Fluid Dynamics
Early-time dynamics of the Richtmyer-Meshkov instability at Kelvin-Voigt viscoelastic interfaces
Xuning Zhao and Mauro Rodriguez Jr
The Richtmyer-Meshkov instability (RMI) is initiated when a shock wave interacts with a perturbed interface between two materials, imparting an initial velocity to the interfacial perturbation. When the materials are viscoelastic, the subsequent
evolution is influenced by the coupled effects of inertia, viscous dissipation, and elastic resistance. The interplay of these effects remains poorly understood and is important for predicting the dynamic response of shock-loaded soft materials, e.g., in blast-induced injury. Using the incompressible Cauchy momentum equation with a Kelvin-Voigt constitutive model, we conduct a linear stability analysis for the early-time post-shock
evolution of a single-mode interface perturbation of two viscoelastic materials. We derive an implicit dispersion relation for the temporal growth rates of the perturbation. This relation depends on density, viscosity, and elasticity contrasts between the two materials and an viscoelastic parameter which is a bounded ratio of the viscous dissipation and elastic resistance response rates. Four material cases, spanning symmetric to highly contrasted interfaces, are examined to show how the viscoelastic number affects the growth rate structure as complex-dominated (oscillatory) or real-dominated (monotonic relaxation). The critical viscoelastic number separates these two regimes and is primarily governed by the density and viscosity contrasts. These results provide a theoretical criterion for predicting the early-time response of shock-driven interfaces involving fluids and viscoelastic solids.
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Optics
Coherent ultraviolet frequency combs generated by resonant dispersive waves
Michael Hemsworth, Arthur K. Mills, Christina Hofer et al. (+2 more)
Resonant dispersive wave (RDW) emission in gas-filled anti-resonant hollow-core fibers (AR-HCF) offers a wavelength-tunable route to ultraviolet (UV) and
vacuum ultraviolet (VUV) frequency comb generation. RDW emission is expected to be coherent fundamentally, but various noise sources are expected to limit the coherence, including amplitude-to-phase fluctuations due to input pulse intensity noise and
plasma formation. It is critical, therefore, to establish the limits of the coherence of UV RDW pulses to determine their applicability for precision spectroscopy. Here, we demonstrate the coherence of RDW emission at 260~nm generated with an amplified Yb-doped fiber system at a 2-MHz repetition rate with a self-heterodyne beat measurement between the RDW and the fourth harmonic of the source. We isolate the RDW contribution to the frequency noise by coherently subtracting the carrier-envelope offset noise contribution measured simultaneously with an f-2f interferometer. We observe a beatnote linewidth below 10~kHz over timescales longer than 100~ms, and we find a conservative upper bound of 1~rad on the integrated RMS phase deviations over a frequency range spanning 1 to 30~kHz. The dominant frequency noise contributions are consistent with environmental noise of the interferometers and AR-HCF coupling fluctuations. These results provide strong evidence that RDW emission is coherent under well-controlled experimental conditions, confirming its potential for UV and VUV frequency comb generation.
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All authors Michael Hemsworth, Arthur K. Mills, Christina Hofer, TJ Hammond, David J. Jones
Physics and Society
Learning Social Influence with Unobserved Attention
William Thompson, Moyi Tian, Linh Huynh et al. (+4 more)
Social influence, the tendency of our social environment to sway our beliefs and behaviors, is often cited as a driver of population-level trends in opinions or beliefs. Many quantitative theories of social influence assume that an
agent interacts with its entire social environment. This assumption is often unrealistic. For example, users of online platforms typically see posts from only a small subset of the accounts they follow at any given time. We introduce a simple probabilistic
agent-based model of social influence that explicitly models unobserved
attention mechanisms. The first of these latent mechanisms is selection: each
agent interacts with only one other
agent at a time from its social environment. The second is retention: the
agent chooses whether or not to ignore the opinion of the other. Our model admits an efficient maximum-likelihood
algorithm which allows us to fit it to opinion traces even when these mechanisms are unobserved. On synthetic data, omitting either latent mechanism reduces the overall strength of social influence governed by the inferred interaction kernel, which hides the evidence for the underlying nonlinearity of the social influence. We study a selection of previously analyzed empirical
datasets and curate several new ones. We show that models that include the latent mechanisms are often more predictive on these
datasets; indeed, they typically improve prediction far more than the choice of interaction kernel does. Modeling latent
attention increases held-out predictive likelihood on 14 of these 15
datasets. Each mechanism changes the size of the inferred kernel in a predictable direction, while its effect on the kernel's shape depends on the data.
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All authors William Thompson, Moyi Tian, Linh Huynh, Alexander Wiedemann, Vicki Modisette, Philip S. Chodrow, Heather Z. Brooks
Physics Education
Measuring the Speed of Light with a Time-of-Flight Setup: An Authentic Experiment for High School
Alberto Mazzi
Students often perceive scientific experiments as clean, straightforward procedures with predictable outcomes. Students' beliefs about the nature of experimental physics typically remain inconsistent through the initial stages of undergraduate education. In reality, experimental work in physics involves hands-on activities such as soldering
circuits, assembling provisional setups, and building functional mock-ups from simple materials before reliable measurements can be achieved. To address this gap, we designed a laboratory activity in which students were completely involved in the experiment design, construction and operation with the final goal of measuring the speed of light with a low-cost and compact setup. The educational objective of this activity was double: firstly, to make students aware of the importance of experimental choices and constraints in the setup design and secondly, to discover the potentiality of
semiconductor technologies. As far back as the 1990s, with the spread of compact disk players,
laser diodes and photodiodes capable of fast timing performance in the order of nanoseconds became largely available. In the last decades, consumer electronic devices made high performance technologies ubiquitous. Nevertheless, the idea of measuring ultrashort time intervals and the relevance of such measurements in science and technology remain unknown to most students. Nowadays, digital instruments with significant performance such as waveform generators and oscilloscopes are affordable for high school physics labs, paving the way to a closer contact with the world of experimental physics.
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Chemical Physics · Cross-listed from math.NA
Fourier Transform-Based Computation of Two-Electron Four-Center Coulomb Repulsion Integrals
William Chen, Lior Silberman, Ury Segal et al. (+1 more)
We present a
Fourier-based method for evaluating two-electron four-center Coulomb repulsion integrals over Slater-type orbitals. Spherical
Fourier-Bessel coefficients of the convolution factors are precomputed and reused. Since the spherical part of the integral can be evaluated directly, there remains only a one-dimensional quadrature problem in the radial direction. For the ten reference integrals reported here, the largest absolute discrepancy is $1.7\times10^{-9}$. A preliminary resident-cache
benchmark computes approximately $4.2\times10^5$ unique integrals per second on an NVIDIA V100 at the contraction stage.
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All authors William Chen, Lior Silberman, Ury Segal, Chen Greif
Optics
Broadband soliton microcombs in the biological window enabled by a resonant coupler
Xinrui Luo, Du Qian, Yuanlei Wang et al. (+8 more)
Broadband
soliton microcombs near 1 $\mu$m offer coherent, evenly spaced optical lines for measurements in the biological window. However, combining broad spectral coverage with closely spaced comb lines increases the pump-power requirement and makes thermally stable
soliton access more difficult. Here, we demonstrate broadband silicon nitride (Si3N4)
soliton microcombs enabled by a resonant coupler that enhances pump delivery to the nonlinear resonator. The device generates a spectrum spanning 930-1380 nm with an approximately 98-GHz comb-line spacing at on-chip pump powers of tens of milliwatts, while providing reliable thermally stable access to the single-
soliton state. These results provide a route to compact, power-efficient microcomb sources that combine broad spectral coverage with closely spaced comb lines for spectroscopy and imaging in the biological window.
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All authors Xinrui Luo, Du Qian, Yuanlei Wang, Yiwen Yang, Kaixuan Zhu, Yinke Cheng, Haoyang Luo, Junqi Wang, Bei-Bei Li, Qihuang Gong, Qi-Fan Yang
Computational Physics
Parallel Design of a GPU-Resident Semi-Lagrangian Drift-Kinetic Solver on Unstructured Extruded Tokamak Meshes
Mark F. Adams
Semi-Lagrangian (SL) schemes advect a distribution function along characteristics and remap it to a fixed grid. They are unconditionally stable without a global solve, and with discontinuous Galerkin (DG) integration the transfer along a
magnetic field line conserves mass exactly. This paper presents the parallel and GPU design of a 3X+2V SLDG drift-kinetic tokamak solver on unstructured, flux-surface-aligned poloidal planes of shaped cross section, extruded toroidally. Transport follows
magnetic field lines across the planes, and each velocity channel advances a fixed distance per step, proportional to its parallel speed, so the
halo depth a channel needs grows with its speed. Velocity-dependent ragged
halos exploit this, sending each channel only its own upwind depth. The 4D decomposition includes the toroidal angle, the 2D poloidal plane and the
magnetic moment, and leaves the parallel velocity whole: no stage transports across the
magnetic moment, while a parallel-velocity partition would put a
halo inside the acceleration stage. On NVIDIA A100s the design strong-scales over the
magnetic-moment axis from one to 12 four-GPU nodes, and a weak series of three points refining all five phase-space dimensions together, 16x in total per series point, holds a weak-scaling efficiency of 75% on 16 GPUs and 57% on 256, from a one-GPU base case. The pointwise interpolations within the poloidal plane do not conserve mass, and the full step's mass drift stays below 2.1x10^-5 at every series point. The design was implemented with an AI code assistant under a machine-checked regression discipline, in which every change is held to one of four exactness classes, from bit-identical to a physics rate within a stated tolerance.
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Atomic Physics · Cross-listed from quant-ph
Correlated Pair Creation with Positive- and Negative-Frequency Oscillators in a Trapped-Ion Quantum Simulator
Visal So, Mingjian Zhu, Han Pu et al. (+1 more)
Spin-dependent forces enable independent control over the effective frequencies of bosonic motional modes and their coupling strengths to
spins in trapped-ion
spin-
boson quantum simulators. We show that the relative signs of these effective frequencies provide an important programmable resource for engineering distinct multimode dynamics. Focusing on a two-mode
quantum Rabi model, changing the frequencies of the two modes from the same sign to opposite signs reorganizes the resonance structure of the
spin-mediated bosonic interaction, converting the dominant resonant process from excitation exchange to correlated pair creation. In the dispersive regime, the resulting effective interaction realizes a two-mode parametric amplifier with stable oscillatory, critical, and exponentially amplifying regimes separated by a
spin-dependent instability threshold. Across these regimes, the dynamics generate collective-quadrature squeezing and bipartite motional
entanglement. Preparing the
spin in a coherent superposition further allows different
spin components to experience distinct stability regimes, generating tripartite
spin-
boson entanglement, while
spin postselection can coherently combine the conditional bosonic branches to generate non-Gaussian entangled bosonic states. Finally, we outline an implementation using parameters within reach of current trapped-ion platforms.
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All authors Visal So, Mingjian Zhu, Han Pu, Guido Pagano
Physics and Society · Cross-listed from cs.SI
Separating Field–Camp Alignment from Structural Balance in a Frustrated Random-Field Ising Model of Social Networks
Natalia Romero
Frustrated Ising models of social networks mix ferromagnetic (agreement-seeking) and antiferromagnetic (antagonistic) couplings, and the antagonistic bonds are almost always placed independently at a fixed rate, yielding a +/-J
spin glass. Real signed networks (Epinions, Slashdot, Wikipedia) instead show an excess of balanced triads, and perfectly balanced placement is gauge-equivalent to an unfrustrated ferromagnet. We compare three sign-placement conditions on a frustrated random-field Ising model of a polarised population, sharing an identical graph, field assignment, and edge-weight magnitudes: an independent baseline; a balance-structured condition whose camp membership coincides with each voter's field sign; and the same condition with camp labels randomly permuted, so triadic balance is identical by construction while the camp-field correlation is removed. On a confound-controlled Kolmogorov-Smirnov test of the sampled energy-gap distribution (simulated annealing), the aligned condition differs from the baseline at every density tested (p = 7.3e-8), while the permuted condition shows no significant difference (paired mean difference 0.003, 95% CI [-0.041, 0.046]). Field-camp
alignment, not triadic balance alone, drives the effect. The qualitative direction follows from a known mapping; the contribution is a quantitative, confound-controlled measurement of the effect's size away from that limit, robust to parallel-tempering sampling, system sizes up to N = 300, and structural noise calibrated to real networks, while triadic balance alone produces no detectable effect at N = 90 and at most a small one at N = 180-300 (paired change in KS distance about 0.04, 95% CI upper bound below 0.09).
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Computational Physics · Cross-listed from cond-mat.supr-con
Enhanced Coulomb pseudopotentials in high-pressure hydrides from first principles
Kieran Bozier and Chris J. Pickard
In the Eliashberg theory of superconductivity, Coulomb interactions oppose the
phonon-mediated attraction and suppress the superconducting critical temperature $T_{\mathrm{c}}$. However, most first-principles predictions continue to treat this repulsion with an empirical Coulomb pseudopotential $\mu^*_{\text{ME}}$ in the range 0.1-0.15. Here we present first-principles calculations of the Coulomb parameter $\mu$ and pseudopotential $\mu^*_{\text{ME}}$ in superconducting hydrides across a range of pressures. The screened Coulomb interaction is evaluated at the random phase approximation level with the Mantel code. We demonstrate that several hydride systems, including H$_3$S, YSbH$_6$ and Mg$_2$IrH$_6$, have strong Coulomb repulsions driven by a large density of states at the Fermi energy, $N_{\mathrm{F}}$. For Mg$_2$IrH$_6$ we obtain $\mu=0.62$ and $\mu^*_{\text{ME}}\approx0.39$, leading to a 40 K suppression in $T_{\mathrm{c}}$ relative to a standard $\mu^*_{\text{ME}}=0.16$. Our obtained $\mu^*_{\text{ME}}$ is far above the conventional range and, accounting for the energy dependence of the density of states, resolves the factor-of-two discrepancy between $T_{\mathrm{c}}$ estimates of previous studies. Across all systems and pressures studied, we find that $\mu$ closely tracks $N_{\mathrm{F}}$, suggesting that commonly used $\mu^*_{\text{ME}}$ values lead to overestimated $T_{\mathrm{c}}$ in materials where the density of states at the Fermi energy is large.
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General Physics
Binary Classifier Wire-Resistance Attack on KLJN: Bayes-Optimal Limit at a Narrow Resistor Gap
Mehmet Yildirim, Fahrettin Ay and Laszlo B. Kish
Machine learning is shown not to increase Eve's successful bit-guessing probability in the binary-classifier-based wire-resistance attack on the Kirchhoff Law-Johnson Noise (KLJN) key exchange when the resistor gap is narrow. Using time-domain simulations of a non-ideal KLJN loop with $R_L = 9$ k$\Omega$, $R_H = 10$ k$\Omega$ and wire resistances from 0 to 3200 $\Omega$, we generate 3000 secure bits per wire resistance and evaluate the mean-square noise voltages at both ends over 100-sample bit exchange periods (BEPs). This probability $p$ is evaluated for the sign rule, the nearest-line rule, logistic regression, support vector machine (SVM), $k$-nearest neighbors, random forest and gradient boosting. The sign rule gives $p = 0.711$ at 50 $\Omega$ and 0.682 at 3200 $\Omega$. The nearest-line rule, logistic regression and SVM stay close to it and the other classifiers perform worse. For Gaussian noise, the likelihood ratio between the HL and LH cases depends only on the difference of the two mean-square voltages, so the sign rule is Bayes-optimal. The resulting $p_{\max}$ reproduces the measured values and predicts that about 900 to 1200 samples per bit are needed for $p = 0.95$. This difference carries about 0.19 bit per secure bit, 1.5 times the hard-decision value.
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Chemical Physics
The Reference Set Is Not the Answer Set: GEOM-DRUGS Is Missing Low-Energy Conformers on Its Own Energy Surface
Alvand Vahediahmar, Aaron Zweig, Mingxuan Zhang et al. (+1 more)
Machine-learned conformer generators are scored on GEOM-DRUGS by proximity to a reference ensemble that standard
benchmark metrics implicitly treat as ground truth. We test whether that finite set is complete on the GFN2-xTB surface used to construct it. Across five public generators, settled, low-energy structures remain outside the $0.75$\,Å reference radius after removing reflections, incorrect stereochemistry, and post-relaxation connectivity changes ($2.3$–$7.0\%$ of submitted conformers, depending on architecture). In a targeted DFT audit, $107$ of $113$ chemically screened novel/reference pairs remain outside that radius after full $\omega$B97X-D3BJ re-optimization. A threshold-free complementary test needs no radius and no energy window at all: on $15.0\%$ $[8.0, 22.0]$ of molecules some generator has already found a chemically intact structure lying below the entire released ensemble. A complementary CREST search reproduces the minimum released in $65$ of $69$ molecules, and in $11$ of them it reaches more than $0.5$\,kcal/mol below the complete released ensemble. Augmenting the references with conformers proposed by held-out architectures raises absolute coverage-precision for every generator tested, by $1.48$–$2.99$ points. Together, these results show that GEOM-DRUGS precision is sensitive not only to generator quality but also to incomplete reference support.
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All authors Alvand Vahediahmar, Aaron Zweig, Mingxuan Zhang, Elham Azizi
Chemical Physics
MolP2SBench: Do LLMs Really Reason from Molecular Properties to Structure?
Yuhang Xie, Zhiyi Shi, Zeyi Zhang et al. (+4 more)
Large
language models (LLMs) are increasingly used in molecular discovery and structure elucidation, where decisions depend on physical observations. Existing evaluations primarily focus on end-to-end molecular reconstruction, where correct predictions do not necessarily indicate reliance on the supplied properties. We introduce MolP2SBench, a
benchmark for molecular property-to-structure (P2S)
reasoning in LLMs. Each task presents a masked molecular scaffold with given properties and asks the model to select the missing structure from plausible candidates. We shuffle or replace properties to test reliance on correct property-structure pairing and responsiveness to counterfactual property changes. A channel-wise leaderboard uses Property-Grounded Recovery (PGR), giving equal weight to source and counterfactual recovery. Evaluating seven models, we find that LLMs can use physical properties for structure prediction, but performance varies substantially across property types. Higher accuracy with a property does not necessarily imply greater responsiveness to it: a model may benefit more from $^1$H NMR than from $^{13}$C NMR, yet be less likely to change its prediction when the $^1$H data is replaced. LLM performance also need not reflect the relative difficulty of these properties for human chemists. Although UV-Vis spectra provide less direct structural information than NMR spectra, Astra achieves higher accuracy with UV-Vis data than with NMR peaks. MolP2SBench supports studying how LLMs extract and use physical-property information, revealing patterns that may differ from conventional chemical intuition and providing a foundation for more reliably property-grounded models for molecular and
quantum-chemical inference. Code and
dataset: https://github.com/DUAL-Xiao/MolP2SBench.
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All authors Yuhang Xie, Zhiyi Shi, Zeyi Zhang, Zhisheng Qi, Yushun Dong, Yu Wang, Xiongye Xiao
Computational Physics · Cross-listed from cs.LG
Bi-FORK: Generative Modeling of High-Dimensional Bifurcating Systems
Anna Zimmel, Fleur Hendriks, Markus Holzleitner et al. (+4 more)
Bifurcations are ubiquitous in physical systems, from structural buckling to fluid and
climate dynamics, yet they remain largely unexplored in deep learning. At a
symmetry-breaking bifurcation, a single input admits multiple equally valid solutions, violating the one-to-one assumption underlying most learned physical surrogates. We introduce Bi-FORK, a generative framework for learning these one-to-many solution maps in high-dimensional systems. Bi-FORK generates complete trajectories through latent flow matching, preserving space and time coherence, and uses repulsion-guided sampling to recover distinct solution branches in a single amortized pass. We evaluate Bi-FORK on buckling beams, mechanical metamaterials, and Allen-Cahn phase separation, spanning continuous, discrete, and field-valued bifurcations with discretizations up to 260,000 points. Bi-FORK recovers the
multimodal solution structure while scaling several orders of magnitude beyond prior approaches, opening generative modeling to high-dimensional bifurcating physical systems.
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All authors Anna Zimmel, Fleur Hendriks, Markus Holzleitner, Florian Sestak, Martin Weichselbaumer, Vlado Menkovski, Johannes Brandstetter
Biological Physics · Cross-listed from cs.AI
Ecology of AI Agents: Collaboration Creates a Population Threshold for Takeoff
Erin Crawley and Hidenori Tanaka
AI
agents can now conduct real-world cyberattacks, scale up capabilities with the number of
agents, and collectively pursue misaligned goals to obtain rewards. Together, these factors raise the risk of a population explosion of misaligned
agents:
agents could compromise computers and secretly deploy additional
agents, creating a self-reinforcing cycle where larger populations develop greater collective cyber capability and expand further. This raises a fundamental question: What determines whether a population of misaligned
agents remains contained or takes off into this self-reinforcing cycle? This population-level problem is ecological safety: unlike individual-
agent or multi-
agent safety with a fixed population, it concerns the dynamics of the population itself. Here, we develop an ecological theory of AI-
agent populations based on a population growth equation in which fitness (growth rate) depends on cybersecurity capability. We show that, without collaboration, the population takes off only when individual-
agent capability exceeds a critical threshold. With collaboration, however, collective cybersecurity capability increases with population size. This creates a critical population threshold: below it, the population declines; above it, the population takes off, even though individual-
agent capability has not changed. In ecology, this phenomenon is known as the strong Allee effect. Because red teaming a small group of
agents cannot guarantee ecological safety in larger populations, our theory calls for ecological red teaming and population pacing: gradually deploying larger
agent populations in controlled environments, while measuring how cyber capability scales with population size, and estimating the critical population size for takeoff. Capability gains may lower this threshold, requiring re-estimation for each new model generation.
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Atomic Physics
Local polarization control of optical tweezer arrays
Xiangkai Sun, Richard Bing-Shiun Tsai, Andrew Winnicki et al. (+4 more)
Optical tweezer arrays trapping ultracold atoms and molecules are a versatile
quantum science platform with broad impact in
quantum simulation,
quantum computation, and
quantum metrology. The polarization dependence of anisotropic vector and tensor light shifts offers a degree of freedom for precise
quantum state engineering of trapped particles. Similarly, state-selective operations depend on the polarization of the addressing light through the atom–light coupling strength. Yet, methods for controlling the polarization of individual tweezers or local addressing beams remain an experimental challenge. Here, we demonstrate independent, site-resolved linear polarization rotation across an optical tweezer array by exploiting the local birefringence tunability of a spatial light modulator. Applying this capability to an array of $^{88}\rm{Sr}$ atoms, we homogenize differential light shifts imparted by the 813-nm tweezers to the narrow ${^1{\rm S}_0}\leftrightarrow{^3{\rm P}_1}$ transition, enabling sideband cooling under a magic-angle condition. Furthermore, we show dynamic transport of trapped atoms across distinct polarization zones with high survival and preserved coherence. Finally, we characterize tweezer polarization noise and demonstrate closed-loop stabilization referenced to the atomic transition frequency, reaching mrad-level stability. Our work establishes a technique for manipulating polarization-sensitive atomic and molecular transitions in tweezer architectures, with immediate applications to optical tweezer clocks and multi-zone
quantum processors.
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All authors Xiangkai Sun, Richard Bing-Shiun Tsai, Andrew Winnicki, Yuan Le, Kon H. Leung, Nelson Darkwah Oppong, Manuel Endres
Computational Physics · Cross-listed from math.NA
A neural characteristic mapping method: Lagrangian PINNs based on flow maps for transport-dominated problems
Martin Campos Pinto, Victor Fournet, Emmanuel Franck et al. (+2 more)
We propose a novel numerical method for transport-dominated problems using Physics-Informed Neural Networks (PINNs). In such problems, the solution can develop large gradients and fine structures over time. This is particularly challenging for classical PINNs, which represent the solution itself. In our approach, rather than the solution, we choose to approximate the flow map of the equation. The solution is then recovered by pulling back the exact initial condition, so that its fine structures are produced by the map rather than represented by the network. Our method is constructed by approximating the flow map of the equation with multiple PINNs, one per time subinterval, and composing them using the semigroup property of the flow, so that each network only has to represent a map close to the identity. To highlight the advantages of our method compared to classical PINNs or classical numerical schemes, we present numerical results on the linear advection equation, the incompressible Euler equation in vorticity formulation, the Vlasov-Poisson equation, and finally a drift-kinetic equation.
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All authors Martin Campos Pinto, Victor Fournet, Emmanuel Franck, Philipp Krah, Victor Michel-Dansac
Computational Physics · Cross-listed from cs.AI
Prior or Feedback? What an LLM Uses When Adapting Neural Operators
Julian Chan and Javier Mora Jimenez
Do LLM scientific
agents rely only on their initial task context, or do they adapt their decisions in response to experimental feedback? We study this question in neural operator adaptation, where a large
language model (LLM) selects fine-tuning configurations under a limited trial budget. Across transfers within and between partial differential equation (PDE) families, the LLM achieves lower held-out test nRMSE than random search and
Bayesian optimisation in nearly every matched comparison. Endpoint performance alone cannot distinguish what happens, so we verify each attribution with controlled interventions. Before observing any validation score, the LLM's first configuration already ranks near the top of the corresponding random-search pool, indicating a useful initial bias. A complementary cold-start intervention shows that the selected base learning rate shifts with the PDE description. Once feedback becomes available, reassigning validation scores among evaluated configurations changes the next proposal in every case tested, whereas a value-preserving rewrite produces no comparable aggregate effect. These interventions establish that the LLM's decision-level actions respond to the given task and observed outcomes, showing that it combines a task-dependent prior with sensitivity to experimental feedback.
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Chemical Physics
MERCE: Method-Space Correlation Energy Extrapolation toward High-Rank Coupled Cluster Accuracy
Mateusz Witkowski, Szymon Śmiga, Pavlo O. Dral et al. (+1 more)
Energy extrapolation is a well-established strategy for approaching
quantum-chemical limits without performing prohibitively expensive calculations. Complete basis set extrapolation is widely used, whereas extrapolation across electronic structure methods remains much less developed. Method-Space Effective-Rank Correlation Energy Extrapolation (MERCE) is introduced as an adaptive effective-rank framework for estimating high-rank finite-basis correlation energies from second-order Moller-Plesset perturbation theory (MP2), coupled cluster with single and double excitations (CCSD), and CCSD with perturbative triples [CCSD(T)]. The method assigns adaptive effective ranks to MP2 and CCSD(T) from the local MP2-CCSD-CCSD(T) correlation energy pattern and fits a compact three-point extrapolation form for each system. A hierarchical model-selection protocol balances cross-
dataset transferability, maximum
dataset-level mean absolute error (MAE) ratios, robustness on developmental sets referenced to full configuration interaction (FCI), size-consistency defects, and performance for A24 noncovalent interaction energies. Across the chemically diverse
benchmarks used during model development, the selected MERCE model substantially reduces CCSD(T) errors and frequently improves upon the tested higher-rank coupled cluster baselines. Transferability is further assessed using calculations performed only after the functional form and parameters were frozen. Formal nonadditivity is quantified using artificial noninteracting pairs and the actual A24 and selected S66 component calculations.
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All authors Mateusz Witkowski, Szymon Śmiga, Pavlo O. Dral, Ireneusz Grabowski
Instrumentation and Detectors
Cavity Characterization for the Princeton Axion Search
Nate Otto, Jianyang Fu, Joelle-Marie Begin et al. (+4 more)
The Princeton
Axion Search (PXS) targets the mass window $0.8\text{--}2.1~\mu\text{eV}$ (corresponding to frequencies $200\text{--}500~\text{MHz}$) at DFSZ sensitivity. The experiment, a haloscope, will use a cylindrical copper-plated aluminum cavity of $700~\text{mm}$ diameter and $1022~\text{mm}$ height, immersed in a $5~\text{T}$ solenoidal
magnetic field. A $152~\text{mm}$ diameter tuning rod spanning the height of the cavity rotates to tune the primary mode resonance from $390\text{--}480~\text{MHz}$, the frequency range for initial operations. We deduce the electric and
magnetic field mode patterns using a combination of dielectric and metallic bead perturbations on the empty cavity. The measured mode frequencies and the $\text{TM}_{010}$ field profile, as a function of tuning rod position, are in excellent agreement with simulations. We describe the effect of fabrication and assembly imperfections, including the bowing of end plates and tilt of the tuning rod. The welded seams and end-cap joints are demonstrated to not significantly degrade $Q$. We combine measurement and simulation to assess an uncertainty on form factor across the full tuning range. Our results validate PXS's cavity architecture and inform future searches for sub-$\mu\text{eV}$
axions, in which large-volume high-$Q$ structures are required to reach QCD sensitivity.
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All authors Nate Otto, Jianyang Fu, Joelle-Marie Begin, Saptarshi Chaudhuri, Roman Kolevatov, Lyman Page, Joe Wiedemann
Medical Physics
Deep preferential evolution guided by pairwise comparisons enables motion correction in photoacoustic tomography
Karteekeya Sastry, Yousuf Aborahama, Junhao Zhu et al. (+2 more)
Motion artifacts in three-dimensional photoacoustic
tomography (PAT), caused by extended mechanical scanning of sparse arrays, degrade image quality. Since sparse sampling results in low-quality sub-reconstructions, tracking motion directly is challenging. While final image quality could in principle guide artifact correction, conventional regularizers do not accurately reflect photoacoustic image quality. Here, we introduce deep preferential
evolution (DPE), a derivative-free optimization framework where a learned image comparator guides evolutionary search over reconstruction parameters. By learning relative quality differences between same-target image pairs rather than absolute quality scores, the comparator generalized more robustly from simulation to in-vivo images (after unlabeled domain calibration) than an absolute scorer. Comparator-based DPE successfully corrected synthetic motion across diverse anatomies and backgrounds, and it mitigated artifacts from natural, unconstrained motion in human palm images. These results demonstrate that pairwise learned comparison can provide a transferable optimization objective for high-dimensional image restoration when labeled experimental training data are scarce.
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All authors Karteekeya Sastry, Yousuf Aborahama, Junhao Zhu, Manxiu Cui, Lihong V. Wang
Fluid Dynamics
Learning minimum-time navigation policies in two-dimensional flows with a genetic algorithm
Vladimir Parfenyev
We study minimum-time navigation between two points in a known two-dimensional fluid flow for a vessel with fixed-magnitude slip velocity and controllable direction. The navigation policy is parameterized by a neural network and optimized using a genetic
algorithm that evolves an ensemble of candidate strategies through Darwinian selection and parameter mutation, without crossover. The method recovers analytical and numerical optimal-control solutions in several
benchmark cases, including two-dimensional
turbulence, and outperforms Q-learning and one-step actor-critic method. We further show that the learned navigation strategies are robust to variations in the starting position and to unresolved small-scale turbulent fluctuations, provided that the characteristic velocity of these fluctuations remains small compared to the vessel's slip velocity. The proposed approach provides a compact representation of the navigation policy, is readily parallelizable, and eliminates the need for reward shaping, offering an efficient alternative to existing analytical and numerical methods for computing minimum-time trajectories in complex flows.
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Optics
Spatiotemporal computing for temporal nonlinear tasks using few-mode fiber and SOA nonlinearity
Adhila Nazarudeen, Silvia Ortín and Apostolos Argyris
Step-index few-mode fibers (FMFs) provide a compact passive platform for high-speed photonic information processing by exploiting modal dispersion to map temporal inputs into spatiotemporal representations with short-term
memory. In previous studies, we have demonstrated linear classification tasks with such FMFs, such as ultrafast multibit header recognition. Here, we extend the computational capability of such architectures towards solving nonlinear tasks by introducing an optical nonlinearity at the output of the dispersive medium. The FMF output is processed through parallel direct and nonlinear branches, with the latter implemented using a nonlinear
semiconductor optical amplifier (SOA). We experimentally evaluate the architecture at 28.5 Gb/s on two delayed XOR and higher-order parity tasks. We show that combining the direct and SOA-transformed representations reduces classification errors and broadens the range of operating conditions supporting low-error classification. In addition, by exploiting multiple temporal samples within each bit period as independent classifier features, we increase the dimensionality of the photonic representation and improve the computational performance.
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Fluid Dynamics
Online Learning of a Spectral Eddy Viscosity Closure for Large Eddy Simulation Using Ensemble Kalman Inversion
Katerina Kostova and Yifei Guan
Large Eddy Simulation (LES) resolves only the large, energy-containing structures of turbulent flows while modeling the effects of unresolved subgrid scales. As a result, the ac- curacy of LES strongly depends on the subgrid-scale closure. This work investigates the online calibration of a spectral viscosity closure for forced two-dimensional
turbulence using ensemble Kalman Inversion (EKI). The closure consists of a wavenumber-dependent viscosity function, which is parameterized by a 16-dimensional vector. An ensemble of potential parameter vec- tors is passed through coarse resolution LES simulations, and the resulting kinetic energy spec- tra are compared with a DNS reference spectrum up to the LES-resolved wavenumber range. Then, ensemble statistics iteratively update the spectral viscosity parameters in order to reduce the model-data discrepancy. The convergence of the spectral mismatch is used to assess the accuracy of the calibrated closure by comparing LES with the filtered DNS data in spectral space. The results demonstrate the potential of EKI-based online calibration as a derivative- free framework for constructing data-driven
turbulence closures with multiple parameters.
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Plasma Physics
Kinetic wave activity and proton heating in 3D hybrid simulations of decaying balanced and imbalanced Alfvénic turbulence
C.A. González, T.A. Bowen, A. Mallet et al. (+1 more)
We investigate turbulent dynamics from large to sub-
proton scales, the kinetic wave activity and resulting
proton heating in three-dimensional hybrid-kinetic simulations of freely decaying balanced and imbalanced Alfvénic
turbulence. We considered initial fluctuations with same amplitude and spectral shape but different degrees of correlation between velocity and
magnetic field perturbations (i.e., normalized cross-helicity), which define the corresponding turbulent regimes. We found that our balanced
turbulence simulation exhibits stronger energy dissipation and more efficient
proton heating compared to the imbalanced case. On the large fluid scales, the two simulations undergo distinct nonlinear dynamic and energy cascades, leading to different spectral properties once
turbulence is well developed. At sub-
proton scales, fluctuations are predominantly oblique with right-hand polarization, a property common to both regimes. In contrast, only the imbalanced simulation displays substantial wave power parallel to the guide
magnetic field, with clear signatures of ion-cyclotron waves. These waves are excited in regions with strong temperature anisotropy that arise during the initial nonlinear steepening of Alfvénic fluctuations, a process that simultaneously generates parallel-propagating
proton beams. Furthermore, non-Maxwellian features emerge in velocity space, indicating different heating mechanisms in balanced and imbalanced regimes and providing evidence for the role of kinetic instabilities in regulating the turbulent dynamics. Overall, these results show that cross-helicity is a key parameter controlling the large-scale
evolution, kinetic activity, and energy dissipation in collisionless turbulent
plasmas.
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All authors C.A. González, T.A. Bowen, A. Mallet, A. Tenerani
Fluid Dynamics · Cross-listed from math.NA
Variational Derivation of the Weak Form for Viscous Flows Involving Free Surfaces with Implications for Geodynamic Simulations
Karim Norouzi Moghanjoghi, Javier Garcia Pintado, Daniel R. Shapero et al. (+1 more)
Geodynamic simulations involve solving the Stokes equations, where boundary conditions play a crucial role in governing internal dynamics and surface topography
evolution. A common approach is to model the interface in contact with air as a free surface. This interface is stress-free and deforms in response to internal force balances, which is essential for understanding processes ranging from rifted margins to subduction. However, a free surface can introduce instabilities and oscillations in the numerical solution, commonly known as the "drunken sailor" instability, forcing the use of small time steps. Geodynamic models therefore often employ stabilization terms based on the gravitational effect of free-surface motion. To date, these terms have been derived primarily on a heuristic basis. Here, we apply variational calculus to the energy-minimization formulation of incompressible Stokes flow. Using the Gateaux derivative, we derive the complete weak form for a deforming free-surface domain and show that the stabilization terms arise naturally from the free-surface assumption. We consider the linear-viscosity case and identify two boundary-integral contributions: one associated with internal viscous deformation and the other with gravitational body force. Two
benchmark cases demonstrate the consistency of the derived formulation with previously proposed stabilization approaches. Our results provide a mathematical foundation for the commonly used
gravity stabilization and identify an additional viscous boundary contribution.
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All authors Karim Norouzi Moghanjoghi, Javier Garcia Pintado, Daniel R. Shapero, Marta Perez-Gussinye
Optics
Harnessing Machine Learning for Ultrafast Science in Facility Settings: Opportunities and Challenges
Mousumi Upadhyay Kahaly, Ishraq Md Anjum, Davorin Peceli et al. (+2 more)
Ultrafast science is rapidly evolving from a collection of specialized
laser experiments into a facility-scale, data-intensive research endeavour. High-repetition-rate femtosecond and attosecond sources, synchronized multidimensional diagnostics, and increasingly nonlinear
laser-matter interactions now generate heterogeneous data streams whose volume, speed, and complexity often exceed the capacity of conventional manual workflows. This chapter examines how machine learning can become an enabling layer for next-generation ultrafast facilities, with emphasis on the Extreme Light Infrastructure (ELI) ecosystem. We discuss the scientific drivers for artificial-intelligence-assisted experimentation, propose a facility-scale digital architecture spanning acquisition, ingestion, storage, analytics, machine learning, adaptive feedback, and user interfaces, and review representative examples from ELI ALPS, ELI Beamlines, and ELI-NP. The central thesis is that machine learning should not be treated merely as an offline data-analysis tool; rather, when constrained by physical insight and embedded in robust data infrastructure, it can support real-time diagnostics, surrogate modeling, anomaly detection, experimental optimization, and progressively autonomous discovery workflows.
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All authors Mousumi Upadhyay Kahaly, Ishraq Md Anjum, Davorin Peceli, Zsolt Divek, Subhendu Kahaly
Fluid Dynamics
Extending convolutional neural networks to finite-volume meshes for fine-scale reconstruction of wall-bounded turbulence
Hesam Tofighian, Jordan A. Denev and Nikolai Kornev
Standard convolutional neural networks for
turbulence super-resolution are restricted to uniform structured grids, preventing their direct application to the non-uniform and non-orthogonal meshes commonly used in wall-bounded flows and complex geometries. We introduce a finite-volume convolution unit (FVCU) that removes this restriction. The key idea is to construct convolutional layers using finite-volume operators that are applicable to non-uniform and unstructured meshes. To this end, we use an exact representation of a 3D convolution as a linear combination of successive axis-wise convolutions. We adopt axis-wise gradient, averaging, and identity operators, as they can be evaluated on arbitrary polyhedral
cells using finite-volume discretization. The resulting FVCU is well-defined on general meshes, serving as a drop-in replacement for conventional convolution layers. The FVCU is embedded in a super-resolution neural network and trained to reconstruct 3D velocity fields from $4\times$ coarsened low-resolution fields in turbulent channel flow on a non-uniform mesh, and in circular-pipe flow on a non-orthogonal O-grid mesh. Inspired by the role of the Van Driest damping function in near-wall subgrid-scale modelling in large-eddy simulations, this function is supplied as a conditional input to encode wall proximity. An ablation study confirms that the network interprets this input in a physically consistent manner. In both configurations, the model accurately reconstructs instantaneous velocity fields, Reynolds-stresses and their anisotropy, and energy spectra beyond the Nyquist limit of the low-resolution mesh. On an elliptic pipe unseen during training, the model reproduces the main flow features and local statistics, with a modest overprediction of small-scale energy. These results demonstrate a framework for extending CNN-based super-resolution to general finite-volume grids.
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Instrumentation and Detectors
First demonstration of $50\,\mathrm{ps}$ four-dimensional vertex reconstruction with the Timepix4 beam telescope
David Bacher, Kazu Akiba, Martin van Beuzekom et al. (+10 more)
The first demonstration of 4D vertex reconstruction at the $50\,\mathrm{ps}$ scale using a silicon pixel detector is reported. Hadronic interactions are produced by directing a $180\,\mathrm{GeV}/c$ mixed-
hadron beam onto a fixed target upstream of the Timepix4 beam
telescope at the CERN SPS. The resulting particles are reconstructed using 4D track and vertex fits that include time directly alongside the spatial parameters. The vertex time resolution follows the expected statistical scaling with the number of timing measurements, reaching $54\pm5\,\mathrm{ps}$ for vertices reconstructed from 16 timing-plane clusters. An irreducible component of $29.2\pm0.6\,\mathrm{ps}$ remains in the fitted resolution model, including sizeable contributions from the clock distribution and the time reference system rather than from the combination of the timing measurements. The observed scaling down to $54\,\mathrm{ps}$ without an unforeseen resolution floor above represents a significant experimental step towards precision-timing in future high-pile-up detectors.
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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, Loris Martinazzoli, Tommaso Pajero, Yutaro Takahashi, Espen de Wit
Atomic and Molecular Clusters
Multiple photoelectron inelastic scattering in large He nanodroplets
Niklas Scheel, Narcis Silviu Blaj, Rajni Rajni et al. (+7 more)
Helium nanodroplets are ideal testbeds for elucidating secondary processes induced by ionizing radiation in condensed matter. Here we study photoionization electron spectra of large He nanodroplets in the extreme ultraviolet region, where photoelectrons undergo multiple inelastic collisions with He atoms in the nanodroplets. The resulting characteristic electron energy-loss features are identified by their dependence on
photon energy and nanodroplet size and by comparison with a simple numerical simulation. Conditions for minimizing electron scattering with the He nanodroplets are discussed.
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All authors Niklas Scheel, Narcis Silviu Blaj, Rajni Rajni, Asbjørn Ørnemark Lægdsmand, Jayesh Sadasivan, Subhendu De, Sivarama Krishnan, John Bozek, Aleksandar R. Milosavljevic, Marcel Mudrich