative
benchmark of a survey involving 22 expert chemists reveals that expert evaluators provide deep insights.
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All authors Vansh Ramani, Har Ashish Arora, Dhairya Kuchhal, Sayan Ranu, Tarak Karmakar
Biological Physics · Cross-listed from cond-mat.soft
triangulax: Differentiable Simulations with Triangular Meshes across Soft Matter Physics
Samuel Silliman, Qiwei Yu and Nikolas Claussen
Many physical and biological systems -foams, membranes, elastic shells, or tissue sheets- can be mathematically described as 2D surfaces. These surfaces can host complex dynamics, like reaction-
diffusion systems, and undergo drastic deformations. Here, we present triangulax, an open-source Python library for simulations and geometry processing that discretizes surfaces using triangular meshes available at https://github.com/nikolas-claussen/triangulax. It combines two features. First, discrete differential geometry provides coordinate-free discretization, which allows simulating strongly deforming surfaces. Second, the library is built on the machine-learning framework JAX, so mesh-based energies and entire simulations can be differentiated automatically (including simulations with
stochastic forces or topological modifications). This simplifies multi-physics simulations by computing forces automatically and allows fitting model parameters to experimental data or design objectives. We implement an
algorithm to preserve mesh quality in strongly deforming surfaces and apply it to a model of membrane mechanics coupled to an on-membrane concentration field. We validate our approach against exact solutions and existing software, and demonstrate triangulax across a range of problems, including membrane mechanics, vertex models, and inverse design of a shape-morphing elastomer sheet.
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Atmospheric and Oceanic Physics
Dependence of the precipitation intensity distribution on spatial and temporal resolution in observations
Akshay Rajeev, Maxim A. Masleyev and Angeline G. Pendergrass
Precipitation intensity varies with both spatial and temporal resolution, with broad implications across
climate science, yet the nature of this dependence is poorly characterized. We investigate how precipitation intensity distributions change with spatial and temporal resolutions and introduce a framework that approximates these changes between resolutions. Using merged satellite-gauge data regridded to various grid sizes, we construct precipitation amount distributions for spatial resolutions ranging from a base resolution of 0.1$^\circ$ for IMERG and 1$^\circ$ for the FROGS database to a coarse 10$^\circ$. Similarly, we use gauge observations coarsened to temporal resolutions ranging from 1-minute to 24-hours to construct precipitation amount distributions. This approach allows us to remove physical differences in order to isolate the effect of resolution on precipitation. Results show that the precipitation intensity distribution varies across spatial and temporal resolutions even without changes in physical processes. We demonstrate that these resolution-dependent changes in the precipitation amount distribution can be effectively characterized by shifting the distribution in logarithmic space without changing its shape, called the shift mode. Our findings emphasize the need to account for the effects of scale on precipitation when comparing and interpreting differences in gridded precipitation from models or observations.
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Physics and Society
Professional Perspectives on Community and Public Engagement with Fusion Energy
Jared Owens, Shanna R. Daly, Porter Malcolm et al. (+1 more)
Fusion energy technology is rapidly approaching demonstrator deployment, but relatively little consideration has been given to publics' perceptions of or engagement with the technology. This research explores the perspectives of diverse professionals involved with the US fusion energy industry as to their knowledge on, valuation of, and preparedness for community and public (C&P) engagement on fusion energy. Participants perceived multifaceted value in C&P engagement from both industry and personal perspectives including an expressed desire for self-involvement in engagement activities, though ideally with increased support from home organizations and from engagement experts. Participants also revealed framings of fusion C&P engagement that align with deficit and contextual models of science communication thinking. These findings suggest an opportunity for collaboration between fusion professionals and engagement experts beyond one-way science communication to more participatory engagements with implications for the future of fusion energy.
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All authors Jared Owens, Shanna R. Daly, Porter Malcolm, Aditi Verma
Fluid Dynamics · Cross-listed from cond-mat.soft
Steady granular discharge across aperture shapes
Kevin Yifan Zhao, Ram Sudhir Sharma, Alexandre Leonelli et al. (+2 more)
We recently proposed a framework for the steady mass flux of particles through circular apertures [R.~S.~Sharma et al., Phys. Rev. Lett. 136, 068204 (2026)]. Here, we combine experiments and discrete-element simulations of spherical particles discharging through triangular and rectangular apertures to test this framework beyond circular openings. Despite changes in aperture shape, we observe a similar free-fall flux, with a modified relevant transport length. Deviations due to confinement reflect packing modifications, which are explained by geometry. A boundary layer dilation imposed by the aperture edge is shown to be a mechanism for the decrease of flux across various aperture shapes. By integrating the local packing deficit over the opening, we identify a hydraulic diameter $D_h=4A/P$, where $A$ and $P$ are the aperture area and perimeter, respectively, as the leading confinement length. This length also provides a reference scale, $\sqrt{gD_h}$, for the mean outlet velocity when the aperture is much larger than an individual particle. The same local packing model predicts the field in a lens-shaped aperture without further fitting, while the normalized discharge rates follow a common trend across the tested shapes.
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All authors Kevin Yifan Zhao, Ram Sudhir Sharma, Alexandre Leonelli, Eckart Meiburg, Alban Sauret
Chemical Physics
libterachem.py: Modular development of electronic structure methods with graphical processing units
Juncheng Harry Zhang, Ruiyan Wang, Andreas Erbs Hillers-Bendtsen et al. (+1 more)
We present libterachem.py, a Python interface and method framework that facilitates development and prototyping of electronic structure methods while leveraging TeraChem's optimized graphical processing unit (GPU) operations. Direct access to these building blocks and their intermediate quantities allows users to assemble and modify methods within Python while reusing the underlying GPU implementations. A layered architecture, programmable at both the operation level and the method level, supports numerical experimentation, composition of complete electronic structure methods with interchangeable components, and integration with external scientific libraries. We illustrate operation-level use with grid
pruning for least-squares tensor hypercontraction and with exchange–correlation functionals supplied through LibXC and by the machine-learned Skala functional. We illustrate method-level use with dispersion corrections, periodic calculations, and geometry optimization with an external optimizer. Python-driven self-consistent-field calculations using libterachem.py incur no significant computational overhead compared to native TeraChem and preserve its multi-GPU scaling. A photoactive yellow
protein QM/MM molecular dynamics application further demonstrates throughput comparable to native TeraChem in a workflow composed with external simulation libraries. These results establish libterachem.py as a platform for method development and interoperable molecular simulation that retains the performance of TeraChem's GPU operations.
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All authors Juncheng Harry Zhang, Ruiyan Wang, Andreas Erbs Hillers-Bendtsen, Todd J. Martínez
Physics and Society
Critical behavior of stochastic rumors on the one-dimensional integer lattice
Nevena Marić, Aleksandar Minić and Pablo M. Rodriguez
We study rumor-spreading models on the one-dimensional integer
lattice, with particular emphasis on the effect of forgetting mechanisms on the long-term behavior of the rumor. We consider the spatial version of the classical Maki–Thompson model together with two variants that incorporate forgetting. Each model is formulated as an interacting particle system and describes the propagation of information through local interactions between neighboring individuals. Our results show that the introduction of forgetting fundamentally changes the behavior of the process. In the classical Maki–Thompson model, the rumor becomes extinct, whereas the two models with forgetting exhibit nontrivial phase transitions between extinction and survival. More precisely, we establish the existence of critical transmission rates that separate regimes in which the rumor dies out almost surely from regimes in which it survives with positive probability. We also investigate the dependence of these critical thresholds on the stifling rate. Combining rigorous arguments with numerical simulations, we prove the existence of the phase transitions, obtain estimates for the critical values, and explore the corresponding phase diagrams. These findings show that forgetting, despite acting as a mechanism that removes spreaders, can qualitatively alter the dynamics of rumor propagation by creating regimes of sustained information spread. Our work provides new insight into the critical behavior of spatial rumor models and raises several questions for future research.
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Optics
Digital twin for full field-of-view Fraunhofer holographic patterning
Antoni J. Wojcik, Dilawer Singh, Hannah J. Joyce et al. (+1 more)
Computer-generated holography allows for nearly complete dynamic control of optical fields, but discrepancies between idealised propagation models and physical hardware limit its performance, particularly across large fields of view. We present a compact differentiable model of a Fraunhofer holographic projector which describes the complete propagation chain, from a phase-only spatial light modulator (SLM) to the camera recording the focal plane of a
Fourier lens. It uses physically interpretable parameters for the SLM, illumination, pupil, coherent stray light, and camera. The model is identified in situ from noise-probed hologram-image pairs without additional hardware, system modification, or camera-in-the-loop refinement. The recovered digital twin is used to generate holograms for patterns spanning the entire first diffraction order of the SLM at its spatial-bandwidth limit. This full-field capability is enabled by measuring field-dependent pupil aberrations and compensating severe inter-pixel crosstalk by operating at the $4\pi$ phase modulation range. The stray light responsible for residual zeroth-order artefacts is recovered in amplitude and phase, allowing its physical origin to be investigated, and its contribution suppressed during hologram synthesis. The work establishes a framework for calibration-aware field synthesis in holographic systems operating in the Fraunhofer regime for applications such as maskless lithography and optical tweezer arrays.
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All authors Antoni J. Wojcik, Dilawer Singh, Hannah J. Joyce, Timothy D. Wilkinson
Plasma Physics
IGNITE Tokamak World Model Architecture
Peter Steiner, Azarakhsh Jalalvand, Nathaniel Chen et al. (+4 more)
We introduce IGNITE, a generative world foundation model for fusion
plasma behavior simulation trained in a self-supervised manner from over a decade of unlabeled experimental data at the DIII-D National Fusion Facility. The core of IGNITE is a dynamics model that can simulate DIII-D discharges from a given set of actuator trajectories. These trajectories can be supplied or generated on-the-fly from a textual
prompt or from desired experimental outcomes. The model architecture consists of several spatio-temporal tokenizers that embed the different input modalities, including time-series like spatio-temporal measurement data, image sequences, and high-resolution spectrograms, each of which collected at vastly different time scales. The backbone is composed of an auto-regressive dynamics model that has the capacity to predict entire DIII-D discharges given initial latent
plasma states and actuator trajectories over a theoretical infinite horizon. IGNITE paves the way towards efficient AI-driven experimental planning and world modeling for nuclear fusion.
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All authors Peter Steiner, Azarakhsh Jalalvand, Nathaniel Chen, Kouroche Bouchiat, Ricardo Shousha, SangKyeun Kim, Egemen Kolemen
Optics · Cross-listed from cond-mat.mes-hall
Quantum materials QED with van der Waals crystals
Suheng Xu, Brian Vermilyea, Ran Jing et al. (+19 more)
A central goal of the emerging field of materials QED is to harness subwavelength electromagnetic confinement in engineered cavities to tailor light-matter interactions. Here, we demonstrate that van der Waals multilayer cavities composed of stacked
graphene and hexagonal boron nitride (hBN) provide unprecedented control over hybridization between their electromagnetic resonances. By magnetically tuning
graphene inter-Landau-level transitions (ILTs) into resonance with hBN
phonons, we witness broadband, mode-selective strong coupling in the mid-infrared. Our
quantum-material QED framework shows that cavity geometry and layer placement jointly control both coupling strength and modal selectivity. For experimentally realistic cavity parameters, the predicted mode splitting exceeds the width of the hBN Reststrahlen band, providing a direct route to the ultrastrong coupling regime. Together, these results establish van der Waals multilayer cavities as a scalable platform for tunable multimode QED and broaden the design space for reconfigurable
quantum materials.
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All authors Suheng Xu, Brian Vermilyea, Ran Jing, Lukas Wehmeier, Jihoon Park, Rafael A. Mayer, Xinzhong Chen, Samuel L. Moore, Fuyang Tay, Boyi Zhou, Wenjun Zheng, Dihao Sun, Birui Yang, Cory R. Dean, Daniel Cazorla, Xu Du, Milan Delor, Andrew J. Millis, Angel Rubio, Michael M. Fogler, Mengkun Liu, D. N. Basov
Space Physics
A Student Sounding Rocket Experiment to Probe Electron Density in the Lower Ionosphere
Joshua Goodwill, Timothy Tomaszewski, Jarrod Bieber et al. (+17 more)
Earth's ionosphere is primarily generated by the Sun's ionization of atoms in the upper atmosphere. This
plasma environment is highly dynamic, exhibits strong spatial and temporal, variations and significantly impacts power systems, navigation, and satellite and ground communications. The University of Delaware Ionospheric Probe (UDIP) was developed as a hands-on learning experience for University of Delaware students on the ionosphere itself and on the development of space technology. Students (mostly undergraduates) have led all aspects of the experiment including mission planning, electronics development, mechanical development, software development, and scientific data analysis. The present study is intended as a guide to readers on how the development of space and near-space flight missions can be successfully incorporated into an undergraduate physics curriculum. The fourth version of the experiment (UDIP-4) flew along with student experiments from across the USA on the 2024 RockSat-C sounding rocket, which was launched from NASA Wallops Flight Facility. During the flight, UDIP-4's Langmuir probe successfully measured the ionosphere's density profile – electron density as a function of altitude – up to an apogee of 114 km. Though the measured density profile agrees well overall with the predictions of theoretical models, UDIP-4 identified a thin layer of enhanced electron density at an altitude of approximately 110 km that is consistent with a sporadic E layer.
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All authors Joshua Goodwill, Timothy Tomaszewski, Jarrod Bieber, Carson P. Brown, Ashton Southwick, Samuel Auerbach, Sreenidhi Banda, Alex Bruce, Marisol Catalan Olais, Markos Duey, Ranyah Khan, Connor McCleery, Jonathan Rosado, Amanda Swenson, Matthew Ward, Jarod Dagney, David Sundkvist, John W. Bonnell, William H. Matthaeus, Bennett A. Maruca
Plasma Physics · Cross-listed from astro-ph.HE
Suppression of spiral density waves in collisionless accretion flows
Stephen Majeski, Matthew W. Kunz and Jason Dexter
Spiral density waves are ubiquitous phenomena in differentially rotating flows. They are non-modal, non-axisymmetric perturbations that, due to shear in the background flow, possess time-evolving wavenumbers, frequencies, and amplitudes. In global simulations of optically thin, low-luminosity black-hole
accretion flows, the excitation of such waves leads to spiral patterns in the
plasma density that influence the morphology of the emission region. As such, the observed variabilities of such
accretion flows are expected to be affected by the behavior of spiral density waves. Thus far, investigations of spiral density waves in
accretion flows have exclusively used the assumption that the
accretion-flow
plasmas are in local thermodynamic
equilibrium (LTE) at all times. In reality, hot and diffuse
accretion-flow
plasmas are expected to deviate far from LTE, and should be modeled accordingly. Here, we employ a combination of kinetic and fluid models to describe spiral density wave propagation, damping, and excitation in a collisionless shearing sheet, which more accurately represents the
plasma conditions of low-luminosity
accretion flows than do models that assume LTE. We find that micro-physical
plasma instabilities are especially important in determining how spiral density waves behave, because the weak particle scattering that they induce leads to appreciable viscous damping. Observational implications of these results include reduced emission-region variability, which may improve agreement between Event Horizon
Telescope (EHT) observations and simulation/theory, as well as a reduction in emission-region pattern speeds compared to simulations employing LTE, which bears on the interpretation of eventual EHT movies.
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Fluid Dynamics
Linear stability of electroconvection in a viscoelastic electrolyte: polymeric-work pathways and eigenmode selection
Minyoung Kim
Electroconvective (EC) instability near ion-selective surfaces plays a key role in electro-chemical transport beyond the
diffusion-limited regime. While EC instability in Newtonian electrolytes has been extensively investigated, the influence of viscoelasticity on the instability mechanism remains less understood. In the present study, we investigate the viscoelastic modulation of EC instability using linear stability, eigenspectrum, and mechanical-work analyses, supported by asymptotic pathway ordering. The analysis reveals a non-monotonic dependence on the Weissenberg number Wi, with the maximum growth rate and minimum critical voltage occurring near Wi ~ O(1). The mechanical-work balance separates electric, solvent-viscous, and polymeric contributions, and further decomposes the polymeric work into normal-stress-difference and shear-stress pathways according to their constitutive origins. For Wi << 1, the
polymer stress recovers Newtonian viscous dissipation at leading order. As Wi approaches unity, the phase efficiencies of the Newtonian-like polymeric pathways decrease substantially, causing the negative polymeric work to collapse and the instability to intensify. At intermediate Wi, an additional minimum in the critical voltage appears at low wavenumbers. Eigenspectrum analysis attributes this feature to the selection of a slowly propagating mode that retains substantial negative polymeric work. At large Wi, this mode no longer determines the instability threshold, and the low-wavenumber minimum disappears. The normal-stress-difference and shear-stress work then nearly cancel, accompanied by stronger ESC-localized shear deformation and predominantly solvent-viscous dissipation. Thus, the non-monotonic response is governed by stress-deformation phase relations, solvent dissipation, and eigenmode selection rather than stress magnitude or a single polymeric contribution.
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Physics and Society
Measuring Diversity and Segregation with Convex Functions
Philip S. Chodrow
The problem of segregation measurement is to quantitatively describe the interaction of a population's attributes (such as race, ethnicity, education level, or income) with a separating distinction (such as organizational rank, school district, or spatial location). This primarily expository article gives an opinionated tour of one mathematical approach to measuring segregation. We begin with some basic intuitions about diversity and unify these through the framework of convex functions on the probability simplex. Formalizing segregation as a local-to-global comparison of diversity measures, resulting in the general class of Jensen informations as segregation measurements. We describe and computationally illustrate three ways to incorporate spatial structure into segregation measurements: spatial smoothing, aggregation, and local Jensen information. We close with several suggestions for future work.
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Fluid Dynamics
Leading-edge energy transfer governs flutter onset of a pitching finite wing in low-Reynolds number flows
Apoorva R. Parvathgari, Tulsi Ram Sahu and Aditya G. Nair
Flutter onset is determined by the sign of the aerodynamic work on the structure, but cycle-integrated work does not reveal where this energy transfer originates. We perform direct numerical simulations of incompressible flow over an aspect-ratio-two NACA 0015 wing pitching at $Re=1000$ and use lobe-resolved force-moment partitioning to identify the vortical contributions to aeroelastic power. The net energy transfer changes sign near $U^*=4.25$. Across this transition, the upper leading-edge contribution changes from damping at $U^*=2$ to excitation near onset and becomes the largest positive contribution at $U^*=6$. Near the stability boundary, positive leading-edge work is nearly balanced by negative trailing-edge work. This change is accompanied by a reorganization of the leading-edge pressure footprint and a reduction in the phase difference between aerodynamic moment and pitch rate, linking flutter onset to the timing of the leading-edge
vortex system. A single slowly growing-amplitude simulation recovers the finite-amplitude energy boundary, while a free-pitching calculation confirms the predicted instability. As the motion approaches its limit cycle, the energy pathways redistribute, and a response-frequency-matched forced calculation reproduces the large-amplitude energy budget. These results distinguish the mechanism that triggers flutter from that which saturates the response and identify the leading edge as a natural target for flutter suppression.
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Geophysics
Multi-Modal Surface Wave Inversion using Physics Guided Variational Autoencoders
Sujith Swaminadhan, Andrey Bakulin, Sergey Fomel et al. (+1 more)
The growth in seismic acquisition techniques has led to large-scale surveys, making traditional manual dispersion analysis logistically and computationally prohibitive. While deep learning techniques can offer an automated alternative, standard convolutional neural networks (CNNs) often fail to account for the non-uniqueness of the inverse problem and can produce geologically unrealistic, smooth velocity models. To address these challenges, we propose a physics-guided multi-modal variational autoencoder (PG-VAE) for scalable near-surface characterization. Our framework introduces a multi-stream fusion strategy that maps independent latent features from both raw shot gathers and spectral-dispersion images by leveraging the physics of Rayleigh-wave dispersion. We implement a specialized decoder architecture with learned transposed convolutions and residual refinement to enforce a structural bias towards layered earth models. Physics guidance is incorporated via a differentiable surrogate network that serves as a forward-consistency regularizer, penalizing predictions that violate the physics of Rayleigh-wave dispersion. The network is trained on diverse synthetic data generated via elastic modeling, and then validated on unseen synthetic data, and subsequently evaluated on an ultra-high-density field
dataset from the Devine test site in Texas. We also employ gradient-weighted class activation mapping (Grad-CAM) to examine the input features contributing to the network predictions. The proposed method could serve as a scalable tool for near-surface characterization and as an initial model-building tool for high-resolution elastic full-waveform inversion.
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All authors Sujith Swaminadhan, Andrey Bakulin, Sergey Fomel, Yangkang Chen
Computational Physics · Cross-listed from math.NA
A Polynomial-Scaling PDE Solver with Entanglement-Basis Tensor Networks
Abhijatmedhi Chotrattanapituk, Michael J. Landry, Chu-Liang Fu et al. (+1 more)
We develop a finite element method (FEM) for partial differential equation (PDE) solver based on the
entanglement-basis representation introduced in our companion work. By lifting non-linear finite-element equations into an augmented coefficient space, the governing PDE together with boundary, initial, and inter-element constraints can be expressed through a unified quadratic residual minimization. Although this augmented space grows exponentially with the number of elements, its tensor-product structure allows it to be represented efficiently using tensor networks. Using the
matrix product state (MPS) as a concrete example, we show that density
matrix renormalization group (DMRG) sweeps enable element-by-element optimization without explicitly constructing the full augmented space. For bounded bond dimension, the resulting computational cost scales polynomially with the number of finite elements. We extend the framework to time-dependent problems through implicit temporal discretization and demonstrate convergence under both mesh and
polynomial refinement using
diffusion equations.
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All authors Abhijatmedhi Chotrattanapituk, Michael J. Landry, Chu-Liang Fu, Mingda Li
Optics
Intrinsic electromagnetic properties
Eduardo Barredo-Alamilla, Timur Z. Seidov, Daniel A. Bobylev et al. (+1 more)
Electromagnetic properties of materials are commonly inferred from how they scatter light. Here we show that this diagnostic is fundamentally incomplete. We predict the existence of an intrinsic electromagnetic property that remains invisible in any scattering experiment independently of geometry, polarization, far-field or near-field excitation. Still, this property affects the radiation emitted by the sources embedded inside the material. We reveal this phenomenon for a specially designed nonreciprocal magnetoelectric material combining
axion and dual
axion responses. Our results establish intrinsic electromagnetic properties as a distinct class of material responses and show that scattering experiments alone are insufficient to characterize matter.
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All authors Eduardo Barredo-Alamilla, Timur Z. Seidov, Daniel A. Bobylev, Maxim A. Gorlach
Geophysics
SW1D: A Python package for seismic surface wave solutions in 1-D elastic media
Piyush Kolhe, Arjun Datta, Shijil Umer et al. (+1 more)
We present SW1D, a Python version of a legacy code for the forward computation of Rayleigh and Love wave dispersion curves as well as synthetic seismograms, in elastic, isotropic, laterally homogeneous media. This code solves the surface wave
eigenvalue problem for 1-D media using a propagator-
matrix technique and subsequently obtains surface wave excitations, in the frequency domain, for a point source described by its moment tensor. SW1D is validated through rigorous theoretical analyses and benchmarked against a well-established seismological software package. Ultimately, we demonstrate its place within an ecosystem of Python-based seismological tools designed for niche applications such as modelling surface wave propagation across lateral discontinuities, or forward modelling of ambient noise cross-correlations with arbitrary spatial distributions of ambient seismic sources.
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All authors Piyush Kolhe, Arjun Datta, Shijil Umer, Bivas Das
Chemical Physics · Cross-listed from quant-ph
Polynomial-time classical and quantum simulation of quantum impurity models
Jiaqing Jiang, Nathan Ju, Ojas Parekh et al. (+2 more)
Quantum impurity models are paradigmatic models of interacting
quantum matter, as well as key computational primitives for modern electronic-structure methods. They describe a small subsystem of interacting
fermions coupled to a large, noninteracting bath. We perform a comprehensive study of the computational complexity of simulating impurity models, delineating the boundary between classical and
quantum tractability for this class of problems. Our main finding is that static properties of
quantum impurity models can be calculated efficiently on a classical computer. Specifically, we give classical
algorithms that (1) estimate the ground-state energy to additive precision $\delta$ in time $\mathrm{poly}(n,\delta^{-1})$, and (2) estimate the partition function at inverse temperature $\beta$ to relative precision $\delta$ in time $\mathrm{poly}(n,\beta,\delta^{-1})$, where $n$ is the system size. These results improve the previous best-known complexity for ground-state energy estimation from quasipolynomial to
polynomial time, while establishing for the first time rigorous
polynomial-time guarantees for simulating impurity models in thermal
equilibrium. On the other hand, we find that simulating dynamical properties of impurity models is hard for classical computers but easy on a
quantum computer. As a canonical example, we show that computing their nonequilibrium Green's functions captures the full power of
quantum computation, even at finite temperature. Taken together, our results rule out superpolynomial
quantum speedups for computing static properties, but provide an avenue for
quantum advantage in simulating impurity physics out of
equilibrium.
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All authors Jiaqing Jiang, Nathan Ju, Ojas Parekh, Chaithanya Rayudu, Andrew Zhao
Plasma Physics
Birth-Potential Multigroup Fluid Model for Ballistic Electrons: Breakdown and Cathode Sheath Regimes
Bernard Parent and Brendan Perry
Fluid models of discharges fail where electron runaway dominates. On the left branch of the Paschen curve and in high-voltage cathode sheaths, the electron population splits into a thermal bulk and a ballistic beam that no single electron fluid can represent. We present a multigroup fluid model that divides ballistic electrons into groups indexed by their birth potential: the electrostatic potential at which they were liberated, offset by the forward kinetic energy they carried. Because the birth potential is a constant of ballistic motion, the electric field never moves electrons between groups and only collisions do. Each group requires a single continuity equation because its velocity follows algebraically from the local potential, and the number of groups is set by the energy resolution alone independently of the mesh. A runaway criterion based on the local field assigns each newly liberated electron to the bulk or a ballistic group, so the model applies even where no sheath exists. Benchmarked against a kinetic solver in argon, it reproduces the left branch of the Paschen curve—which local-field and local-energy models miss or misplace—and tracks the current–voltage characteristic of a cathode sheath to within a few percent at reduced fields exceeding $10^4$ Td, where the local-energy model overpredicts the current up to thirtyfold. Since as few as 10 to 30 groups suffice, depending on the field, the approach brings near-kinetic accuracy to multi-species computational fluid dynamics solvers at the cost of that many additional species, without added velocity-space dimensions or particle noise.
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Medical Physics · Cross-listed from cs.CV
MIRTO: a registration-gated, multiverse-tested evaluation protocol for unsupervised anomaly segmentation in brain MRI
Negin Kafee Hernashki and Soumick Chatterjee
Unsupervised anomaly detection (UAD) methods for
brain MRI are ranked by a single score, yet that score rests on choices that are rarely reported: how each anomaly map is aligned with the reference, how and on which data the threshold is set, and which false-positive budget, metric, aggregation and lesion definition are used. We present MIRTO, an evaluation protocol that makes these choices explicit and measures their effect. It gates the geometry of every comparison with a registration check and label-free diagnostics of known power, sets thresholds on validation data alone and reports the false-positive volume actually realised on test, repeats each comparison over 15,552 defensible evaluation pipelines, and attaches paired subject-bootstrap intervals with multiplicity control. Applied to four UAD methods trained on the same healthy data and tested on 312 BraTS 2020 subjects, MIRTO showed that an axis-order mismatch between stored maps and the reference lowered a
diffusion model's voxel AUROC from 0.873 to 0.583 whilst barely moving its slice-level AUROC. Within each metric, the method explained at least 0.95 of the variance in voxel AUROC and AUPRC and 0.77 in Dice, but only 0.14 in lesion sensitivity, where the lesion definition and hit criterion dominated. A Dice advantage that was significant at validation thresholds vanished at equal realised false-positive burden, and an exact identity attributes it to threshold transfer. A training-free change to REFLECT's latent aggregation raised Dice at equal burden by 0.052. Nine hypotheses were tested against explicit criteria; because the same cohort served to develop the protocol, all inference is exploratory.
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Medical Physics
Diffusion broadening of the point spread function in steady-state MRI
Bibek Dhakal and John C. Gore
Purpose: To demonstrate how
diffusion blurs the longitudinal magnetization in steady-state gradient-echo imaging, quantify resolution dependence on flip angle and repetition time, and the effects on relaxometry at microscopic resolution. Methods:
Diffusion was incorporated into the Bloch-Torrey equation in $k$ space to determine how the point spread function (PSF) of $T_1$-weighted spoiled gradient-echo images varies with flip angle. Theoretical predictions were validated numerically and extended to b-SSFP. Results:
Diffusion acts as a low-pass filter whose width depends on the
diffusion rate ($D$), repetition time (TR), $T_1$ and flip angle ($\alpha$). The blurring length spans from $\sqrt{D \cdot \mathrm{TR}/2}$ at large $\alpha$ to $\sqrt{D T_1}$ as $\alpha \rightarrow 0$, equaling $\sqrt{D T_1/2}$ at the Ernst angle. Consequently, multi-angle acquisitions have mismatched resolution (PSF widths of 39 $\mu$m at $\alpha = 10^\circ$ versus 11 $\mu$m at $\alpha = 80^\circ$ for TR = 50 ms). For the flip-angle pair optimizing variable-flip-angle $T_1$ mapping, the effective resolutions differ by a factor of 2.4. In simulated DESPOT1 maps the $R_1$ of a 30 $\mu$m feature is reduced to 1.80 s$^{-1}$ (true value 2.00 s$^{-1}$). In b-SSFP, blurring depends on $T_1$, $T_2$, and $\alpha$ but is independent of TR, with a minimum of $\sqrt{D T_2}$. Conclusion: The PSF of a steady-state sequence depends on the flip angle and $T_1$. Combining images acquired at different flip angles implicitly assumes identical spatial resolution, but that is not valid at microscopic scales, affecting the accuracy of quantitative relaxometry. The theory identifies a new limitation on the ultimate resolution of MRI set by
diffusion.
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Fluid Dynamics
Spiral motion enables simultaneous homogenization and enhanced transport in granular pipe flow
Wing To Ku, Patric Mueller and Thorsten Poeschel
Density waves in
gravity-driven granular pipe flow reduce transport efficiency and produce large fluctuations in the local solid fraction. Helical wall textures suppress these instabilities but at the expense of reduced throughput. Using discrete element simulations (DEM), \RefA{we find that this homogenization is associated with} spiral particle motion induced by the helical wall texture. Quantifying this motion by the mean angular velocity reveals a strong correlation between spiral motion, flow homogenization, and transport performance. Motivated by this insight, we introduce a helical pipe whose centerline follows a helical path while maintaining a constant circular cross section. This design generates stronger spiral motion than conventional helical wall textures. For suitable design parameters, the helical pipe simultaneously enhances flow homogeneity and increases the mean flow velocity beyond that of a plain straight pipe. These results support spiral motion as a useful diagnostic and design variable for simultaneously improving flow homogeneity and transport efficiency within the investigated parameter range.
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Optics
Thin-capping layer epitaxial quantum dots for near-field quantum photonics
Yuting Guo, Jonathan Bar-David, Pasquale Cilibrizzi et al. (+3 more)
Epitaxial
quantum dots (QDs) are widely recognised as one of the best
quantum light sources, given their good stability, brightness,
quantum efficiency and coherence. To reach such properties, QDs are protected from potentially detrimental surface states by a relatively thick capping layer, typically exceeding 50nm. This prevents the implementation of near-field effects, like plasmonic-based ones, that can dramatically increase the light-matter interaction, since the control of the spontaneous emission dynamics and directionality of the emission can only occur if the emitter is in close proximity (typically tens of nanometers or less) from the metallic structures. In this work, we report the growth and optical characterisation of InAs/GaAs QDs with GaAs capping layer thickness of 10nm, 20nm, and a more standard 95nm. Remarkably, we observe emission linewidths up to 5 times smaller than previously reported, with unperturbed excitonic lifetimes. These results demonstrate that the epitaxial QD's high optical quality can be maintained even when the emitters are at reduced distances from the surface, opening the path for the exploration of near-field light-matter interactions with coherent and stable emitters, suitable for
quantum technology applications.
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All authors Yuting Guo, Jonathan Bar-David, Pasquale Cilibrizzi, Sung-Yul L. Park, Jin Dong Song, Luca Sapienza