Plasma Physics
Neural network predictions of plasma confinement loss in Wendelstein 7-X pellet-fueled discharges
K. C. Hammond, J.-P. Bähner, J. Baldzuhn et al. (+17 more)
The energy confinement time is a key parameter of a magnetized fusion
plasma, helping to determine whether ignition can occur. Experiments in tokamaks and stellarators have shown that the confinement time can be improved via pellet injection. The state of enhanced confinement brought about by a given pellet typically deteriorates over time unless and until a subsequent pellet is injected. In this work, we develop a data-driven model that predicts, at any moment, the remaining time before a
plasma in Wendelstein 7-X (W7-X) will lose its enhanced confinement state. This "remaining time" metric effectively sets a deadline for when the next pellet must be injected in order to steadily maintain a high confinement time. We describe the development and training of the model and compare its predictions to observations from previous experiments. At least 90% of the model predictions are accurate to within 51 ms, which is below the typical W7-X energy confinement time as well as the minimum time separation between subsequent pellet injections. The model can be evaluated rapidly and could be suitable for use in a control system that optimizes the pellet injection rate in real time.
Read the full abstract ▾
All authors K. C. Hammond, J.-P. Bähner, J. Baldzuhn, S. Bozhenkov, K. J. Brunner, A. Dinklage, E. Edlund, G. Fuchert, M. Huck, A. I. Mohammed, N. Pablant, M. Porkolab, G. L. Schmidt, J. Smoniewski, T. Stange, N. Tamura, E. Villalobos Granados, A. von Stechow, G. Weir, the W7-X team
Fluid Dynamics
A Fully Parallel Dual-Grid Immersed-Boundary Framework for Flow-Induced Sound from Complex Moving and Deforming Bodies
Amirhossein Fardi and Muhammad Saif Ullah Khalid
Predicting flow-induced sound from moving and deforming bodies is computationally demanding because the near-field hydrodynamics and the far-field acoustics require substantially different spatial resolutions and domain extents. A fully parallel hybrid framework is developed to address this disparity by coupling an incompressible Navier-Stokes solver to an acoustic perturbation equation (APE) solver on independently generated, non-conforming Cartesian grids. A sharp-interface ghost-
cell immersed boundary method, with radial-basis-function reconstruction, imposes the boundary conditions for complex moving geometries on both grids. The converged flow field supplies the acoustic source through a one-way, precomputed parallel interpolation operator. This arrangement confines the flow grid to the body and wake while allowing the acoustic grid to extend independently into the far field. The framework is validated for Gaussian-pulse propagation, pulse scattering by a rigid cylinder, tonal sound from flow past a cylinder, and radiation from a traveling wavy foil. The predicted waveforms, wavelengths, pressure amplitudes, and radiation patterns agree closely with analytical solutions and published reference data. Applications to eel and Jack fish locomotion, a four-eel school, a manta ray, and a harbor seal further demonstrate the treatment of realistic three-dimensional morphologies, large boundary deformation, and multiple interacting swimmers. The results resolve morphology-dependent acoustic signatures and interference-driven changes in far-field directivity without requiring the flow grid to span the acoustic far field.
Read the full abstract ▾
Optics
Toward Topology-Optimized Foundry PDKs: A Seeded Design Framework for Multimode Interferometers
Jacob M. Hiesener, Archana Kaushalram, Joshua J. Wong et al. (+2 more)
We present an end-to-end design methodology for multimode interferometer (MMI)-based photonic devices that combines parameter optimization (PO) on analytical models with seeded
topology optimization (TO) to maximize performance while preserving foundry design-rule compliance. A PO seed device is further refined via seeded TO, accessing a larger design space than analytical or parameterized methods alone can reach. We validate this pipeline on a 1x2 splitter, a TE modal multiplexer, and a polarization splitter, fabricating and measuring the first two on a commercial foundry process. Seeded TO reduces the measured insertion loss of the 1x2 splitter from 0.20 to 0.14 dB and improves TE00 transmission of the modal multiplexer from -2.79 to -1.01 dB over O-band. Applying this pipeline to a commercial foundry process design kit (PDK)-provided 1x2 and 2x2 splitter improves simulated transmission and tightens the 2x2 splitting ratio from 0.524 to 0.506, along with improved fabrication robustness, offering a practical, foundry-validated route toward incorporating TO-designed components into commercial PDKs.
Read the full abstract ▾
All authors Jacob M. Hiesener, Archana Kaushalram, Joshua J. Wong, Robert P. Pesch, Stephen E. Ralph
Plasma Physics
Numerical investigations of low-latitude ground magnetic fields due to cavity mode oscillations
Khilav Majmudar, Robert L. Lysak and Kazue Takahashi
Pi2 oscillations have been known to be associated with cavity mode oscillations (CMOs) in the inner magnetosphere at low latitudes. CMOs can also be excited by interplanetary shocks on the dayside. We present results from a global linear MHD wave model in spherical coordinates that allows us to analyse these waves at equatorial latitudes. We see that the model reproduces some of the principal CMO harmonics. The standing wave cavity modes are set up due to a dense plasmasphere. The CMOs associated with interplanetary shocks are compared with ground observations from the EMMA array at mid-latitudes. In accordance with observations of Pi2 pulsations, we also see a small phase difference between compressional mode Alfvén waves at midnight at low altitudes and on the noon ground, both at equatorial latitudes. This supports the finding that upon being excited in the nightside inner magnetosphere, these waves travel quickly around to the dayside. We compare our results to observations of the night-to-day time delay of ground Pi2 pulsations.
Read the full abstract ▾
Optics
Ultra-broadband integrated optical parametric amplifier for quantum sensing
Kai-Chi Chang, Tushar Sanjay Karnik, Chun-Ho Lee et al. (+9 more)
Although thin-film lithium niobate (TFLN) facilitates efficient signal generation and nonlinear and
quantum interactions, the realization of optical parametric amplification (OPA) that can provide simultaneous ultra-broadband and high-gain operation in an integrated chip continues to pose a challenge. Here we demonstrate continuous-wave-pumped OPA in an X-cut MgO-doped, dispersion-engineered, and adaptively-poled TFLN waveguide of 1.6 cm length, achieving a flat-top profile covering a 450 nm-wide optical wavelength window, corresponding to a 3-dB gain bandwidth of about 56 THz. Among reported TFLN platforms, our device exhibits the broadest 3-dB gain bandwidth. The same device can be pumped either directly at visible wavelengths through second-order x(2) interactions or in the telecom band through cascaded x(2) processes, the latter eliminating the need for a high-power visible pump
laser. We achieve maximum gains of 8.87 +- 0.39 dB and 10.79 +- 0.43 dB at on-chip pump powers of 74 mW and 170 mW for the direct and cascaded schemes, respectively. We further directly probe OPA gain across the 1650-1900 nm wavelength range, where experimental gain measurements have remained scarce. With a normalized on-chip gain of 1.21 dB per W per mm, our device sets a new
benchmark among reported cascaded x(2) nonlinear processes. This work advances the realization of integrated optical parametric amplifiers, offering high efficiency, robust gain, ultra-broadband bandwidth, and continuous-wave operation, thereby enabling new capabilities for next-generation
quantum sensing and photonic systems.
Read the full abstract ▾
All authors Kai-Chi Chang, Tushar Sanjay Karnik, Chun-Ho Lee, Kiyoung Ko, Xinyi Ren, Ian Christen, Reshma Kopparapu, Clayton Cheung, Kiwon Kwon, Kamila Kunes, Zaijun Chen, Mengjie Yu
Optics
A Comprehensive Review of Large Language Models for Nanophotonics: From Surrogate Modeling to Autonomous Design
Huanshu Zhang, Kegeng Tang, Lei Kang et al. (+3 more)
Metasurfaces have revolutionized the development of photonic devices by enabling unprecedented precision in light manipulation. However, their design processes are often constrained by computationally expensive simulations and complex high-dimensional design spaces. Although deep learning has accelerated the design process by serving as a surrogate model, it remains constrained by task-specific architectures and lacks universal
reasoning capabilities. This review surveys how Large
Language Models (LLMs) are adding semantic interfaces, code generation, and tool orchestration to established numerical nanophotonic workflows. We first outline the development from classical neural networks to
transformer-based models and their applications in nanophotonic design. We then review the emergence of LLM-related methods in nanophotonics and organize them into two operational modes: surrogate models that treat structure-spectrum mapping as a
language task, and agentic systems that have been demonstrated to generate code, orchestrate selected simulation steps, and support closed-loop optimization. Furthermore, to identify future cross-disciplinary opportunities, we briefly explore applications of LLMs in research fields such as materials science and wireless communications. This review concludes by looking ahead to the next generation of
multimodal foundation models with physical perception capabilities. In this
vision, artificial intelligence is evolving from passive tools into active collaborators, participating in autonomous scientific discovery.
Read the full abstract ▾
All authors Huanshu Zhang, Kegeng Tang, Lei Kang, Sawyer D. Campbell, Zihao Wang, Douglas H. Werner
Geophysics
Yingjie Xu, Siwei Yu and Jianwei Ma
Classical seismic data reconstruction relies on manually designed structural priors and iterative operators, whose coupled design space is far larger than manual trial and error can explore systematically. Deep-learning methods encode the reconstruction rules in learned weights rather than in an explicit operator that can be inspected and modified. We propose SeisEvo (Seismic
Algorithm Evolution), which does not optimize a single reconstruction result but searches for the
algorithm that produces it. Starting from a classical reconstruction
algorithm, an LLM-driven multi-
agent search modifies only the components that the user has opened for editing, without prescribing the mechanism to be discovered. Candidates that violate the physical constraints of the task are rejected outright, and the remaining ones are scored by execution. The output is neither an
agent system nor a neural network, but a standalone white-box
algorithm that requires no
agent or neural network at inference time. For interpolation without added noise, the search discovered a residual-gated, phase-aligned dip-consistency projection; Evo-POCS improves the SNR over classic POCS by 3.49 dB on average across missing ratios from 30% to 70%. For simultaneous interpolation and denoising, it discovered a reliability-grouped singular-value shrinkage; Evo-MSSA improves the average reconstruction SNR by more than 7 dB over classic MSSA and by more than 3 dB over a stronger rank-reduction baseline. Both operators retain their gains on data not used during the search. To the best of our knowledge, this is the first study to formulate the design of a seismic reconstruction operator as a constrained, LLM-driven program
evolution task. Agentic
algorithm evolution can thus complement deep learning in discovering explicit, inspectable, and deployable seismic processing
algorithms.
Read the full abstract ▾
Fluid Dynamics
Intrusive versus non-intrusive reduced-order modeling of generalized Newtonian fluid flows
Parajal Rai, Michelle Spanjaards, Patrick Anderson et al. (+2 more)
This study compares three reduced-order modeling (ROM) approaches for flow simulations of generalized Newtonian fluids described by the Carreau rheological model. All three methods rely on offline snapshot generation in the rheological parameter space using the full-order model (FOM), followed by a proper orthogonal decomposition (POD) of the snapshot
matrix to obtain a reduced basis, but they differ in how they reconstruct the solution for new parameter values in the online phase. The three ROM approaches examined are: (i) intrusive Galerkin projection onto the reduced basis with full operator reassembly (ROM-FULL), (ii) intrusive hyper-reduced Galerkin projection using the discrete empirical interpolation method with GappyPOD for the nonlinear term (ROM-DEIM), and (iii) a non-intrusive interpolation approach using radial basis function interpolation (ROM-RBF). We demonstrate these three ROM approaches on two
benchmark flows: a lid-driven cavity and a sphere settling in a closed container, spanning boundary-driven and force-driven flows. ROM-FULL achieves the highest accuracy but requires reassembling the full-order nonlinear operator during the online phase, whereas ROM-RBF is fully non-intrusive, and its accuracy is closely tied to data availability and deteriorates outside the training data range. ROM-DEIM offers a balance between efficiency and accuracy, even when data are sparse. The results provide guidelines for selecting an appropriate ROM strategy based on solver accessibility, computational efficiency, and desired accuracy.
Read the full abstract ▾
All authors Parajal Rai, Michelle Spanjaards, Patrick Anderson, Ye Wang, Nick Jaensson
Medical Physics
Angle-domain null subtraction imaging from beamformed plane-wave data
Henri Leroy
Null subtraction imaging (NSI) narrows the apparent lateral response by combining fields produced with a zero-mean apodization and two DC-offset variants. Usually, NSI requires channel data and flexible beamformers (Receive-NSI). We investigate Angle-NSI, which operates on per-angle images during coherent plane-wave compounding. A
matrix formulation was used to distinguish receive-domain and angle-domain null subtraction expressions. The method was evaluated using a simulated point target, experimental resolution phantom targets, an open microbubble (MB) traces
dataset and two in vivo carotid acquisitions. Angle sampling, noise, phase jitter and target position were swept in simulation. A synchronized GPU
benchmark compared the complete reconstructions. Six beamforming methods were compared: delay-and-sum (DAS), coherence-factor-weighted DAS (CF-DAS), minimum variance beamforming (MV), delay-multiply-and-sum (DMAS), Receive-NSI and Angle-NSI. Angle-NSI reduced the simulated -6 dB lateral width from 0.226 mm for DAS to 0.116 mm, compared with 0.009 mm for Receive-NSI, while the axial width was 0.720 mm for these three methods. The lateral narrowing was confirmed on an experimental resolution phantom. Angle-NSI had shorter reconstruction time than Receive-NSI and the lowest post-delay processing time among the nonlinear methods. In power Doppler, microbubble trace widths were reduced by 50.2% for Receive-NSI and 30.9% for Angle-NSI compared to DAS. On a phantom and in vivo, both NSI variants reduced generalized contrast-to-noise ratio relative to the conventional methods. Angle-NSI provides simple post-processing of per-angle beamformed images. It narrows the apparent lateral response at low computational cost, at the expense of B-mode detectability: this makes it a well-suited alternative for high-resolution power Doppler applications.
Read the full abstract ▾
Optics
A compact low-frequency optomechanical triaxial inertial sensor
Daniel George, Jose D. Hernandez Rivero, Moritz Mehmet et al. (+5 more)
Triaxial optomechanical accelerometers offer compact, ground-testable alternatives to electrostatic sensors for satellite geodesy, seismometry, and various other applications. We demonstrate a low-CSWaP triaxial sensor using monolithic fused-silica resonators with dual heterodyne interferometric readout. The X axis reaches a 60~pico-g/$\sqrt{\mathrm{Hz}}$ readout noise floor, and the in-plane axes resolve ambient seismic ground motion in agreement with a co-located commercial seismometer from 4~mHz to 8~Hz. The Z axis exhibits higher noise under $1g$ loading due to
gravity-induced
geometric stiffening (44.45~Hz versus 11.07~Hz in a 0g-equivalent configuration), with measurements indicating in-orbit performance on par with the in-plane sensors. These results support the feasibility of triaxial optomechanical accelerometry for space missions and ground-based applications.
Read the full abstract ▾
All authors Daniel George, Jose D. Hernandez Rivero, Moritz Mehmet, Ramses Miranda Espino, Xiangyu Guo, Andrea Nelson, Jose Sanjuan, Felipe Guzman
Instrumentation and Detectors
Transferable Fast Calorimeter Shower Generation via Multi-Geometry Pre-training
Thorsten Buss, Henry Day-Hall, Frank Gaede et al. (+4 more)
Detailed Geant4 simulation of calorimeter showers dominates the computing budget of high-energy physics experiments. Deep generative surrogates reduce this cost, but they have remained tied to the detector they were trained on, so each new geometry needs a large in-domain
dataset. We study whether a single point cloud shower generator can be pre-trained on multiple detectors and transferred to unseen calorimeters. The pre-training geometries come from synthetic
geometric variation rather than real-detector data. We introduce SimpleBox, a family of $10^4$ box calorimeters spanning the plane of sampling fraction and longitudinal
segmentation, and
benchmark it against pre-training on realistic detectors. On a calorimeter unseen in pre-training, with $10^3$ target showers for fine-tuning, the two priors reduce the aggregated sliced Wasserstein distance to Geant4 by factors of 5.2 (synthetic) and 8.0 (realistic) relative to training from scratch. At larger target sizes the synthetic prior performs better than the realistic one.
Geometric diversity alone is therefore a practical way to pre-train a transferable shower generator.
Read the full abstract ▾
All authors Thorsten Buss, Henry Day-Hall, Frank Gaede, Gregor Kasieczka, Katja Krüger, Peter McKeown, Lorenzo Valente
Atomic Physics
Probing the electroweak structure of nuclei with rare atoms and molecules
Silviu-Marian Udrescu, Antoine Belley, Jason D. Holt et al. (+2 more)
Precision experiments of atoms and molecules have become a powerful probe of the electroweak structure of atomic
nuclei and of physics beyond the Standard Model. We review how the interaction between a
nucleus and its surrounding bound electrons can be exploited to precisely measure the electromagnetic, parity-violating, and CP-violating properties of
nuclei and their fundamental constituents. We focus on rare, unstable isotopes, surveying the experimental techniques and facilities developed in recent years that have extended these measurements to the most exotic regions of the
nuclear chart. Recent advances in the precision control and interrogation of single molecules, together with direct
laser excitation of
nuclear transitions, are opening new frontiers in
nuclear and particle physics. At the same time, progress in
nuclear theory, machine learning, and high-performance computing is strengthening the connection between our microscopic description of nature and laboratory observables. In many cases, the precision with which
nuclear and particle physics properties can be extracted is now limited not by experiment, but by the molecular, atomic, or
nuclear theory required to interpret the measurements. This challenge presents a major opportunity for combined theoretical and experimental advances that will enable future discoveries.
Read the full abstract ▾
All authors Silviu-Marian Udrescu, Antoine Belley, Jason D. Holt, Gilad Perez, Ronald F. Garcia Ruiz
Instrumentation and Detectors · Cross-listed from astro-ph.IM
Radiation damage to the Hubble Space Telescope has been several years out of phase with the Solar cycle
Gavin Leroy, Juan Paolo Lorenzo Gerardo Barrios, Maximilian von Wietersheim-Kramsta et al. (+8 more)
As well as obtaining beautiful images of the Universe, the Hubble Space
Telescope's CCD detectors are sensitive radiation dosimeters that have been monitored in Low Earth Orbit for more than 24 years. The rate of radiation damage they received has varied over each
Solar cycle, but several years out of phase with the appearance of sunspots or coronal mass ejections. We investigate functional forms that successfully fit the time series of damage to
telescopes elsewhere in the
Solar system. We obtain remarkably accurate fits to Hubble data but with physically absurd parameter values. During image post-processing, such fits can be used empirically, to correct more than 99.5% of the radiation damage's effect on image quality. However, fits to the time series with physically reasonable parameters produce worse performance. Our results highlight the diversity of radiation environments in different parts of our
Solar system, and the complexity of Low Earth Orbit in particular. Our results also motivate continued monitoring of radiation damage to currently operational spacecraft, to more reliably predict the rate of degradation in (and useful lifespan of) future missions.
Read the full abstract ▾
All authors Gavin Leroy, Juan Paolo Lorenzo Gerardo Barrios, Maximilian von Wietersheim-Kramsta, Richard Massey, Richard G. Hayes, Jacob A. Kegerreis, David Lagattuta, Zane D. Lentz, James W. Nightingale, Jesper Skottfelt, Felix Vecchi
Physics Education
Teaching Quantum Design Automation with Block-Based Programming
Damian Rovara and Robert Wille
As
quantum circuits grow beyond small toy examples, preparing them for execution on physical devices becomes increasingly complex. Design automation is therefore essential for scalable
quantum computing: Compilation procedures optimize resource requirements and transform
circuits to a format compatible with specific hardware; resource estimation evaluates execution cost; verification methods prove
circuit correctness. However, these concepts present a steep learning curve for novices, particularly when
quantum circuits are introduced through low-level textual representations. To address this, we present a block-based programming framework for
quantum design automation, implemented as an extension to the Scratch programming platform. This system allows users to build
quantum circuits as a sequence of blocks and embed them in classical control logic to perform evaluations, compare simulation results, and directly apply different design automation techniques. We evaluated the approach in a user study with computer science students, who completed guided exercises using the platform and provided structured feedback in the form of self-reports and short knowledge assessments. Results demonstrate strong understanding and confidence in
quantum design automation concepts, suggesting that the block-based approach successfully lowers the entry barrier to
quantum design automation. The implemented framework is open-source and available at https://github.com/munich-
quantum-toolkit/scratch-
quantum.
Read the full abstract ▾
Data Analysis and Statistics · Cross-listed from cs.LG
Safe Domain Adaptation for Physics: Overcoming Nuisances, Label Shifts, and Simulation Priors
Ivan Kharuk
Domain adaptation is widely used to make neural networks trained on simulations applicable to experimental data. Its premise is that the two domains differ only in nuisances, and that the quantity of interest is distributed identically in both. In physics neither assumption holds: simulations can be wrong about the physics, and the distribution of the target quantity - an energy spectrum, a
redshift distribution - is often the measurement itself. We study the consequences of such mismatches on a toy air-shower
benchmark in which a detector-response nuisance, a physical simulation shift, and an energy-spectrum shift can be switched on separately or together. Standard
adversarial adaptation handles the conditional shifts, but once the two spectra differ it aligns them, replacing an uncontrolled bias by one anchored on the simulation prior. We present adaptive domain adaptation, which reweights the simulated events so as to focus domain adaptation on the genuine physical mismatch alone. Since the predicted spectrum depends on model training configuration, we provide a label-free model selection rule for selecting the near-the-best operation point.
Read the full abstract ▾
Atomic Physics
Ultrafast and high resolution spatial light modulation for cold atoms
Alexander Dennisovich Deters, Yanfei Li, Alexander Douglas et al. (+2 more)
Programmable arrays of ultracold atoms are a leading platform for
quantum computation and simulation, enabling state-of-the-art implementations of
quantum error correction, and analog simulations of Hubbard models that address open problems in condensed matter physics. In these systems, all local control is mediated through precisely shaped optical fields, and so the challenge of managing many-body
quantum states becomes an exercise in optical design. In particular, one wishes for fast, flexible control with low disorder and heating, and access to large arrays with many atoms. An ideal optical system therefore must generate arbitrary patterns with high spatial resolution and low disorder, and alter these patterns on a timescale that is faster than the relevant atomic dynamics. Here, we present an optical system that is comparable to previous approaches in scale, while advancing all other axes. We demonstrate arbitrary pattern generation with $10^{-3}$ intensity resolution, a frame rate of $>84$ MFPS (megaframes per second), and a spatial resolution of $83 \times 52$ beam waists (with $11 \times 52$ waists accessible via a single $40$ GHz electro-optic modulator). These capabilities unlock a new class of experiments. We develop and numerically validate a scheme for fully programmable Hubbard models, with time-dependent control over local chemical potentials, tunneling amplitudes, on-site interactions, and patterns of artificial
magnetic flux. The same architecture performs fast, arbitrary permutations of tweezers in 2D, decoupling optical constraints from the design of high-rate error-correcting codes.
Read the full abstract ▾
All authors Alexander Dennisovich Deters, Yanfei Li, Alexander Douglas, Markus Greiner, Aaron W. Young
Computational Physics · Cross-listed from cond-mat.dis-nn
Quantum Geometric Tensor Preconditioning for Stable Training of Recurrent Neural Quantum States
Adil Attar, Amine M. Aboussalah and Mohamed Hibat-Allah
Neural
Quantum States (NQS) provide a powerful neural network-based variational framework for representing many-body wave functions and solving for ground states. Recurrent Neural Networks (RNNs) are particularly promising owing to their relatively low computational cost and their autoregressive property, which enables perfect sampling. Recently, RNNs have been reported to be unstable under
curvature-based optimizers such as the minimum-step
stochastic reconfiguration (minSR) method. In this paper, we address this perceived limitation and show that minSR can be stabilized through simple regularization techniques, enabling robust training of RNN-based NQS with only a few samples. Our approach outperforms the Adam optimizer on the one-dimensional transverse-field Ising model and the one-dimensional cluster state, and provides competitive results on the two-dimensional Heisenberg and $J_1-J_2$ models. This work offers a promising pathway for using modern optimization techniques with autoregressive NQS to address open questions in
quantum simulation.
Read the full abstract ▾
Applied Physics
Achieving Long Retention in Area-Dependent Resistive Memory with Phase-Separated Amorphous Tantalum Oxide
Sangyong Lee, Anton V. Ievlev, Dongjae Shin et al. (+2 more)
Resistive random-access
memory (ReRAM) is a promising future nonvolatile
memory technology. Most ReRAM exhibit a fundamental tradeoff: filament-type ReRAM provides long data retention but suffers from poor uniformity and high switching current, whereas nonfilamentary ReRAM shows lower-current, area-dependent switching but generally poor retention. No two-terminal device has been able to overcome this tradeoff. In this work, we present a two-terminal Ta2O5/ TaOX resistive
memory cell that achieves both nonfilamentary switching and long retention. Electrical measurements and composition depth profile show that the switching is not confined to a single filament but is instead uniform across the entire switching region. Despite the nonfilamentary switching, this device can retain information for over 22 hours at 190 {\deg}C, which is comparable to the best filamentary devices. We propose that this long retention arises from composition phase separation in amorphous tantalum
oxide. Our work shows that the fundamental tradeoff between information retention and switching uniformity can be overcome, and thereby provides a pathway toward more uniform and reliable
oxide memory devices.
Read the full abstract ▾
All authors Sangyong Lee, Anton V. Ievlev, Dongjae Shin, Jingxian Li, Yiyang Li
Medical Physics · Cross-listed from eess.IV
Primitive Representation Learning for Unsupervised Dynamic Contrast Enhanced MRI Reconstruction
Veronika Spieker, Wenqi Huang, Cemre Ariyurek et al. (+5 more)
Reliable quantitative analysis of dynamic contrast-enhanced MRI requires high-quality spatiotemporal reconstructions at high undersampling rates. Scan-specific reconstructions using Gaussian and Gabor primitives have shown promising results without the need for large training
datasets, but have not addressed the additional dimension of dynamic contrast. We propose a multi-dimensional, primitive based framework for dynamic contrast-enhanced MRI reconstruction that disentangles the underlying anatomy, the dynamic contrast enhancement, and residual motion into separate temporal basis functions, thereby enabling a geometrical interpretation of the representation. We show that this architecture achieves performance competitive with conventional reconstruction methods, both in reconstruction quality and in the accuracy of extracted aorta and kidney enhancement curves. The modular tier design extends naturally to additional dynamic factors and higher acceleration rates. Code available at https://github.com/compai-lab/2026-GaborDCE-spieker.
Read the full abstract ▾
All authors Veronika Spieker, Wenqi Huang, Cemre Ariyurek, Liam Timms, Daniel Rueckert, Onur Afacan, Julia A. Schnabel, Sila Kurugol
Medical Physics · Cross-listed from eess.IV
Harnessing Magnitude-Only and Complex Measurements for Improved Dynamic MRI Reconstruction with Learned Priors
Mahdi Saberi, Yaşar Utku Alçalar, Merve Gülle et al. (+2 more)
MRI reconstruction methods for undersampled k-space data naturally utilize complex-valued measurements. Parallel developments in sparse phase
retrieval have shown that magnitude-only measurements may provide complementary information for signal recovery. However, their use in MRI reconstruction remains largely unexplored, due to lack of practical settings where informative magnitude measurements can be obtained without additional scan time. In this work, we investigate the use of auxiliary k-space magnitude information for accelerated steady-state dynamic MRI reconstruction, and demonstrate strong consistency of k-space magnitudes across time-frames. Building on this observation, we propose $\mathbb{C}+\text{Mag}$, a magnitude-informed physics-driven deep learning reconstruction method. The proposed method employs an ADMM-based unrolling framework with a novel magnitude-aware data-fidelity formulation, where quadratically smoothed optimization and momentum-based updates are introduced to address the non-differentiability and non-convexity of the magnitude constraints. Experiments on retrospectively undersampled cine MRI and phase-contrast flow MRI
datasets, as well as prospectively undersampled real-time cine MRI acquisitions, demonstrate improved artifact suppression, sharper anatomical recovery, and better preservation of phase information compared to conventional PD-DL methods, which is further supported through blinded expert reader evaluations.
Read the full abstract ▾
All authors Mahdi Saberi, Yaşar Utku Alçalar, Merve Gülle, Chetan Shenoy, Mehmet Akçakaya
History and Philosophy of Physics · Cross-listed from quant-ph
Gisin's Argument and the Limits of Causal Explanations in Relativistic Spacetime
Felix J. Rutzinger
Gisin has provided an argument for the conclusion that no covariant nonlocal model can reproduce the operational statistics observed in Bell experiments. Gisin's argument applies only to deterministic models and we argue that proposed generalizations of the argument beyond determinism based solely on statistical notions are unconvincing, as they lack the resources to formulate a satisfactory notion of no-retrocausality. We introduce the framework of frame-indexed causal models in order to extend Gisin's argument to the genuinely probabilistic case. Frame-indexed causal models associate potentially different causal models with different reference frames while requiring corresponding operational variables to agree runwise. Within this framework, we establish two no-go results based on Gisin's argument. First, we show that no empirically adequate frame-indexed causal model can jointly satisfy Independent Settings, No-Retrocausality, and Causal Lorentz Invariance. Second, it is established that this result continues to hold when Causal Lorentz Invariance is weakened to Lorentz Invariance of Causal Connections, which requires only the skeleton of the causal structure to remain invariant, thereby allowing the direction of causal influence to depend on the choice of reference frame. These results are discussed in the context of relativistic interpretations of
quantum theory and nonclassical causal inference.
Read the full abstract ▾
Optics
Conservation of Pseudoangular Momentum in the Radiative Emission and Optical Excitation of Valley-Polarized Surface Lattice Resonances
Siamak Khorasani, Marc R. Bourgeois and David J. Masiello
Surface
lattice resonances (SLRs) are collective polaritonic excitations in nanoparticle arrays, with band-edge states enabling
symmetry-based control of radiation including scattering, photoluminescence, conventional and polariton lasing, and condensation. Here, we show that the integer pseudoangular momentum (PAM) of valley-polarized SLRs in parity-broken hexagonal arrays of honeycomb and kagome arrangements is directly encoded in the phase and polarization structure of their emitted electromagnetic fields. The effects of parity-
symmetry breaking on SLR PAM and the localized surface plasmon angular momentum associated with the individual nanoparticles composing the array are investigated, and an explicit link between these quantities is established. Leveraging electromagnetic reciprocity, we further propose a structured optical field possessing the same array
symmetries and integer PAM as the underlying SLRs and demonstrate theoretically and numerically that it selectively couples to valley-polarized SLRs with matching quantized PAM. Our results establish PAM-resolved light-matter interactions involving
lattice resonances as providing new opportunities for
symmetry-selective nanophotonic control,
chiral light generation, and angular-momentum-engineered metasurfaces.
Read the full abstract ▾
Computational Physics · Cross-listed from cs.LG
Composing Flow-Matching Energies with Known Physics: Generation, OOD Detection, and Inversion on PDE Fields
Yixuan Sun, Anirban Samaddar and Sandeep Madireddy
Probabilistic modeling of physical fields benefits from both a data-driven prior and known physical structure such as the governing equations. Energy-based models (EBMs) are a natural fit since energies compose additively, which enables augmenting physics information during inference. However, EBMs have been difficult to train and sample from due to the intractable partition function. We show in this work that flow matching models with a potential-induced velocity yield an explicit scalar energy at all transport times, whose gradient is exactly the converted learned score and which recovers the marginal negative log-density at the population optimum. The time-dependent energy functions are obtained purely from the matching regression objective on an independent linear Gaussian interpolation, without a variational form or additional MCMC steps, and the sampling retains the flow ODE. Access to the energy function from a trained model serves three roles: energy-corrected data generation, energy as a scoring function for out-of-distribution (OOD) detection, and energy compositional posterior sampling for inverse problems. In particular, we show the explicit energy permits general MCMC samplers in the predictor-corrector sampling framework, reducing PDE residual and spectral distance compared to the flow ODE baseline. Furthermore, we demonstrate utilizing the data energy and physics-based energy (e.g., PDE residuals) as complementary mechanisms to improve detection accuracy for OOD tasks. In addition, we explore the connection to MCMC-based inference for inverse problems by composing the energy with a quadratic observational likelihood that yields a posterior energy, used as an explicitly chosen family of inference-time targets.
Read the full abstract ▾
Atmospheric and Oceanic Physics
How Do AI Climate Models Respond to Warming Across Climate Zones?
Charlotte C. Merchant, Milan Klöwer, Bradley Stanley-Clamp et al. (+4 more)
Regional
climate zones are expected to shift under global warming. Whether AI
climate models have learned to generalize
climate-zone distributions under warming in a physically meaningful way affects their suitability for
climate projection. We address this question by applying a Köppen-Geiger
climate-zone decomposition to AIMIP Phase 1 models under prescribed +4K SST forcing and comparing their responses to physics-based AMIP models. Using this diagnostic, we compare baseline classification skill, per-zone responses in temperature, precipitation, and near-surface specific humidity, and the spatial structure of departures from physics-based models. All AI models considered reproduce the 1979-2014 ERA5 climatology within the physics-based models' range, but only the hybrid physics-AI model NeuralGCM-HRD reorganizes zones in agreement with established thermodynamic and hydrological scaling relations. The remaining emulators have distinct failure modes traceable to their architectural treatment of land
cells. A physically consistent
climate-zone response is therefore necessary for AI models intended for
climate projection.
Read the full abstract ▾
All authors Charlotte C. Merchant, Milan Klöwer, Bradley Stanley-Clamp, Maren Höver, Simon L. L. Michel, Edward Groot, Hannah M. Christensen
Atomic Physics · Cross-listed from quant-ph
High-Harmonicity Planar Penning Traps for Single-Electron Qubits
Kedar Mal, A. N. Agnihotri, Sugam Kumar et al. (+2 more)
We present a detailed account of the design choices required for planar Penning traps that feature a highly harmonic confining potential. High harmonicity is indispensable for a number of applications, particularly for confinement of single electrons as
qubits in
quantum-information processing. The present work extends previous studies [D. Goldmann and G. Gabrielse, Phys. Rev. A 81, 052335 (2010)] by a fully analytic treatment of finite electrode gaps and their relevance for small traps on the millimetre size scale and below, when relative gap sizes are non-negligible. We derive the overall trap potential with a particular focus on different models of finite-gap potentials and show how to find the optimum trap geometry and electrode voltages to minimize anharmonicities. The analytic calculations are compared with detailed finite-element simulations.
Read the full abstract ▾
All authors Kedar Mal, A. N. Agnihotri, Sugam Kumar, Wolfgang Quint, Manuel Vogel
Medical Physics
Flexible Piezoresistive Yarn Pressure Sensor for Arterial Blood Pressure Waveform Measurement
Rizal Maulana, Ádám Rák, Sándor Földi et al. (+1 more)
The arterial blood pressure (ABP) waveform contains essential information concerning the hemodynamic status and its relation to the diagnosis of cardiovascular conditions. Therefore, the continuous monitoring of the ABP waveform has become important. A measurement system that offers a non-invasive approach and high accuracy in detecting morphological details of the ABP waveform is needed. In this study, we introduce a flexible piezoresistive yarn (FPY) pressure sensor for continuous non-invasive ABP waveform measurement. The sensor was experimentally validated using two scenarios: measurements on a hardware simulator and at an arterial site. The simulator measurement aimed to assess the sensor's performance in detecting consistent waveforms, while the arterial site measurements evaluated the sensor's performance in measuring ABP waveforms corresponding to the cardiovascular cycle. The simulator measurement results showed high repeatability, reproducibility, and accuracy relative to the reference signal at the upper limit of realistic force magnitude, as measured by the correlation metric: 0.9999 $\pm$ 0.0002, 0.9994 $\pm$ 0.0002, and 0.9731 $\pm$ 0.0027, respectively. The correlation value derived from measurements at the arterial site compared with measurements using a 3D force sensor as a reference sensor was 0.9880 $\pm$ 0.0035. Based on our results, the FPY pressure sensor can be used as an innovative solution for monitoring ABP waveforms and has further potential to be developed as a diagnostic device for evaluating cardiovascular conditions.
Read the full abstract ▾
All authors Rizal Maulana, Ádám Rák, Sándor Földi, György Cserey
Physics and Society
Simplicial closure fragments the explosive cooperation transitions in higher-order public goods games
Wenjia Rao and Jianneng Huang
Hypergraph (HG) and simplicial complex (SC) are two common representations of higher-order networks, and are often expected to differ mainly quantitatively when they encode comparable group interactions. Here we show that this expectation fails in evolutionary cooperation dynamics. Using a controlled higher-order public goods game (PGG) with minimal ad hoc parameters, we compare cooperation transitions on randomized HG and SC constructed from the same triangular backbone. We find that the impact of simplicial closure is selective: when the cooperation transition is continuous-like on HG, imposing simplicial closure mainly broadens the transition without changing its qualitative nature; however, when the transition is explosive and first-order-like on HG, simplicial closure will fragment the compact low/high bistability into a broad ensemble of metastable final states. Detailed analysis reveals that this selective effect arises from the dual role of simplicial closure in cooperative-
nucleus dynamics: it promotes the survival of local cooperative
nuclei while suppressing their conversion into system-wide cascades. Further experiments confirm that this fragmentation is not tied to a specific payoff form, but is a generic feature of explosive cooperation transitions in higher-order PGGs.
Read the full abstract ▾
Geophysics · Cross-listed from math.NA
From complex-step differentiation to a general reconstruction framework
Rafael Abreu and Chahana Nagesh
The complex-step method is traditionally derived from the Taylor expansion of an analytic function and is widely used as a numerical technique for derivative approximation. We present an alternative formulation based on the Cauchy–Riemann equations and show that the classical complex-step relation arises naturally from the harmonic structure of holomorphic functions. In particular, the complex-step method admits two complementary harmonic interpretations: as a Cauchy problem, in which the derivative is identified with the normal datum of the imaginary component on the real axis, and as a reconstruction problem in a strip, in which the finite imaginary perturbation provides the upper-boundary data. The latter formulation leads explicitly to the strip Poisson and conjugate Poisson kernels and their derivatives. A related harmonic reconstruction framework in the upper half-plane leads to the Poisson, conjugate Poisson, and Cauchy kernels as elementary reconstruction operators for harmonic and holomorphic functions. Extending this reconstruction from ordinary boundary functions to finite measures yields the classical Stieltjes transform and its inversion formula. The same measure-theoretic structure appears in spectral theory, where scalar
matrix elements of the resolvent are Stieltjes transforms of the associated spectral measures. These results establish a common complex-analytic structure connecting complex-step differentiation, harmonic reconstruction, Stieltjes inversion, and spectral reconstruction, while distinguishing the boundary-value problems through which the corresponding information is recovered.
Read the full abstract ▾
Physics and Society
Computational Prosopography across a Millennium: Mathematically Oriented Lineages Traced from the Fields Medalists
Hiroyuki Chuma, Kanji Otsuka and Yoichi Sato
We reconstruct the mentor–student network through which documented scholarly training passed across roughly nine centuries, and subject both the network and the means of reconstructing it to source criticism. From Wikidata, which aggregates the Mathematics Genealogy Project and the MacTutor Archive, we extract approximately 470,000 mentor–student assertions, yielding a directed acyclic graph of 372,853 persons. Using all 64 historical Fields Medalists as a fixed, ex ante tracer set, backward traversal enumerates some 25.5 million distinct paths reaching 57 generations. Three structural observations follow. Genealogical traffic through Leibniz forms an hourglass: thin upstream, 5.3 paths per node on average, and thick downstream, 53.4, a ratio near 10:1, with no counterpart at Newton, who lies on only four of the 64 lineages. Across a window centered on Leibniz, seven independently extracted predicate dimensions reorganize together, and recorded learned-society membership rises from 6.5 to 82.1 percent of the cohort. Upstream, 54 of the 64 lineages converge on the same five twelfth- and thirteenth-century Islamic and Byzantine scholars before terminating at an eleventh-century boundary we name the Monastery Wall. We argue that such observations cannot be assessed without tool criticism. The traversal engine is algebraically reversible, so every ranking decision it makes can be reconstructed afterward. We characterize its measurement bias in closed form, show that the macro-structures survive switching that bias off, and report the family of lineages the traversal returns at different resolutions rather than a single ranked list.
Read the full abstract ▾
Biological Physics
A higher-derivative model predicts a smoothness vs duration relationship in head-pointing movements
N. Boulanger, F. Buisseret, J. Burny et al. (+4 more)
Head movements require integration of visual, proprioceptive, vestibular, and cervical motor signals. The dynamical principles governing their temporal organization remain unclear. We tested whether a Pais-Uhlenbeck oscillator – a higher-derivative model with one internal frequency parameter $\omega$ – can model head kinematics during virtual-reality-based head pointing. Our model predicts a
parabolic relationship between movement duration ($T$) and smoothness (log-dimensionless jerk, $LDLJ$) that we have experimentally checked. Sixty-two healthy young adults performed horizontal and vertical head movements with an amplitude of 30{\deg}. Movement duration, endpoint accuracy, peak angular velocity, overshoot, and $LDLJ$ were extracted from headset kinematics. A mixed-effects
parabolic regression confirmed the predicted $LDLJ$ vs $T$
parabolic relation ($R^2 = 0.851$). The quadratic coefficient was not significantly modified by direction or participant, but the intercept and linear coefficient differed between horizontal and vertical movements. As an internal consistency check of the model, we find that the parameter $\omega$, as estimated from the regressions, gives time durations that closely match the measured ones. These findings outline a nonlinear temporal organization of head pointing modulated by direction-specific biomechanical constraints, and suggest that higher-derivative mechanics may provide a principled, non-invasive framework for quantifying cervical motor planning, warranting further validation in clinical populations.
Read the full abstract ▾
All authors N. Boulanger, F. Buisseret, J. Burny, F. Dierick, W. Estievenart, A. Teregulov, O. White