Data Analysis and Statistics · Cross-listed from nlin.CD
Identifying the structure of dynamical transitions in logistic map
Aswin Balaji, Shruti Tandon, Shwetha Viswesh et al. (+3 more)
Nonlinear dynamical systems manifest rich variety of dynamical states and transitions driven by fluctuations. To understand the pattern of fluctuations during dynamical transitions, we investigate the structural features of chaos to order transition in logistic map. We determine fluctuations as amplitude jumps and encode them onto a complex network where nodes represent amplitude levels and links represent transitions between distinct amplitude bins. We discover that global network measures identify points of period doubling, regimes of periodicity and chaos, including interior crises events. Using local network measures, we also unravel novel peculiar
parabolic-shaped patterns in the orbit diagram that we show are reminiscent of the distribution of stable and unstable periodic points in the bifurcation diagram.
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All authors Aswin Balaji, Shruti Tandon, Shwetha Viswesh, Norbert Marwan, Juergen Kurths, R. I. Sujith
Fluid Dynamics
A Low-Fidelity Method for Aerofoil Shape Optimisation for Curvilinear Blade Kinematics
Benjamin Irwin, David Toal and Swathi Krishna
This study develops a low-fidelity framework for aerofoil shape optimisation under curvilinear blade kinematics, using a hovering cyclorotor as a representative case. Aerofoil optimisation can improve cyclorotor efficiency by suppressing leading-edge
vortex separation during dynamic stall, but conventional approaches rely on computationally expensive CFD-based optimisation. The proposed method uses a single-streamtube model to estimate the rotor throughflow and optimises the aerofoil camberline using separate leading- and trailing-edge criteria. Assessed across configurations with varying blade counts and chord lengths, the framework consistently identifies aerofoils that improve hover efficiency, quantified by Figure of Merit. For the baseline four-bladed configuration, the low-fidelity optimum achieves 77% of the Figure of Merit improvement obtained using high-fidelity optimisation at a fraction of the computational cost. The analysis also reveals an additional torque-minimising design family at increased chord lengths, highlighting the influence of trailing-edge loading. Aerofoil optimisation is also compared to blade-pitch kinematics optimisation, which improves the efficiency through similar control of the leading-edge
vortex separation. While both approaches produce comparable improvements in efficiency, the optimised kinematics substantially reduces thrust. Aerofoil optimisation may therefore be more practical, as maintaining a target thrust with optimised pitch kinematics would require higher rotational speeds, potentially introducing structural and noise issues.
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Atmospheric and Oceanic Physics
A Graph Neural Network Framework for Characterizing Rainfall Variability Regimes across India
Pradyumnan Raghuveeran, Gaurav Chopra, Ajay Bankar et al. (+1 more)
The Indian Summer Monsoon shows significant spatial variation. While prior work primarily focused on
forecasting rainfall amounts, little
attention has been given to how consistently a location's seasonal rainfall trajectory repeats from year to year. We introduce a graph-based machine learning framework to classify locations across India by this inter-annual consistency. Using 2001 to 2022 GSMaP ISRO data (excluding 2012), we constructed graphs for 29,026 grid points where nodes represent individual years and edges denote cosine similarity. A Graph Convolutional Network classified locations as either consistent or erratic with 96.8% accuracy. Applied to the Indian landmass, the model successfully identified the Western Ghats, Northeast India, and parts of central India as consistent regions. This classification was rigorously validated through statistical testing and temporal stability analysis, showing 93.6% agreement across two independent timeframes. Crucially, the results reveal a previously unreported coupling: regions with higher rainfall volumes are also the most temporally repeatable year-to-year, demonstrating an emergent, spatially coherent structure.
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All authors Pradyumnan Raghuveeran, Gaurav Chopra, Ajay Bankar, R. I. Sujith
Fluid Dynamics · Cross-listed from nlin.CD
Data-Driven Characterisation of Wave-Forced Turbulence Using Time-Resolved Forecast-Error Growth
Raj Jyoti Baishya, Joychen Kenglang, Andrei Velichko et al. (+1 more)
Periodic surface-wave forcing can reorganise turbulent flows through coherent spectral response, synchronization, intermittency, and changes in short-term predictability, so its dynamical effect need not vary monotonically with forcing frequency. We reanalyse laboratory acoustic Doppler velocimetry records obtained at constant discharge under four conditions (0, 0.5, 0.67, and 1~Hz) using one common KNN–GMAE forecast-error-growth protocol. A distance-weighted $k$-nearest-neighbour predictor generates out-of-sample forecasts over multiple horizons, and the slope of the early quasi-linear region of $\ln(\mathrm{GMAE})$ versus physical forecast time is reported as a finite-horizon forecast-error-growth rate, $\lFEG$. For the full 120-s records, $\lFEG$ is 5.68, 0.85, 3.39, and 4.11~s$^{-1}$ for 0, 0.5, 0.67, and 1~Hz, respectively. The ordering 0~Hz $>$ 1~Hz $>$ 0.67~Hz $>$ 0.5~Hz is preserved in all seven nearby parameter configurations, indicating that the comparative result is not an artefact of a single KNN setting. A 40-s sliding-window analysis with a 10-s step reveals substantial temporal structure. The no-wave condition remains predominantly high and the 0.5-Hz condition predominantly low, whereas the 1-Hz record has weaker local support for a single exponential-growth regime: only 3 of 9 windows satisfy the adopted early-fit criterion $\RFEG\geq0.90$, compared with 8/9, 7/9, and 7/9 for 0, 0.5, and 0.67~Hz.
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All authors Raj Jyoti Baishya, Joychen Kenglang, Andrei Velichko, Bimlesh Kumar
Optics
High-Resolution Speckle-based Single-Pixel Imaging using Silicon Photonic Non-Redundant Optical Phased Array
Keita Hirashima, Taichiro Fukui, Kento Komatsu et al. (+3 more)
Optical phased arrays (OPAs) are promising wavefront controlling devices for imaging applications due to their compact and high-speed nature. However, conventional periodic OPAs have a limited spatial resolution, which scales only linearly with the number of optical
antennas $N$. Here, we experimentally demonstrate high-resolution imaging using a non-redundant OPA (NR-OPA). Due to the non-redundant
antenna layout of NR-OPA based on the Costas array, the resolution scales quadratically with $N$. Combined with the speckle-based single-pixel imaging (SSPI) scheme, which avoids the need for precise phase calibration, we experimentally achieve a large number of resolvable imaging points exceeding 10,000 using a silicon photonic NR-OPA chip with only $N=127$ without sweeping the wavelength. The demonstrated scheme provides a promising route toward mega-pixel imaging by a compact OPA chip with reduced number of phase shifters.
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All authors Keita Hirashima, Taichiro Fukui, Kento Komatsu, Chun Ren, Yoshiaki Nakano, Takuo Tanemura
Chemical Physics
Orbital-Optimized Quasi-Variational Distinguishable Cluster Doubles Method
Fangcheng Wu and Daniel Kats
We present the orbital-optimized quasi-variational distinguishable cluster doubles (OQVDCD) method, obtained by applying the distinguishable cluster (DC) approximation to orbital-optimized quasi-variational coupled cluster doubles (OQVCCD). The resulting method retains a Hermitian-like energy functional, is size-extensive, obeys the generalized Hellmann-Feynman
theorem at a stationary point, is exact in the isolated two-electron and two-hole limits, and scales as O(o2v4), like coupled cluster singles and doubles (CCSD). For the tested closed-shell reaction energies, it is shown that OQVDCD reduces the mean absolute deviation relative to OQVCCD from 1.20 to 0.81 kcal/mol, bringing the errors close to distinguishable cluster singles and doubles (DCSD). In the strongly correlated examples considered here, OQVDCD shifts the OQVCCD energies toward the
benchmark, although the convergence remains somewhat less robust than for DCSD.
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Plasma Physics
Reinforcement learning for vertical position control on the EXL-50U spherical tokamak
Lei Xing, Huicong Ma, Changquan Yu et al. (+14 more)
Vertical position control is essential for sustaining high-performance operation in spherical tokamaks, where increased
plasma elongation introduces stringent requirements on fast and robust stabilization. This work presents an experimentally validated reinforcement-learning(RL)-based vertical position control framework for the EXL-50U spherical tokamak. A high-fidelity discharge-reconstructed simulation environment is developed by integrating physics-based
plasma-
circuit models with experimental
equilibrium information, enabling systematic controller synthesis and sim-to-real evaluation. Within this framework, RL is benchmarked in simulation against operational proportional–integral–derivative (PID) and model-based linear quadratic regulator (LQR) controllers under identical plant dynamics, actuator constraints, and measurement imperfections.Simulation results show that RL achieves tracking accuracy comparable to PID with consistently lower vertical-stabilization coil effort, while lightweight integral compensation improves robustness against residual model–plant mismatch. The RL controller is subsequently deployed on EXL-50U for closed-loop experiments. Across more than ten discharges with RL takeover, stable vertical regulation is achieved within the controlled windows. For seven representative discharges, RL maintains millimetre-scale tracking accuracy comparable to the operational PID controller (MAE typically ~ 1-5 mm) while consistently reducing actuator effort. These results demonstrate the feasibility of learning-based
plasma control on a real spherical tokamak and establish a practical pathway toward future fusion control systems.
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All authors Lei Xing, Huicong Ma, Changquan Yu, Xuanhe Wang, Jiayi Zhi, Pei Guo, Mengyao Li, Zhengyuan Chen, Yapeng Zhang, Guoyang Shi, Dongkai Qi, Xiang Gu, Siqi Ding, Yong Liu, Jianguo Chen, Tianyuan Liu, the EXL-50U Teama
Atomic Physics · Cross-listed from astro-ph.HE
Excitation and Recombination Data for Nd I-V with Applications to Kilonovae
N. Ferguson, L. P. Mulholland, M. McCann et al. (+2 more)
Neodymium (Nd), a key r-process element, significantly influences the opacity and emergent spectra of kilonovae resulting from
neutron star mergers. We present new atomic data for the first five ionization stages of Neodymium, with emphasis on recombination rate coefficients and electron-impact excitation relevant to non-local thermodynamic
equilibrium (non-LTE) modelling. Using the relativistic atomic structure code autostructure, we compute energy levels, radiative and dielectronic recombination rates using the isolated-resonance approximation, and excitation data for these ions. Excitation
datasets are constructed using both direct (distorted-wave) and resonant excitation approaches, allowing a systematic assessment of the impact of resonance contributions on effective collision strengths. For the case of Nd ii, we compare collisional strengths with those produced by the Dirac Atomic R-
matrix Codes (darc). These comparisons demonstrate the importance of resonance-mediated excitation, especially for low-charge ions where near-threshold resonances significantly enhance rates. In terms of recombination for higher ionization stages, dielectronic recombination (DR) is found to dominate over a wide temperature range, while radiative recombination (RR) is comparable with DR at low temperatures for near-neutral species. Our results highlight the pivotal role of non-LTE atomic physics in interpreting kilonovae observations and constraining nucleosynthesis yields. This work strengthens the atomic foundation necessary for linking observed electromagnetic signals to the physics of compact object mergers and heavy element formation.
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All authors N. Ferguson, L. P. Mulholland, M. McCann, C. P. Ballance, M. G. O'Mullane
Applied Physics · Cross-listed from cond-mat.mes-hall
Towards Ultra Scalability of Non-Volatile Magnetic Tunnel Junctions with a 3D Storage Layer
Nuno Caçoilo, Shunsuke Fukami, Olivier Fruchart et al. (+1 more)
The perpendicular
Spin Transfer Torque
Magnetic Random Access
Memory is one of the most promising emerging non-volatile
memory technologies, based on ultra-thin
magnetic tunnel junctions. However, as these devices are limited by their thermal stability factor at technological nodes smaller than 20 nm, their scalability is compromised. A possible solution to this limitation relies on taking advantage of the shape anisotropy, by increasing substantially the thickness of the storage layer. Thanks to the combination of a vertical aspect-ratio and enhanced volume, high thermal stability can be maintained at sub-10 nm nodes. Here, we present the technological advancements and understanding of the magnetisation reversal that led to faster switching speeds at reduced switching voltage, providing a viable approach for dense arrays of ultra-small
magnetic tunnel junctions.
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All authors Nuno Caçoilo, Shunsuke Fukami, Olivier Fruchart, Ioan-Lucian Prejbeanu
Plasma Physics
Advantage-level Aggregation Reinforcement Learning for X-point Target Magnetic Configuration Control in an EXL-50U Experiment-Calibrated Simulation Environment
Siqi Ding, Xuanhe Wang, Pei Guo et al. (+10 more)
Managing divertor heat loads is a central challenge for compact, high-power tokamaks. To increase local flux expansion and decouple the dissipation volume from the core, EHL-2 adopts the X-point target (XPT) divertor. This requires the secondary X-point to remain on the divertor leg; displacement degrades the
topology and exhaust geometry. Current experiments, including EXL-50U discharges, rely on precomputed feedforward waveforms with PID loops on global quantities. Lacking dedicated closed-loop feedback for the secondary null, XPT operation is repeatable but not routine. We formulate XPT feedback as a multi-objective reinforcement learning (RL) control problem in a free-boundary environment calibrated to EXL-50U discharge #13906. To address strong coupling among
plasma current, shape, and null constraints - where reward scalarisation collapses objective-specific temporal credit - we develop Advantage Aggregation (AdvA). AdvA preserves objective-wise temporal credit before worst-objective-aware nonlinear scalarisation and introduces a residual correction to policy updates. AdvA-PPO is evaluated against Reward-PPO and a feedforward-plus-PID baseline under nominal operation, measurement uncertainties, and unseen initial
equilibria. On a 500 ms rollout, AdvA-PPO raises the mean worst-channel score from 0.23 to 0.81 over Reward-PPO, reducing X-point flux RMSE by ~20x. Under combined measurement uncertainties, it is the only learned controller completing the horizon while retaining a usable XPT shape. Multi-initialization fine-tuning enables a single AdvA-PPO policy to complete full-horizon operation across divertor and limiter initial
equilibria. These results provide a simulation-based foundation for future real-time XPT validation on EXL-50U.
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All authors Siqi Ding, Xuanhe Wang, Pei Guo, Guoyang Shi, Changquan Yu, Yiting Wang, Xianming Song, Xiang Gu, Zhengyuan Chen, Lei Xing, Yapeng Zhang, Jianguo Chen, Tianyuan Liu
Physics and Society · Cross-listed from cs.DL
The Legibility Gap: How Gender Equity Interventions Redistribute Recognition Across Cultures
Binglu Wang, Jose Cervantez, Jiahui Xue et al. (+2 more)
Efforts to promote gender equity in science increasingly rely on name-based inference to quantify representation and guide policy and behavior. Yet linguistic cues that signal gender vary across cultures and are often obscured when names are transliterated into English. Here we identify a pattern we call the "legibility gap": when gender is inferred from names, equity interventions systematically benefit women whose names signal gender while bypassing those whose names lose such cues in translation. Using both observational and experimental evidence, we show how this gap reshapes recognition in science. Analyzing citation diversity statements-an emerging practice in which authors report the algorithmically estimated gender composition of their reference lists-we find that papers that include this practice cite women more frequently, but the gains accrue almost entirely to authors with gender-signaling Western names. By contrast, women whose names lose gender cues in English transliteration, predominantly those with East Asian names, receive fewer citations in these same papers. Two preregistered experiments (N = 2,250) corroborate this pattern and identify its mechanism: linguistic legibility, not cultural unfamiliarity, determines who is recognized as a woman and who benefits from policies designed to support women in science. Overall, these findings expose a previously unrecognized layer of inequity embedded in global equity infrastructures. As science becomes increasingly global and equity efforts increasingly algorithmic, the legibility gap reveals how uneven identity recognition reshapes
fairness. In global systems of recognition, equity depends not only on whether policies are effective on average, but also on whether they are equitable across cultures.
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All authors Binglu Wang, Jose Cervantez, Jiahui Xue, Katherine L. Milkman, Dashun Wang
Atomic Physics
Attosecond Transient Polarimetry
Nicola Mayer, Kylie Gannan, Stephen R Leone et al. (+1 more)
We introduce elliptical-dichroic attosecond transient absorption spectroscopy (eDATAS), an ultrafast attosecond spectroscopy technique in which an atomic gas is probed by elliptically polarized attosecond and near-infrared pulses with an arbitrary angle between the polarization ellipses and a varying time-delay. Using perturbation theory, we show that the transient absorption signal factorizes into form factors whose dependence on pulse ellipticities and polarization-ellipse orientations are governed entirely by dipole selection rules: ellipticity sets the amplitudes of the interfering multiphoton pathways, while the relative orientations of the ellipses control the phase. We reinterpret circular- and linear-dichroic attosecond transient absorption spectroscopy as limiting cases of eDATAS, and validate the theory through an eDATAS measurement in helium, finding excellent agreement. Finally, we demonstrate that eDATAS can also serve as an in situ measurement of the polarization state of the XUV field, avoiding the need for a dedicated XUV polarimeter.
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All authors Nicola Mayer, Kylie Gannan, Stephen R Leone, Lauren B Drescher
Physics and Society
Making Sense of Quantum Technology: Nuance and Comparison in Non-Expert Stakeholder Communication
Floris Löffler, Lisanne van Veenen, Muhammad Unggul Karami et al. (+1 more)
The development of
quantum technology (QT) is partly driven by a growing group of non-expert stakeholders, such as policymakers, journalists, and philosophers. Although many of these stakeholders have minimal formal training in
quantum science, their communications may greatly influence the QT ecosystem, yet research into it is scarce. We therefore explored the use of framing, metaphors, and jargon by non-expert QT stakeholders through a qualitative content analysis of 12 semi-structured interviews. We found that the stakeholders applied a nuanced lens to QT, typically through socioeconomic framing. Several types of framing previously unidentified in the literature were found, such as explaining QT by comparison with current (emerging) technologies, for example, artificial Intelligence, in terms of capabilities and implementation in society. The use of stakeholder desiderata in such research was also found to be subsidiary to the stakeholder's occupation. This explorative study is meant to serve as a starting point for future studies of this stakeholder group.
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All authors Floris Löffler, Lisanne van Veenen, Muhammad Unggul Karami, Julia Cramer
Plasma Physics
Two-fluid effects on the nonlinear dynamics of RFP relaxation
Wentan Yan, Ping Zhu, Hong Li et al. (+2 more)
This study investigates the role of two-fluid effects during
magnetic relaxation in reversed-field pinch (RFP)
plasmas. Within the multiple-helicity (MH) regime, two-fluid simulations produce distinct sawtooth oscillations, in contrast to the sawtooth-free state obtained in single-fluid simulations. Analysis of the
magnetic field aligned projection of Faraday's law reveals that, tearing modes collectively generate a dynamo electric field that sustains the
magnetic relaxation, a process analogous to the flux-pumping in tokamaks. Despite the reduced linear tearing-mode growth rates, stronger two-fluid effects produce more pronounced sawtooth activity over the parameter range considered. Modal energy analysis shows that
Hall-mediated nonlinear energy redistribution disrupts the coherent tearing-mode dynamics required to sustain steady flux-pumping, thereby facilitating intermittent reconnection. This transition is interpreted as a
Hall-mediated dynamical bifurcation between steady flux-pumping and quasi-periodic sawtooth relaxation.
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All authors Wentan Yan, Ping Zhu, Hong Li, Wandong Liu, Bing Luo
Biological Physics
Amphibian water to land transition reveals physical limits of olfaction
Martin James, Loranzie S. Rogers, Moreira Salsman et al. (+3 more)
The evolutionary transition from water to land required animals to sense chemicals under fundamentally different physical constraints. Because chemicals diffuse orders of magnitude more slowly in water than in air, aquatic olfaction faces significant challenges in odor capture. Yet, aquatic organisms exhibit strikingly sensitive and rapid chemosensory behaviors. Amphibians represent a powerful model to address this discrepancy because they transition from aquatic to terrestrial environments within a single lifetime. Here, we combine metamorphosis of adult axolotls with asymptotic theory and numerical simulations to show that odorants are captured by
diffusion in tens of seconds in water compared with milliseconds in air; yet aquatic axolotls respond to odorants on a much more rapid timescale. Despite inefficiency, we find that proximity to large patches of highly concentrated odor supports sensation in the presence of flow. While aquatic axolotls did not display any respiratory flow, they display behavioral adaptations that generate flow and may even draw in odor through ciliary pumping. Thus, aquatic olfaction is slower and more wasteful than terrestrial olfaction, but behavioral adaptations support olfaction as an effective proximal sense.
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All authors Martin James, Loranzie S. Rogers, Moreira Salsman, Francesco Viola, Nicholas W. Bellono, Agnese Seminara
Optics
Differentiable high-fidelity vectorial propagator
Dohyun Kim and Jonghwa Shin
The numerical accuracy and computational efficiency of free-space field propagation directly determine what physics can be predicted and what devices can be designed, from nanophotonic metasurfaces to optically addressed
quantum processors. Existing methods face a fundamental accuracy-speed trade-off, which is further compounded in the vectorial regime, where major formalisms remain fragmented with no unified computational framework. Here we address both challenges: a theory that unifies the vectorial diffraction framework, and an
algorithm that resolves the accuracy-speed trade-off, each reinforcing the other. We prove that all major vectorial diffraction formalisms and vector-potential seeding methods arise from a single surface equivalence principle and reduce, without approximation, to a finite sequence of scalar angular-spectrum operations. Any advance in scalar propagation can therefore be exploited across the entire vectorial ecosystem. The error-compensating angular spectrum method (E-ASM) serves as a fast and accurate scalar solver, delivering peak signal-to-noise-ratio gains exceeding 60 dB over the state-of-the-art band-extended angular spectrum method at order-of-magnitude faster speed, with a freely configurable observation window. Their combination, implemented in a GPU-accelerated, fully differentiable form, turns rigorous vectorial field computation, previously prohibitive for higher-order beams over macroscopic distances, into a practical, inverse-design-ready tool. We demonstrate the framework on optically addressed local
qubit gates and on vectorial Hermite-Gaussian and Laguerre-Gaussian beam propagation, resolving field structures inaccessible to scalar or approximate vectorial methods.
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Chemical Physics
Accurate and Transferable Intermolecular Potential Based on Machine-Learned Molecular Electron Density
Dahvyd Wing, Mihail Bogojeski, Szabolcs Goger et al. (+2 more)
Machine-learned force fields (MLFFs) contain many learnable parameters and therefore require large training
datasets. This poses a challenge for developing highly accurate, general-purpose MLFFs because generating high-quality ab initio reference data is computationally expensive. Classical empirical potentials offer a potentially inexpensive source of synthetic training data, but existing models often lack the accuracy needed to provide useful reference energies. Here, we introduce the density-based intermolecular potential (DensIP), a physics-based model of intermolecular interactions that uses machine-learned electron densities and only four universal parameters. We train and test DensIP on CCSD(T)/CBS interaction energies from DES15K, a
dataset of dimers of small organic molecules. DensIP achieves sub-kcal/mol errors for dimers containing molecules absent from the training set, including molecules in non-
equilibrium conformations, demonstrating strong transferability. We further show that DensIP can be applied to molecules as large as drug ligands. Notably, DensIP outperforms state-of-the-art general-purpose MLFFs for long-range interactions, making it a promising approach for generating accurate synthetic training data at scale.
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All authors Dahvyd Wing, Mihail Bogojeski, Szabolcs Goger, Klaus-Robert Müller, Alexandre Tkatchenko
Optics
Spectral Anisotropy in Transition Radiation from Biaxial Media
Sven Ebel, Bibi Mary Francis, Min Seok Jang et al. (+4 more)
In anisotropic optical media, the electromagnetic response depends on the orientation of the optical field relative to the material's principal dielectric axes. While this direction dependence is well understood in conventional optics, it should also influence light-generation processes driven by free electrons. Here, we experimentally observe spectrally anisotropic transition radiation from biaxial van der Waals
crystals. Using cathodoluminescence spectroscopy on germanium sulphide (GeS) and molybdenum oxydichloride (MoOCl$_2$)
crystals, we show that the transition-radiation spectra differ along the principal in-plane optical axes. To describe this spectral anisotropy, we develop a thin-film transition-radiation model that reproduces the experimental observations. Our results demonstrate that transition radiation is a sensitive probe of the axis-dependent dielectric response of biaxial optical media and suggest that optical anisotropy can provide an additional degree of freedom for free electron-driven spectroscopy, radiation sources, and transition-radiation-based diagnostics.
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All authors Sven Ebel, Bibi Mary Francis, Min Seok Jang, Brian D. Gerardot, N. Asger Mortensen, Mauro Brotons-Gisbert, Sergii Morozov
Physics Education
Using Gemini and LuaLaTeX to transcribe physics videos into PDF/UA-2 and ISO 32005 math-accessible PDFs
Craig W. Looney and Christopher L. Duston
We present an AI transcription workflow that uses freely available tools to transcribe instructional physics
videos with substantial mathematical content into math-accessible PDFs. Guided by our carefully crafted system instructions, Google Gemini transcribes linked or uploaded
videos into LaTeX output. Importantly, all board-written or screen-presented equations are transcribed into LaTeX math and incorporated into the verbal flow or presented in descriptive segments, which also describe graphs, diagrams, and pedagogically important actions and interactions. Gemini's LaTeX output is appended to a prewritten SE tagging-enabled preamble and compiled by a LuaLaTeX engine; the resulting PDF transcripts routinely compile without errors and pass PDF/UA-2 and ISO 32005 validation tests. We provide a balanced performance assessment using numerous examples from test transcriptions. The publicly available supplementary resources contain complete transcripts corresponding to all presented examples, and also contain our well-tested system instructions and preamble so that others can use, adapt, study, and further develop our transcription framework.
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Biological Physics
Thermal scaling laws for open-water swimming
Henry van den Bedem and Ellen Kuhl
Open-water swimming defines a thermal phase-boundary problem in which metabolic heat production competes with environmental heat loss. We derive a scaling law that predicts the critical water temperature and shows how body size, swim pace, and insulation shift this boundary. Longitudinal warm- and cold-water data reveal transient dynamics that exceed single-compartment predictions, but emerge naturally from core–peripheral physiology. Together, our results suggest that thermal safety depends on swimmer-specific characteristics and swimming conditions, not on water temperature alone.
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Physics and Society
Message Order Selects Opposite Collective Opinions on Laplacian-Cospectral Networks
Ruiwu Niu, Xincheng Shu and Ying Zhao
Receiving the same messages in different orders can change an
agent's judgement. We construct a network model in which this local sequence effect determines the final opinion of an entire network. Each
agent has a two-level
quantum-like state with an expressed opinion and an auxiliary context coordinate that can affect the next update. Two valid update rules of equal strength leave the neutral state unchanged and add no positive or negative opinion bias. Nevertheless, reversing their order gives the exact opinions +0.01 and -0.01 from the same initial state. We place four ordered pairs so that their positive and negative contributions sum to zero but occupy different labelled nodes. After this single input, all cases follow the same nonlinear network rule, which preserves valid states and admits two stable consensuses. We compare two 16-node networks whose complete
Laplacian spectra are identical. With every local ingredient fixed, they converge to opposite consensuses. A separate local calculation identifies a specific overlap between the labelled pulse and
Laplacian eigenspace projectors that sets the leading displacement of the basin boundary. Rigorous interval arithmetic proves the finite registered example independently. The construction connects message order to collective selection and shows why
Laplacian eigenvalues alone do not determine which opinion wins.
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Optics · Cross-listed from cs.AI
VortexChat: An agentic framework for autonomous multi-objective integrated photonic design
Faqian Chong, Yulun Wu, Shilong Li et al. (+3 more)
The advancement of modern integrated photonics is frequently bottlenecked by device design workflows that rely heavily on manual simulation and expert intuition. While inverse design offers an alternative, it remains constrained by expert supervision and a lack of end-to-end automation. To address these issues, we present VortexChat, an agentic framework for the autonomous, end-to-end inverse design of integrated photonic devices directly from natural
language specifications. VortexChat couples a large
language model (LLM) decision
agent with
topology generation, gradient-based refinement, and full-wave electromagnetic simulation. This closed-loop architecture enables the system to iteratively decompose design objectives, orchestrate computational tools, and update strategies based on feedback with minimal human intervention. Constrained by the absolute metrics of the
Vortex100
Benchmark, VortexChat autonomously generates devices that strictly meet all predefined performance thresholds without any human-in-the-loop. As an experimental demonstration, we fabricated a broadband terahertz perfect
vortex beam multiplexer, autonomously designed by VortexChat, with measurements confirming high-efficiency operation, high mode purity and low inter-channel crosstalk in agreement with full-wave simulations. These results demonstrate that an LLM
agent can assume key aspects of expert decision-making in photonic inverse design while maintaining physical fidelity and fabrication feasibility, providing a scalable route towards autonomous design of complex integrated photonic systems.
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All authors Faqian Chong, Yulun Wu, Shilong Li, Andrew Forbes, Hongsheng Chen, Song Han
Plasma Physics
Power-law-anchored residual learning for H-mode energy confinement time in tokamaks: interpolation and parameter-defined extrapolation
Zhaokun Wang, Tianyuan Liu, Jianguo Chen et al. (+4 more)
Reliable prediction of the energy confinement time is essential for
magnetic-confinement fusion. Conventional power-law scalings provide constrained extrapolation trends but cannot represent complex nonlinearities, whereas neural networks interpolate accurately but may behave unpredictably outside the training distribution. We propose a unified power-law-anchored residual-learning framework in which a frozen empirical power-law scaling supplies the global trend and a nonlinear model learns only the systematic residual in logarithmic space. PLR-KAN is developed as the primary implementation, while a parameter-matched PLR-MLP serves as a controlled architecture replacement. Using the ITPA DB5.2.3 H-mode confinement database, we evaluate interpolation and parameter-defined held-out cohorts over ten complete training pipelines. PLR-KAN retains near-best interpolation accuracy, achieving R2=0.9671+/-0.0027, while substantially improving the stability of direct KAN under parameter-defined distribution shifts. It outperforms direct KAN across all five non-epsilon single-parameter-defined cohorts and the core-five joint cohort, reaching R2=0.9263+/-0.0157 in the latter. Results from PLR-MLP further demonstrate that the benefit of power-law anchoring is not specific to KAN, although the effectiveness of residual transfer remains architecture and direction dependent. As an exploratory extension, a Mahalanobis-distance-based prediction-time gate improves stability in selected shifted regions but is not universally beneficial and cannot compensate for missing device or physics-regime coverage. Overall, power-law-anchored residual learning provides a practical balance between nonlinear interpolation capability and empirically constrained extrapolation behavior.
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All authors Zhaokun Wang, Tianyuan Liu, Jianguo Chen, Guoyang Shi, Siqi Ding, Yuejiang Shi, Xianmei Zhang
Chemical Physics
Symmetry Adapted Hierarchical Equations of Motion for Exact Simulations of Large Polariton Systems
M. Elious Mondal and Pengfei Huo
Hierarchical equations of motion(HEOM) provide exact dynamics of open
quantum systems coupled to harmonic baths, but their computational cost becomes prohibitive for systems with many independent local environments. In this work, we develop a
symmetry-adapted HEOM formalism to significantly reduce the computational cost for the permutationally invariant Holstein-Tavis-Cummings (HTC) model. The method removes redundant information in two stages. First, all auxiliary density operators (ADOs) related only by relabeling identical molecules and their bath channels are replaced by a single canonical occupation-pattern representative. Second, molecules with the same local hierarchy occupation produce repeated
matrix elements within each representative, allowing only the distinct complex variables to be propagated instead of the full (N + 1) $\times$ (N + 1) ADO
matrices. The resulting
matrix-free equations are evaluated using precomputed connections and molecular multiplicities. At fixed hierarchy depth L and number of bath correlation exponentials m, the number of canonical representatives becomes independent of the ensemble size for N $\geq$ L and the number of unique variables saturates for N $\geq$ L + 2. The formulation easily extends to multiple-exponential bath decompositions, arbitrary initial density operators, static disorders, and cavity loss. Our
benchmarks reproduce conventional HEOM dynamics while requiring far fewer propagated variables and substantially less
memory.
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Plasma Physics
Zero-dimensional multi-physics-constrained parameter design and optimization for advanced quasi-isodynamic stellarators
Ziyuan Sun, Zixuan Guo, Xianglin Hao et al. (+3 more)
A zero-dimensional (0D) multi-physics-constrained framework for parameter design and optimization of Stable Quasi-Isodynamic Designs (SQuIDs) is presented. Single- and multi-objective optimizations for three staged devices are carried out using an in-house stellarator 0D systems code: YF-1 for discharge demonstration, YF-2 for scientific break even, and YF-3 for a commercial demonstration plant. Pareto searches map the main design trade-offs across the three generations. The equal weight optima for YF-2 and YF-3 both lie in the electron-root favorable regime of the adopted root proxy: YF-2 recovers $Q_{phys} \sim 1$, while YF-3 reaches an ignited point at reactor scale. Future work will couple engineering feasibility and economic assessment modules for integrated plant evaluation.
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All authors Ziyuan Sun, Zixuan Guo, Xianglin Hao, Longjun Qin, Qian Liu, Xiang Teng
Optics
Gas Beam Dump and Power Meter for High Energy Lasers
Harsha Rajesh, Sida Cao, Ke Ou et al. (+3 more)
Gaseous optics exhibit substantially higher
laser-induced-damage thresholds than solid materials, enabling high-intensity operation without permanent degradation. In this Letter, we describe a gas-based beam dump and power meter that exploits the strong absorption of ozone in the Hartley band (200-320 nm). We develop and experimentally validate a model for ozone absorption that predicts the propagation length required for complete energy deposition and the resulting gas temperature rise. By calibrating the temperature rise against incident pulse energy, we demonstrate accurate millijoule-scale pulse energy measurements with an all-gas absorber. These results establish gas-phase absorbers as a scalable, debris-free platform for high-damage-threshold beam dumps and energy diagnostics for high-fluence
lasers.
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All authors Harsha Rajesh, Sida Cao, Ke Ou, Devdigvijay Singh, Pierre Michel, Matthew R. Edwards