Atomic Physics · Cross-listed from quant-ph
Coherence-assisted quantum state preparation and thermal control through repeated interactions
Alessandro Prositto and Dvira Segal
We investigate the relaxation dynamics and steady state of a
qubit interacting with a coherent environment through an energy-conserving flip-flop interaction within the Markovian repeated-interaction framework. We derive an exact analytical solution for the evolving and stationary state, nonperturbative in the environmental coherence. We show that environmental coherence provides a resource for dissipative state preparation by expanding the set of accessible steady states. In particular, coherent ancillas can increase the system ground- state population beyond that of the bath, corresponding to an effective temperature below the bath temperature, and enhance its steady-state purity, with these effects controlled by the interplay between the system energy scale and the system-ancilla coupling. Nevertheless, we prove that the purity of the system can never exceed the purity of the ancilla. We further analyze the thermodynamics of the protocol and show that environmental coherence can modify and even reverse the natural direction of heat transfer, giving rise to anomalous heat flow. Remarkably, for fixed interaction parameters, the direction of the coherence-induced heat transfer is governed by the relative phase between the system and ancilla coherence, whereas their magnitudes determine the amount of heat exchanged. Our results establish environmental coherence as a resource for controlling steady-state preparation and energy transfer in repeated-interaction dynamics.
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Geophysics
Fault-conditioned Seismic Image Generation using Denoising Diffusion Probabilistic Modeling and Neural Style Transfer
Tolulope Agbaje, Nam Pham, Altay Sansal et al. (+1 more)
Seismic data augmentation is critical for training robust deep learning (DL) models for fault detection and characterization, yet conventional approaches remain inadequate.
Geometric transforms, e.g., flips or rotations, and noise injection fail to capture realistic seismic textures, while physics-based forward modeling is computationally expensive and often poorly aligned with the statistical characteristics of field data. We present a denoising
diffusion probabilistic model (DDPM) framework that learns the high-dimensional distribution of seismic images directly from field data via iterative noise injection and learned denoising. We develop two variants: an unconditional model (U-DDPM) that generates realistic post-stack seismic patches and a fault-conditioned model (FC-DDPM) that produces diverse seismic realizations that honor user-specified fault geometries encoded as binary masks. Three open-source field
datasets with differing amplitude scales and processing histories are jointly standardized for training, demonstrating cross-survey generalization. Generated images from U-DDPM reproduce the dominant textural patterns, amplitude distributions, and reflector continuity observed in real seismic data. FC-DDPM maintains structural fidelity to imposed fault masks while introducing realistic variability in surrounding seismic textures, enabling one-to-many realizations from a single structural template. Frequency-wavenumber spectral analysis confirms that synthetic samples preserve the spatiotemporal bandwidth and dip content of the training distribution. The proposed framework provides a practical, controllable augmentation mechanism for expanding limited fault-labeled training sets without computationally expensive forward modeling or survey-specific parameter tuning, directly addressing a key bottleneck in DL-based seismic interpretation.
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All authors Tolulope Agbaje, Nam Pham, Altay Sansal, Yangkang Chen
Optics
Photoluminescence study of Vanadium in 4H- and 6H- Silicon Carbide
Ibrahim Boulares, Anne R. Evans, Jeremy L. Smith et al. (+1 more)
Point defects with optically addressable
spin states are a key building block for
quantum networks, but few solid-state emitters combine a
spin-
photon interface with emission in the low-loss telecommunications band. Vanadium (V4+) defects in silicon carbide (SiC) meet this requirement, emitting zero-
phonon lines (ZPLs) throughout the telecom O-band in both the 4H- and 6H-SiC polytypes while retaining an addressable electron and nuclear
spin system [1,2]. Although the orbital and
spin structure of these defects has been characterized in detail, the polarization selection rules have not been fully mapped experimentally. Here we present polarization-dependent photoluminescence (PL) measurements of vanadium ZPLs in 4H- and 6H-SiC at cryogenic temperature, of two scattering geometries: edge and face-on (emission wavevector perpendicular and parallel to the
crystal c-axis, respectively). Emission from two of the four {\alpha} sites, ({\alpha}2 and {\alpha}3) and the 6H-SiC {\gamma} sites, are polarized parallel to the c-axis ({\sigma}-polarized), whereas emission from {\alpha}1 and {\alpha}4 are polarized perpendicular to the c-axis ({\pi}-polarized). Additionally, we report preliminary, lower signal measurements of the e̱ta} sites.
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All authors Ibrahim Boulares, Anne R. Evans, Jeremy L. Smith, Brenda VanMil
Chemical Physics · Cross-listed from cs.LG
NEMORA: Neural Equivariant Multipole Operators for Long-Range Atomistic Learning
Jay L. Kaplan, Samuel Varner, Rebecca Willett et al. (+1 more)
Equivariant graph neural networks have emerged as foundational architectures for machine-learned interatomic potentials, approaching
quantum-chemical accuracy at a fraction of the computational cost. These models describe local atomic environments accurately, but finite spatial cutoffs truncate long-range information flow, and stacking message-passing layers can lead to over-smoothing and over-squashing. Existing long-range extensions either prescribe a fixed analytical propagation kernel, restrict long-range communication to scalars or degree-preserving channels, are only approximately equivariant, or incur super-linear computational cost. Combining learnable long-range equivariant transport with multiscale many-body expressivity and efficient scaling for larger systems remains a central challenge. We introduce Neural Equivariant Multipole Operators (NEMORA), a neural equivariant extension of the Fast Multipole Method (FMM) for learning long-range tensorial representations. NEMORA generalizes the FMM's analytical multipole expansion and translation operators to learned equivariant counterparts on an adaptive spatial hierarchy. Its operators couple angular degrees and form many-body interactions across length scales, retaining the FMM's hierarchical organization and analytical radial factors as physical inductive biases while learning data-dependent long-range couplings. NEMORA evaluates in linear time and
memory complexity, allowing it to treat larger systems than other long-range methods reaching hundreds of thousands of atoms, and it augments both
symmetry-constrained and unconstrained short-range backbones. On non-local
benchmarks, it reduces force and energy errors relative to the short-range backbones by over an order of magnitude and up to three orders of magnitude, respectively, which is better than or competitive with existing long-range extensions in accuracy.
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All authors Jay L. Kaplan, Samuel Varner, Rebecca Willett, Juan J. de Pablo
Instrumentation and Detectors · Cross-listed from astro-ph.IM
Microwave Loss in Low-Temperature ICPCVD Silicon Nitride: Dependence on Precursor Ratio and RF Substrate Bias
H. Athreya, J. Cornelison, M. Mikhailov et al. (+5 more)
Low-loss dielectric films are an important component of superconducting
circuits with applications ranging from
quantum information to superconducting detectors for astrophysics. In millimeter-wave detector
circuits, dielectric loss can be the limiting factor on efficiency and resonator performance. Some advantageous detector fabrication architectures require dielectric deposition at temperatures substantially lower than those conventionally used to achieve low dielectric loss. In this work, we characterize silicon nitride (SiN$_x$) films deposited by inductively coupled
plasma chemical vapor deposition (ICPCVD) at $150\,^{\circ}\mathrm{C}$ for use in temperature-constrained superconducting detector fabrication. We measure the dielectric loss tangent of these films at $\sim 4-7$ GHz using superconducting niobium microstrip resonators and extract the loss by fitting the temperature-dependent resonant frequency to a two-level-system model. We vary the N$_2$/SiH$_4$ precursor-flow ratio between dielectric depositions and find a clear trend of decreasing loss with decreasing N$_2$/SiH$_4$. We additionally find that applying an RF substrate bias during deposition reduces the loss compared to films deposited without bias. These results identify precursor ratio and substrate bias as practical process parameters for improving low-temperature SiN$_x$ for superconducting detector
circuits.
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All authors H. Athreya, J. Cornelison, M. Mikhailov, R.J.H. van der Kolk, C. Chang, T. Cecil, M. N. Ali, J. A. Zebrowski
Physics Education · Cross-listed from quant-ph
Engineering Quantum Interactions: From Effective Models to Validated Physical Predictions
Sinara S. Dourado, Ciro Micheletti Diniz, Gabriel P. L. M. Fernandes et al. (+3 more)
Effective models make complex
quantum dynamics tractable by retaining only the states, processes, and timescales relevant to a given physical question. Their predictive power, however, depends on treating the retained
manifold, initial state, observables, micromotion, dissipative channels, and eliminated degrees of freedom consistently. We develop a unified framework for constructing and validating effective descriptions using projection methods, Schrieffer–Wolff transformations, Magnus expansions, adiabatic elimination, small rotations, time coarse graining, and Floquet theory. The
quantum Rabi model and its dispersive limits serve as recurring
benchmarks for identifying control parameters, virtual processes, spectral corrections, observable dressing, and breakdown. Higher-order resonances and applications to collective light–matter coupling, Raman processes,
spin-chain buses, lossy mediators, and driven
lattices illustrate how effective interactions can be interpreted and designed. A physical prediction is organized into three order-consistent steps: encoding into the retained description,
evolution under the effective dynamics, and reconstruction of the physical observable. This viewpoint exposes failures caused by leakage, small denominators, neglected micromotion, inconsistent observables,
memory effects, or incorrectly transformed dissipation, while linking derivation, physical interpretation, and mechanism-guided discovery. Companion notebooks provide QuTiP implementations, convergence tests, and microscopic-to-effective comparisons.
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All authors Sinara S. Dourado, Ciro Micheletti Diniz, Gabriel P. L. M. Fernandes, Rogério J. de Assis, G. D. de Moraes Neto, Celso J. Villas-Boas
Optics
Propagation-Invariant Transmission Eigenmodes in Weakly Scattering Media
J.C Osorio-Tascon and P. A. Quinto-Su
Propagation-invariant beams (PIBs) with annular angular spectra preserve their transverse intensity structure over extended propagation distances. Here, we investigate whether scattering invariant modes (SIMs) can simultaneously exhibit scattering and propagation invariance. We measure free-space and sample transmission
matrices (TMs) in an orthonormal annular basis for weak, moderate, and strong scattering media and identify localized SIMs (LSIMs) whose angular spectra remain predominantly confined to one or a few neighboring rings. Under weak scattering, 75 of the 128 SIMs considered for annular localization satisfy this criterion, with LSIMs exhibiting Bessel-like propagation. Under moderate scattering, 25 LSIMs are recovered after filtering low-intensity spectral components, whereas none are found under strong scattering. Although increasing modal crosstalk progressively suppresses annular localization and nondiffracting behavior, SIMs retain higher transverse-intensity correlation over propagation than the Gaussian-like and Bessel beams in the moderate and strong regimes. These results show that annular localization enables SIMs to acquire propagation invariance when scattering is weak, whereas the enhanced propagation robustness of SIMs can persist even after this localization is lost.
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Atomic Physics · Cross-listed from cond-mat.quant-gas
Quantum simulation of material-specific Hubbard models with alkaline-earth atoms
Henning Schlömer, Reuben R. W. Wang, Bo Xing et al. (+2 more)
A long-term goal of
quantum simulation is to faithfully reproduce the low-energy
Hamiltonians of correlated
quantum materials, establishing a direct connection between solid-state experiment, electronic-structure theory, and programmable many-body systems. Here, we introduce an integrated
quantum-simulation architecture based on fermionic alkaline-earth atoms that enables programmable control over the key ingredients of multi-orbital material
Hamiltonians. Long-lived clock states encode synthetic orbitals, their differential polarizabilities generate orbital-selective tunneling, and contact interactions provide multi-orbital Hubbard and Hund couplings. Spatially structured coherent driving on the ultra-narrow clock transition further realizes
symmetry-selective inter-orbital hybridization, while the clock detuning independently controls orbital energy offsets. In this way, the spatial
symmetries of solid-state orbitals can be encoded directly into the effective
Hamiltonian, even though the atoms themselves remain in conventional $s$-wave Wannier orbitals of the lowest optical-
lattice bands. We demonstrate the resulting integrated control using the four-band low-energy
Hamiltonian of the bilayer nickelate La$_3$Ni$_2$O$_7$ as a demanding
benchmark. Self-consistent calculations show that the microscopic parameters obtained from first-principles modeling can be reproduced within a single experimentally accessible
lattice configuration and independently programmed over substantial parameter ranges. Our results establish a general route for translating material-derived multi-orbital
Hamiltonians into programmable ultracold-atom
quantum simulators.
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All authors Henning Schlömer, Reuben R. W. Wang, Bo Xing, Susanne F. Yelin, H. R. Sadeghpour
Applied Physics · Cross-listed from cond-mat.mes-hall
Millisecond phonon lifetimes in piezoelectric nanomechanical resonators
Oliver A. Hitchcock, Matthew P. Maksymowych, Kaveh Pezeshki et al. (+5 more)
Piezoelectric nanomechanical resonators operating at gigahertz frequencies are a promising platform for
quantum information processing and precision sensing, but
phonon lifetimes have been limited to the microsecond range, widely attributed to the higher intrinsic loss of piezoelectric materials. Here we show that this limitation is not fundamental. Using a fabrication process informed by recent studies of defect-mediated loss in lithium niobate, we combine chemical restoration of ion-milled surfaces with a shadow-mask metallization process to produce lithium niobate phononic
crystal resonators with inferred internal
phonon lifetimes of up to 21 milliseconds at 3.6 gigahertz, corresponding to quality factors exceeding $4\times10^8$ ($f\cdot Q_i > 10^{18}$) at millikelvin temperatures. By systematically varying the electrode-to-resonator spacing, we demonstrate that the measured lifetime is set by external coupling between the device and the measurement
circuit rather than material loss. This establishes that lithium niobate, once fabrication-induced surface damage is removed, achieves an intrinsic quality comparable to the best mechanical materials while remaining an intrinsically piezoelectric platform with strong electromechanical coupling.
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All authors Oliver A. Hitchcock, Matthew P. Maksymowych, Kaveh Pezeshki, Takuma Makihara, Erik Szakiel, Chou-Wei Kiang, Daniel Rugar, Amir H. Safavi-Naeini
Data Analysis and Statistics · Cross-listed from cs.SI
Some Twitch chats exhibit multifractal characteristics resembling stream-of-consciousness narrative
Marcin Ból, Stanisław Drożdż, Robert Kluszczyński et al. (+2 more)
Live-streaming platforms generate high-frequency records of collective human activity in which many loosely coordinated users react in real time to shared external stimuli and to one another. We investigate whether the temporal organization of Twitch chat messages exhibits multifractal signatures characteristic of complex dynamical systems. Selected high-activity chats were converted into time series by measuring the length of each consecutive message in words, with platform emotes treated as individual
tokens. The resulting signals display heterogeneous fluctuations, bursty organization, and heavy-tailed distributions that, in several cases, are better captured by q-Weibull forms than by conventional light-tailed models. Autocorrelation analysis reveals persistent temporal dependence over broad ranges of message lags, indicating that the variability is not produced by independent message generation alone. MFDFA shows that some chats possess well-developed multifractal spectra and generalized Hurst exponents strongly dependent on moment order, whereas others exhibit weak scaling, approximately monofractal behavior, or no well-defined scaling regime. Surrogate tests indicate that temporal correlations are essential for the observed multifractality, while heavy tails enhance its apparent strength. Under particular dynamical conditions, Twitch chat may therefore form an emergent collective signal whose fragmented, reactive, associative, and multiscale organization is structurally reminiscent of stream-of-consciousness narrative. This expression is used strictly as a structural and dynamical analogy, not as a claim of a unified collective mind or of semantic, cognitive, or phenomenological equivalence with literary stream-of-consciousness writing. The results position live-stream chat dynamics within the broader physics of complex, intermittent, human-generated signals.
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All authors Marcin Ból, Stanisław Drożdż, Robert Kluszczyński, Jarosław Kwapień, Marcin Wątorek
Classical Physics · Cross-listed from cond-mat.stat-mech
Axiomatic Incompatibility Between Thermal Equilibrium and Volume-Dependent Entropy in Ideal Gas Theory
Andrea Paglietti
A rigorous macroscopic analysis of classical thermodynamics reveals an overlooked contradiction at the foundations of ideal gas theory: the traditional volume dependent formula for
entropy violates the fundamental axiom of thermal
equilibrium during reversible isothermal processes. This paper resolves this inconsistency by establishing a strict operational distinction between strictly isothermal and quasi isothermal processes. We show that in order for the perfect gas to be strictly consistent with the laws of classical thermodynamics, the thermodynamic
entropy of a perfect gas must be a function of temperature only. This independence of volume is also confirmed by a new analysis of the adiabatic free expansion of an ideal gas, which is found to be intrinsically reversible. To reconcile these results with standard thermodynamic practice, we introduce the concept of a pseudo isothermal process (a finite sequence of small adiabatic and isochoric steps) and show that the classical equations for work and heat apply rigorously to these real processes, as opposed to strictly isothermal ones. We conclude by discussing the profound implications of these findings for statistical mechanics, showing that while the corrected thermodynamic
entropy is in perfect agreement with Maxwell's kinetic theory, it is essentially different from the Planck Boltzmann formula. Thus, thermodynamic
entropy and statistical microstate counting are two different physical quantities, and the present analysis provides a simple way to free classical thermodynamics from the paradoxes of microscopic disorder.
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Fluid Dynamics
Kinetic Reduction and Hydrodynamics under Time-Dependent Helical Symmetry
Qingyun Zeng and Fan Feng
Translational and helical
symmetries provide idealized descriptions of many flows encountered in nature and engineering. While translationally symmetric flows have been extensively studied within kinetic and continuum frameworks, the corresponding connection between microscopic dynamics and continuum behaviour for helical flows is less developed. Here, we investigate this connection for a dilute gas with time-dependent helical
symmetry. Using the objective molecular dynamics framework, we derive a reduced Boltzmann equation that retains radial variations while preserving the classical collision operator under helical
symmetry. Formal hydrodynamic expansions then yield the corresponding Euler and Navier-Stokes-
Fourier equations. The resulting models describe how cylindrical geometry and axial deformation influence transport, including viscous transport and heat conduction, and how deformation generates unequal stresses along and across the axis in competition with collisional relaxation. Comparisons with direct simulation Monte Carlo solutions examine both spatially uniform and radially varying flows. In the cases studied, the first-order fluid corrections capture the leading stress anisotropy and improve the prediction of radial temperature variations, while larger Knudsen numbers expose limitations of the approximation.
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Atmospheric and Oceanic Physics · Cross-listed from cs.AI
SciExam for ENSO: Can AI Agents Build Climate Models?
Yinling Zhang, Langchen Liu, Dongbin Xiu et al. (+4 more)
Language-model
agents are increasingly asked to carry out open-ended scientific research, yet their results are usually graded against a known answer, a rubric, or a
language-model reviewer, none of which can tell whether a new scientific model is valid. The AI Science Exam for El Nino-Southern Oscillation (SciExam for ENSO) is a
benchmark in which
agents build low-order
stochastic models of ENSO, the dominant mode of interannual
climate variability, from real observations. Within a six-hour budget,
agents process the observations, write their own diagnostics, which are then frozen, and develop a model using only these diagnostics as feedback. Hidden graders then test whether the model reproduces ENSO's statistics, recovers unobserved variables, and forecasts held-out years, and score a published model in the same way. Across twelve
agent systems, six produce models that score higher than the published model, mainly through better reconstruction and forecasting. The simplified forms of the stronger models are each compatible with one of the two competing explanations of ENSO's warm-cold asymmetry, an open debate that the task never mentions. Controlled runs of the top system under varied information suggest that its scores do not come from recalling the dated observational record and that the information it receives shapes how it builds its model. SciExam for ENSO can thus evaluate
agent research where no answer is known, and the results suggest that
agents can already build competitive models whose structures bear on questions that scientists still debate.
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All authors Yinling Zhang, Langchen Liu, Dongbin Xiu, Xueyan Zou, Xu Kuang, Mengdi Wang, Shilong Liu
Data Analysis and Statistics · Cross-listed from econ.EM
From a Hierarchy of Stochastic Differential Equations to a Hierarchy of Generalized Beta Distributions
Siqi Shao and R. A. Serota
We introduce a mean-reverting
stochastic differential equation with a three-component
stochastic term and show that it generates a hierarchy of steady-state (stationary) distributions. At the top level, the hierarchy is described by a modified-Beta distribution, while one- and two-parameter reductions produce compact-support, power-law-tailed, and exponential-type limiting families within a single
stochastic framework. We then construct two generalized extensions of this hierarchy. In the first, the power transformation is applied directly at the level of the
stochastic differential equation; in the second, the same transformation is applied only after the stationary modified-Beta hierarchy has been obtained. While these two procedures agree on important lower branches and limiting cases they generally differ at the top level. The generalized hierarchy is therefore not unique: nonlinear transformation and stationary-state reduction do not commute. For both routes, we derive the probability density and cumulative distribution functions, express their parameters in terms of the underlying
stochastic dynamics, clarify the relations among their limiting cases, and compare the resulting families with the traditional Generalized Beta framework.
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Computational Physics
Self-Shielded Multigroup Microscopic Cross Section Generation Using Targeted Artificial Neural Networks
Joshua Nichols, Andrew Osborne, Ben Whewell et al. (+1 more)
Multigroup
neutron transport models are used widely in simulation codes due to their
memory and compute efficiency compared to continuous energy methods. However, multigroup codes rely on nuclear data prepared using approximate methods that require significant manual effort and expertise to account for self-shielding effects. In addition, multigroup nuclear data libraries prepared in this way can be limited in their applicability for modeling diverse reactors and operating conditions. Here we show that trained artificial neural networks can predict self-shielded microscopic cross sections accurately in pincell simulations containing uranium dioxide fuel with a fixed geometry and temperature. The model's predictive accuracy remains high across fuel enrichment and burnup ranges encountered in light water reactors. Critically, the neural networks' predictions require only the atomic concentration of the fuel's constituent nuclides as inputs. The networks are trained using 8,704 samples of training and validation data generated using continuous energy OpenMC pincell simulations. The trained neural networks predict self-shielded total, fission, absorption, elastic, and total scattering cross sections for combinations of 90 nuclides in the CASMO-8 energy group structure. The neural networks' predictions of self-shielded cross sections are validated by comparison with 1,500 samples of reference cross sections unseen during training and tallied using OpenMC simulations. The predicted and ground truth cross sections in the test data set agree to within 0.119% on average. When the predicted cross sections are used in a multigroup
eigenvalue run, they achieve a mean absolute error in keff of 200 pcm.
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All authors Joshua Nichols, Andrew Osborne, Ben Whewell, Nathan Gibson
Fluid Dynamics
Phase-controlled lateral forcing of droplets on a vibrating bath
Lawrence A. Wilen, Zahra Shamsi, Johnathan Hoggarth et al. (+1 more)
We introduce a method for controlling the lateral motion of droplets bouncing on a vibrated
liquid bath by superimposing a small periodic rocking motion onto the conventional vertical forcing. We show experimentally that the lateral velocity varies linearly with the rocking amplitude $\alpha$ and sinusoidally with its phase $\phi$ relative to the vertical forcing, while the response depends strongly on the droplet's vertical bouncing mode. A time-averaged model of a harmonic bouncer yields a time-averaged lateral force $\overline{F}_x=mg\alpha\sin(\Phi_b+\phi)$, where $\Phi_b$ is the droplet impact phase. Periodic rocking thus provides a simple, programmable means of exerting lateral forces on bouncing droplets, enabling controlled manipulation of passive and active droplets in pilot-wave hydrodynamics.
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All authors Lawrence A. Wilen, Zahra Shamsi, Johnathan Hoggarth, Bauyrzhan K. Primkulov
Fluid Dynamics
SAGE-PINN: A Singularity-free Axisymmetric Geometry-Encoded Physics-Informed Neural Network for Axisymmetric Multiphysics Flow in Cylindrical Coordinates
Raj Maurya, Joyprakash Akhuli and Doyel Pandey
Physics-informed neural networks (PINNs) provide a mesh-free framework for multiphysics modeling, yet fluid flow in axisymmetric cylindrical coordinates remains largely unexplored because the governing operators are singular at the axis and momentum and energy are strongly coupled through advection and buoyancy. We introduce SAGE-PINN, a Singularity-free Axisymmetric Geometry-Encoded Physics-Informed Neural Network for this class of problems. SAGE-PINN removes the coordinate singularity by construction: the parity-preserving variable $s=r^2$ and a modified stream-function representation eliminate explicit $1/r$ terms from the trained residuals and satisfy mass conservation identically. Boundary-fitted analytical ansätze enforce the boundary conditions exactly, while staged momentum-thermal-joint training, residual normalization, Péclet- and Richardson-number continuation, and a physics-scaled drift guard stabilize the coupled optimization. The framework is trained without labelled data, using only physics and boundary constraints, and its predictions are validated against finite-element reference solutions for steady nanofluid flow and heat transfer through a stenosed artery over a wide range of flow and thermal regimes, without case-specific tuning. Mass conservation remains at single-precision round-off, and over the central operating range the axial velocity and temperature are reproduced within $1.6\%$ and $9.1\%$ relative $L_2$ error, respectively. The main deterioration occurs only in the limiting regimes of strongly buoyancy-influenced slow flow and highly advection-dominated heat transfer.
Embedding cylindrical geometry, conservation structure, and boundary physics directly into the network provides a robust route to PINN simulation of axisymmetric multiphysics flows.
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Fluid Dynamics
Conditional Flow Matching for Generation of 3D Multi-variable Instantaneous Urban Microclimate Fields
Peng Liu, Shaoxiang Qin, Theodore Potsis et al. (+3 more)
Rapid and accurate prediction of urban wind and temperature fields is important for urban microclimate design and
climate adaptation. Large-eddy simulation (LES) effectively resolves these instantaneous fields, but its application is limited in iterative design of urban microclimate applications due to high computational cost. Existing regressive data-driven models offers quick outputs, but they produce only deterministic point predictions that inherently fail to represent turbulent stochasticity. This paper adopts a novel generative framework of Conditional Flow Matching (CFM) that uses building geometry and mean flow as guidance to generate plausible three-dimensional instantaneous velocity and temperature fields for urban microclimate in seconds. To overcome the GPU
memory bottleneck of pixel space 3D generation, the model operates in parallel on overlapping pixel space through a shared-noise initialization that preserves high spatial continuity of flow structure across the entire domain. Against reference LES data, the CFM surrogate can rapidly and accurately restore the first-order statistics with Normalized Root Mean Square Error (NRMSE) of 2.99% for wind and 1.77% for temperature, second-order
turbulence metrics with NRMSE of 7.17% for wind and 8.84% for temperature, turbulent kinetic energy with NRMSE of 7%, probability density function and vertical profiles in representative locations. Wind engineering application of local gust prediction demonstrate that the speed and accuracy of CFM, supporting the use of generative AI for making
turbulence-aware resilient urban design and
climate adaptation more computationally feasible.
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All authors Peng Liu, Shaoxiang Qin, Theodore Potsis, Lili Ji, Dingyang Geng, Liangzhu Leon Wang
Optics
Lattice tuning of optical and mechanical interactions in gold nanodisk metasurfaces
Juan I. Sangiorgio, Diya Xie, Emiliano Cortés et al. (+2 more)
The interaction between neighboring plasmonic nanoparticles can modify their intrinsic optical and mechanical resonances, enabling collective responses to be controlled through the geometry of their arrangement. Periodic arrays therefore provide a means of controlling both electromagnetic and mechanical interactions through a common geometrical parameter, the
lattice pitch. Joint control of these two channels is particularly useful for engineering how coherent mechanical excitations couple to the substrate and are optically read out at the nanoscale. Here, we investigate the optical and mechanical response of gold nanodisk metasurface patches on silica as a function of
lattice pitch using two-color pump-probe spectroscopy and numerical simulations. Optically, simulations show that varying the pitch tunes a Rayleigh anomaly across the broad localized surface plasmon resonance of the nanodisks, producing a strongly asymmetric collective optical response. Pump-probe measurements further reveal a pronounced wavelength dependence of the optical readout of coherent mechanical vibrations. Mechanically, we investigate how coupling between the nanodisks and the substrate depends on the character of the nanodisk resonances. The intrinsic nanodisk modes exhibit markedly different sensitivities to the periodic environment. One remains nearly independent of pitch, whereas another undergoes a pronounced frequency shift mediated by elastic interactions through the substrate. Numerical simulations further reveal that a third nanodisk resonance couples resonantly to a
lattice-selected acoustic mode, giving rise to an avoided crossing and mode hybridization. These distinct mechanical responses are governed by how efficiently each mode couples to near-surface elastic motion in the substrate. These results demonstrate that
lattice periodicity can simultaneously tune optical sensitivity and substrate-mediated mechanical coupling, providing a versatile platform for tailoring nanoscale optomechanical responses in the GHz regime.
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All authors Juan I. Sangiorgio, Diya Xie, Emiliano Cortés, Gustavo Grinblat, Andrea V. Bragas
Fluid Dynamics · Cross-listed from math.NA
A hat function based positive contact discontinuity capturing Boltzmann scheme
Balwinder Singh and S. V. Raghurama Rao
The suitable moments of the Maxwell velocity distribution function recover the conserved variable and flux vectors of the Euler equations of gas dynamics. However, the Maxwellian has infinite support in velocity space, which poses challenges for the numerical analysis of kinetic schemes based on it. Although Maxwellian is a natural choice, it is not the only distribution function that preserves these key moments. In his seminal paper, Perthame (1990) introduced a compactly supported hat function as an alternative. This paper presents the formulation and analysis of a contact discontinuity capturing Boltzmann scheme based on peculiar velocity, constructed using the hat function. Owing to the compact support of the hat function in velocity space, the numerical analysis of the proposed scheme is considerably simplified, allowing for a direct proof of its positivity. The scheme, extended to second-order accuracy without compromising the positivity-preserving property of its first-order counterpart, is tested on several
benchmark inviscid flow problems in one- and two-dimensions.
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Optics
Lithography-free in situ photonic inverse design
Yiqi Zhao, Martin M. Stein, Fan O. Wu et al. (+6 more)
Inverse design has emerged as a powerful engineering approach in nanophotonics, leading to devices with unintuitive designs that extend beyond human intuition. However, inverse-designed devices have been limited by costly and time-consuming fabrication iterations, simulation-reality gaps, and the fixed nature of etched devices. Separately, programmable photonics has emerged as an approach to modify the function of photonic devices after fabrication; recent free-form programmable-photonic devices have been scaled to have many degrees of freedom, in principle providing platforms well-suited for programmably realizing inverse design of conventional linear-photonic functions. Here we demonstrate in situ training of a single free-form programmable-photonic device to achieve a variety of functions that each would have been implemented in separate devices in the conventional inverse-design paradigm. Our platform is a thin-film lithium niobate slab waveguide with full programmability over the refractive index of the two-dimensional slab, with a tuning range of up to 1.7e-3 for each of the 10^4 effective pixels. We demonstrate 4 x 4 Gaussian-mode permutations, 4 x 4 Hermite-Gaussian mode demultiplexing, and 8 x 8 Gaussian-mode permutations, with maximum crosstalk of -27dB, -13dB, and -13dB, as well as 4 x 4
matrix-vector multiplication. We further demonstrate engineering of spectral responses, showcasing both wavelength-independent operation throughout the 1500-1600 nm window and wavelength demultiplexing between 1310 nm and 1550 nm, with maximum crosstalk of -8.4dB. Since the device is reconfigured for each function after fabrication, the investment in device fabrication, characterization, and calibration can be amortized across the various photonic functions the device is ultimately used for.
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All authors Yiqi Zhao, Martin M. Stein, Fan O. Wu, Ryotatsu Yanagimoto, Benjamin A. Ash, Mandar M. Sohoni, Logan G. Wright, Tatsuhiro Onodera, Peter L. McMahon
Applied Physics · Cross-listed from cond-mat.mes-hall
Pseudospin Hall Transport Induced by Berry Curvature
Qinhui Jiang, Jidong Song, Qingyang Mo et al. (+4 more)
Pseudospin-1 Dirac systems exhibit unique physics distinct from conventional Dirac cones, such as flat-band crossings and non-Abelian characteristics, yet their topological transport properties have remained largely untapped in passive, time-reversal-invariant settings. Here we uncover an in-plane polarity of the Berry-
curvature texture in a pseudospin-1 Dirac
Hamiltonian, a previously unexplored
geometric degree of freedom encoded in the sign-resolved distribution of Berry
curvature despite zero net Berry flux, and reveal a new mechanics where Berry
curvature induce pseudospin Hall behaviors in a system. We show that the oriented coupling between this momentum-space polarity and a real-space mass gradient governs a
geometric selection rule that dictates the emergence of gapless pseudospin Hall modes. By engineering the intracell couplings of a four-site planar
lattice, we independently program the Berry-
curvature polarity and the spatial mass gradient without altering the host
lattice symmetry. Acoustic experiments directly confirm this directional selection rule: reversing the mass gradient closes or reopens the dispersive gap, while pseudospin-selective source excitation launches counterpropagating pseudospin branches along arbitrary prescribed axes. Our work establishes
quantum geometric polarity as a versatile tool for reconfigurable wave routing, sensing, and high-capacity
quantum applications.
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All authors Qinhui Jiang, Jidong Song, Qingyang Mo, Bo Li, Dongyi Wang, Shuang Zhang, Mengyao Li
Chemical Physics
General Time-Dependent Surface Flux for Multiple Ionization and Fragmentation of Molecules. I: General Theory
Stefanos Carlström and Serguei Patchkovskii
We introduce a method for efficiently computing multiple photofragmentation of atoms and molecules into photoelectrons and nuclear fragments. The method is a generalization of the time-dependent surface flux originally introduced for single and double photoionization.
Atomic Physics · Cross-listed from quant-ph
Quantum simulation of the Heisenberg XXZ model on a Rydberg atom array
Matthias Werner and Artur García-Sáez
A major application of analog
quantum hardware is
quantum simulation, where a system evolves over time according to a given
Hamiltonian. One challenge when implementing the
Hamiltonian on a programmable
quantum simulator is the availability of distinct coupling types, many of which go beyond the conventional ZZ-couplings found in Ising
Hamiltonians that are naturally implemented by various platforms. In prior work, we have developed a method based on domain wall encoding to simulate one-dimensional
spin chains using only Ising
Hamiltonians, which would otherwise require XX+YY-couplings as well. Here, we implement the method on a commercially accessible Rydberg atom
quantum device, and simulate the time
evolution of the Heisenberg XXZ model with various anisotropies. We successfully probe the XY and Ising phases of the model and reproduce the qualitative behavior of several one- and two-body observables in both phases, as well as at the critical point. Our work augments the toolbox to simulate canonical
quantum many-body systems on analog
quantum simulators.
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Instrumentation and Detectors
Neural network-based timewalk correction for the Timepix4 ASIC
David Bacher, Kazu Akiba, Martin van Beuzekom et al. (+7 more)
Timewalk corrections of hybrid pixel detectors conventionally employ heuristic fits performed independently in bins of the relevant input variables. This requires both an explicit functional ansatz and binning scheme, as well as densely populated calibration data whose required size grows rapidly as further input variables are added. This paper presents a timewalk correction for the Timepix4 readout chip based on a compact feed-forward neural network (NN), which learns the dependence directly from the input variables without requiring an explicit functional ansatz or binning. The NN is compared against the fit-based baseline using data from the Timepix4 beam
telescope at the CERN SPS in 2025, with $100\,\mathrm{\mu m}$ and $300\,\mathrm{\mu m}$ thick planar sensors. Both methods parameterise the timewalk correction as a function of the measured charge and reconstructed intrapixel impact position. With only 20 % of the calibration data, the NN already achieves the same asymptotic time resolutions as the fit-based baseline: $180\,\mathrm{ps}$ and $300\,\mathrm{ps}$ for the two thicknesses, respectively.
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All authors David Bacher, Kazu Akiba, Martin van Beuzekom, Victor Coco, Raphael Dumps, Tim Evans, Kevin Heijhoff, Malcolm John, Edgar Lemos Cid, Tommaso Pajero
Chemical Physics
LLM-Assisted Generation of Transparent, Open-Source Multiphysics Models of Electrochemical Devices
Sebastian Castro, Maya F. Schuchert, Spencer A. McCluskey et al. (+2 more)
Multiphysics continuum models are powerful tools for studying electrochemical devices, enabling in silico reactor design and resolution of local pH, potential, and concentration fields that govern device performance but are difficult to measure experimentally. However, constructing such models requires substantial numerical expertise or reliance on proprietary software. Here, we show that frontier large
language model
agents can remove this implementation burden while keeping the underlying physics under researcher control. Using one-dimensional electrochemical CO2 reduction to CO in a porous gas
diffusion electrode as a test case, we develop a machine-readable, human-specified modeling harness containing governing equations, parameters, numerical methods, logical build stages, and human-verifiable checkpoints. From this specification, the
agent reproducibly constructs complete multiphysics models in open-source Julia. Independently built models, including fully autonomous
agent-built models, agree with an equivalent COMSOL implementation to within 0.7% of the peak CO partial current density, and with one another to within 0.04%. Systematically planted errors demonstrate the importance of explicit specifications for reproducibility and reveal the
agent's capabilities and limitations in debugging model physics. This framework establishes a more transparent approach to multiphysics modeling in which physical descriptions and governing equations, rather than specialized code, become the primary inputs for computational model development.
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All authors Sebastian Castro, Maya F. Schuchert, Spencer A. McCluskey, Eric W. Lees, Justin C. Bui
Fluid Dynamics · Cross-listed from cs.LG
PoreML: A Data-Driven Framework for Learning Multiphase Flow in Porous Media
Chunyang Wang, Mingrui Zhang, Yuyan Zhang et al. (+5 more)
Multiphase flow in porous microstructures is central to CO$_2$ storage, fuel-
cell operation, and flip-chip packaging. Predicting these flows remains challenging because wettability and complex pore geometry govern the nonlinear
evolution of fluid interfaces. Machine learning holds substantial promise for advancing the field, but progress is constrained by scarce time-resolved 3D
datasets and a lack of a unified workflow for training and evaluating models. To fill this critical gap, we introduce PoreML, an open-source framework unifying data generation, model training, and evaluation grounded in pore-scale physics. The framework comprises three core components. (a) A modern GPU-native
lattice Boltzmann solver, validated against analytical solutions and published experiments, enables reproducible data generation. (b) A 3.3 TB
dataset contains 560 simulation runs and 158,546 stored time steps across four application-driven scenarios. These trajectories span synthetic structures and geometries derived from micro-CT scans of real materials, covering diverse wetting conditions and viscosity ratios. (c) A unified learning framework evaluates one-step prediction and autoregressive rollouts. Its domain-specific evaluation protocols assess predictive accuracy and physical consistency. We evaluate five models of diverse architecture under these protocols. Two complementary challenges assess transfer to larger domains and from synthetic to micro-CT-derived structures. PoreML provides a shared foundation for machine-learning research on multiphase flow in porous media, with the aim of empowering the community to develop reliable predictive models and advance the field.
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All authors Chunyang Wang, Mingrui Zhang, Yuyan Zhang, Linqi Zhu, Xin Ju, Edo Sicco Boek, Martin J. Blunt, Gege Wen
Optics
Visibility function and Stokes parameter analysis for non-ideal quarter-wave plates in a polarization contrast scheme
Stepan Kupavskii, Roman Malikov, Sergey Dyakov et al. (+6 more)
The match of the fourth power of the normalized third Stokes parameter for a single non-ideal quarter-wave plate (QWP) and the visibility function of the polarization contrast measured using a polarimetric setup with parallel polarizers and two identical QWPs was examined and observed. The behavior of the Stokes parameters of a single QWP and the visibility function of the polarization contrast scheme were investigated theoretically using the
Fourier-modal method (FMM) and described within the Jones
matrix formalism. This analysis was then generalized to a realistic case, accounting for the transmission anisotropy, phase deviation, and misalignment of optical components from their ideal orientations. An analytical expression for the visibility function of the polarization contrast scheme in terms of the normalized Stokes parameters was derived, along with the asymptotic expansions in terms of small parameters representing the phase deviation, transmission anisotropy, and angular misalignment of the polarization contrast scheme. For the investigated silicon-grating-based QWP, good quantitative agreement between the FMM-calculated fourth power of the normalized third Stokes parameter, and the visibility function (FMM-calculated and experimentally measured) was demonstrated over almost the entire investigated frequency range of 200-700 GHz.
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All authors Stepan Kupavskii, Roman Malikov, Sergey Dyakov, Sergei Tikhodeev, Arseniy Gavrilov, Kirill Dzhikirba, Sofya Andreeva, Igor Kukushkin, Nikolay Gippius
Fluid Dynamics
Steady translation of a chemically active viscous drop along a rigid wall
Nikhil Desai and Sébastien Michelin
Chemically active drops release a chemical solute, gradients in whose concentration cause interfacial flows that advect the emitted solute. This non-linear coupling between solute distribution and fluid flow causes the drop to swim via an advective instability, if the system's Peclet number Pe–ratio of advective-to-diffusive solute transport–is larger than a finite threshold. Theoretical analyses often model active drops as diffusiophoresis-driven active particles, primarily to simplify the implementation of boundary conditions at the drop surface. However, physical insights about drop motion that rely on this simplification risk having limited applicability to fluid drops, especially when the drop is interacting chemo-hydrodynamically with its environment. Towards this, we study here the translation of a chemically active viscous drop parallel to a rigid wall, as an example of a common experimental configuration. We consider a propulsion mechanism where the drop swims due to interfacial Marangoni stresses and diffusiophoretic flows, and compare our results to those of the ”isotropic active particle” model, to examine the validity of the latter. Our analysis shows that, for the same confining force, a less viscous drop swims closer to the wall than a more viscous drop. This results in faster swimming, due to stronger concentration gradients driving the drop's motion. It is also shown that for moderate Pe, the swimming speed of the drop depends weakly on its mobility ratio (relative strength of diffusiophoretic and Marangoni flows). This weak dependence provides a quantitative justification for studying diffusiophoresis-driven active particles to infer the motion of Marangoni-stress-driven active drops.
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Chemical Physics · Cross-listed from cs.LG
Physics-Aligned Electronic Ground-State Learning Improves Generalization
Eike S. Eberhard, Xaver Kainz, Viktor Kotsev et al. (+2 more)
Machine-learned interatomic potentials (MLIPs) excel at in-distribution tasks, accelerating drug and material development, yet they struggle to generalize out-of-distribution. We propose to push the cost-accuracy Pareto frontier by designing observable-agnostic electronic ground-state descriptor models (GSMs) with computational costs situated between MLIPs and Kohn-Sham density functional theory (KS-DFT). We align the learning objectives and architectures of GSMs with the governing equations of KS-DFT by enforcing physical constraints and removing optimization pressure on unphysical or irrelevant degrees of freedom. In our size-extrapolation experiments from QM9 to QM40, our combined contributions OrthoNormal-Loss (ON-Loss) and Grassmann Restricted Occupied-Orbital Training (GROOT) reach a 79.1% energy and 83.4% force mean absolute error (MAE) reduction over previous state-of-the-art density GSMs. For
Hamiltonian GSMs, ON-Loss and Residual Optimal-gauge Conditioning-aware KS-Eq. Training (ROCKET) together reduce the energy and force MAEs of the strongest baseline by 99.8% and 95.9%, respectively. Using a self-consistency rejection criterion, we filter out extrapolation errors on QMugs, rejecting fewer than 0.4% of predictions while reaching an energy MAE of 0.07 mHa. Finally, we demonstrate the efficiency of label-free self-consistency fine-tuning, and transfer GSMs to reactive chemistry in Transition1x, reaching energy errors below chemical accuracy.
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All authors Eike S. Eberhard, Xaver Kainz, Viktor Kotsev, Abdulrahman Aldossary, Stephan Günnemann