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Physics Physics

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Friday 8 May

Optics

Scalable Liquid-Crystal Integrated Silicon Nitride Photonic Circuits for Reconfigurable Quantum Interference

Chunghyun Ahn, Yongjin Hwang, Sangbaek Lee et al. (+7 more)

Integrated quantum photonics requires compact, efficient, and low-power phase modulators. While silicon nitride (SiN) is a promising platform, existing modulators suffer from high power consumption, thermal crosstalk, or high driving voltages. Liquid crystal (LC) offers a compelling alternative because of the large index changes and industrial maturity. However, their suitability for supporting various applications in the photonic quantum system has not been experimentally confirmed.Here, we report the first experimental demonstration that LC-based phase modulators integrated on a SiN platform show highly visible quantum interference. We fabricated a liquid-crystal integrated Mach-Zehnder interferometer (LC-MZI) that achieved CMOS-compatible performance with V_pi * L < 1 V-mm. In two-photon interference experiments, the devices exhibited high-visibility quantum interference (~98.5%) with voltage-tunable phase control. Furthermore, we validated the scalability of our approach by demonstrating wafer-scale fabrication using stepper lithography. This work establishes LC-integrated SiN photonics as a scalable, reconfigurable, and energy-efficient platform for quantum photonic circuits.
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All authors Chunghyun Ahn, Yongjin Hwang, Sangbaek Lee, Jinil Lee, Hyunjin Ko, Sunghyun Moon, Hojoong Jung, Hyun-Yong Yu, Se-Um Kim, Hyounghan Kwon

Applied Physics · Cross-listed from cond-mat.dis-nn

A Hardware-aware Hopfield Network with a Nonlinear Memristor Array for Robust Associative Memory with Superlinear Capacity

Younghyun Lee, Hakseung Rhee, Unhyeon Kang et al. (+9 more)

Associative memory retrieves complete patterns from partial or corrupted inputs and constitutes a primitive form of generative inference. Classical Hopfield networks (CHN) provide a canonical framework for associative memory but suffer from limited memory capacity. Recently, modern Hopfield networks (MHN) were introduced to achieve higher capacity by using explicit pattern-wise storage and neurons with the softmax activation function, which makes the MHN vulnerable to noise and the hardware implementation complicated due to its network size varying with the number of stored patterns. Here, we introduce a hardware-aware Hopfield network (HHN), in which the intrinsic nonlinear current-voltage characteristics of a charge-trap memristor are leveraged to engineer the energy landscape of the HN, increasing the memory capacity. Using a 25 x 25 nonlinear memristor array, we demonstrate reliable reconstruction of corrupted patterns with memory capacity far exceeding the classical limit (K ~ 0.14N, where N is the number of neurons). The HHN preserves Hopfield-type energy-minimization dynamics and remains robust to synaptic conductance noise. Large-scale simulations on high-dimensional image data reveal an empirical memory capacity scaling of K ~ 0.3 x N^1.2 under a fixed synaptic budget. These results establish HHN as a scalable hardware-native architecture for low-power associative memory and generative inference.
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All authors Younghyun Lee, Hakseung Rhee, Unhyeon Kang, Seungmin Oh, Kyungmin Lee, Hyun Jae Jang, Seongsik Park, YeonJoo Jeong, Inho Kim, Jong Keuk Park, Kyung Min Kim, Suyoun Lee

Optics

Perturbation-based Compensation with EEPN-free Phase Recovery as Back Propagation

Chuang Xu and Alan Pak Tao Lau

We propose a feed-forward perturbation-based method that uses the noisy received signal to compensate for nonlinear distortion, which outperforms the conventional decision-based method and avoids decision feedback. Additionally, combining it with the EEPN-free carrier phase recovery shows additional gain due to a fully symmetrical propagation-backpropagation structure.
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Geophysics · Cross-listed from gr-qc

Formulation of testing gravitational redshift based on Laser Time link between China Space Station and a ground station

Rui Xu, Wenbin Shen, Hok Sum Fok et al. (+11 more)

This paper presents a high-precision gravitational redshift test using the China Space Station (CSS) Laser Time Transfer (CLT) system. We develop a comprehensive observation equation based on a c^{-3} order relativistic model for space-ground clock comparison. While the CSS optical clock system is currently in the orbital debugging phase, our simulation using actual CSS orbit data achieves a gravitational redshift verification precision of (1.8 \pm 47)*10^{-7} – approximately one order of magnitude improvement over previous experiments. Our work represents the first application of laser-based time transfer for gravitational redshift verification at such precision, and the first use of the CSS CLT link for testing this fundamental aspect of General Relativity. Unlike microwave-based methods, our laser approach avoids ionospheric effects and first-order Doppler shifts. Residual analysis identifies tropospheric delay variations and atmospheric turbulence as the primary remaining uncertainty contributors. The achieved precision enables gravitational potential difference measurements with 0.1 m^2/s^2 precision – offering new capabilities for both fundamental physics investigations and geodetic applications including intercontinental height transfer. This work establishes a new benchmark for high-precision tests of relativistic physics and demonstrates the transformative potential of space-based optical time transfer.
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All authors Rui Xu, Wenbin Shen, Hok Sum Fok, Pengfei Zhang, Lihong Li, Lei Wang, Kuangchao Wu, An Ning, Youchao Xie, Ziyu Shen, Lingxuan Wang, Yongqi Zhao, Kai Liu, Yuanjin Pan

Atomic Physics

State-resolved electron capture in low-energy Ar2+-Ar/N2 collisions

Shucheng Cui, Dadi Xing, Xiaolong Zhu et al. (+6 more)

As a fundamental process in atomic physics, charge exchange relies on quantum state-resolved data that is crucial for various fields such as astrophysics and plasma physics. However, there remains a g in the research on multi-electron target systems. This study aims to investigate the dynamic mechanisms of single/double electron capture in collisions between Ar2+ ions and Ar atoms or N2 molecules at an energy of 40 keV, thereby supplementing high-precision experimental data in this field. The experiment is conducted on the electron beam ion source (EBIS) platform at the Institute of Modern Physics, Chinese Academy of Sciences, using the cold target recoil ion momentum spectroscopy (COLTRIMS) technique. An ion beam containing ground-state Ar2+ (3s^2 3p^(4 3) P) and metastable Ar2+ (3s^2 3p^(4 1) D,(_^1)S) is used as the projectile, colliding with a supersonic Ar/ N2 mixed gas target. Three-dimensional momentum of recoil ions is reconstructed through coincidence measurements of recoil ions and scattered ions, and the Q-value and scattering angle distribution are calculated. Theoretical comparisons are performed using the molecular Coulombic over barrier model (MCBM).
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All authors Shucheng Cui, Dadi Xing, Xiaolong Zhu, Dongmei Zhao, Dalong Guo, Yong Gao, Shaofeng Zhang, Chenzhong Dong, Xinwen Ma

Atmospheric and Oceanic Physics

GPROF-IR: An Improved Single-Channel Infrared Precipitation Retrieval for Merged Satellite Precipitation Products

Simon Pfreundschuh, Christian D. Kummerow, Jackson Tan et al. (+1 more)

The Integrated Multi-Satellite Retrievals for the Global Precipitation Measurement (IMERG) product combines precipitation estimates from passive microwave (PMW) and infrared (IR) sensors to provide quasi-global precipitation estimates at half-hourly resolution. Because IR observations are primarily sensitive to cloud-top properties, however, IR-based precipitation estimates are generally less accurate than PMW estimates and can introduce artifacts into the merged precipitation fields. We introduce GPROF-IR, a neural-network-based precipitation retrieval designed to provide precipitation estimates from single-channel geostationary IR observations for the upcoming IMERG V08. GPROF-IR uses a convolutional neural network to exploit spatiotemporal information from sequences of half-hourly IR observations. We validate GPROF-IR against independent surface-based precipitation measurements over land and ocean. GPROF-IR substantially improves upon existing single-channel IR retrievals, including the IR retrieval used in IMERG V07 and more recent neural-network-based approaches. During overpasses of flagship PMW sensors, GPROF-IR is more accurate than the corresponding IMERG PMW estimates over CONUS and Austria, but not Korea. Over CONUS, GPROF-IR outperforms the merged IMERG V07 product for hourly and daily precipitation accumulations. Against shipborne disdrometer observations, GPROF-IR is more accurate than IMERG V07 in the tropics but slightly less accurate in the extratropics. Since geostationary IR observations are continuously available across much of the globe since 1983, GPROF-IR provides a promising basis for improving merged precipitation products and developing long-term, temporally consistent precipitation records.
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All authors Simon Pfreundschuh, Christian D. Kummerow, Jackson Tan, George J. Huffman

Fluid Dynamics

A fast Physics-Informed Neural Networks based approach to the 2D design of turbine blades

Yuan Huang and Francesca di Mare

Rapid aerodynamic screening of turbomachinery blades across wide operating envelopes remains a major computational bottleneck in preliminary design, particularly for energy-conversion and storage systems such as emerging Carnot batteries. Physics-informed neural networks (PINNs) offer a mesh-free alternative to conventional CFD, yet convergence and accuracy often deteriorate for complex blade geometries and off-design flows. We propose a progressive Euler-PINN framework that (i) gradually relaxes boundary conditions from tunnel flow without a blade to full outlet static pressure, and (ii) employs a geometry-aware dynamic loss-weighting scheme that intensifies residual penalties near highly curved boundaries. To the best of our knowledge, this is the first study to deploy a single PINN workflow for large-scale, engineering-grade screening of turbomachinery blade families across multiple operating conditions, covering ten NACA6 variants and 30 subsonic operating points. The proposed framework achieves CFD-comparable accuracy for pressure and velocity fields while reducing the computational cost required for family-wide blade screening. These results establish the method as a practical surrogate for two-dimensional turbomachinery blade pre-design and optimisation.
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Biological Physics

Cellular-scale mechanism of cell crawling responding to substrate stiffness

Sohei Nakamura and Mitsusuke Tarama

Biological cells are able to adapt their behaviour in response to environmental cues. Durotaxis is a phenomenon in which cells adjust their migration depending on the mechanical properties of a surrounding substrate. Although durotaxis has been studied more than two decades, basic cellular-scale mechanism of how cells regulate the motility responding to substrate stiffness remains to be elucidated. We address this issue by developing a theory utilising a mechanochemical model that integrates intracellular biochemical reactions with cellular deformation and substrate adhesion. Numerical analysis reveals that the characteristic speed and diffusion constant of cells change non-monotonically with respect to substrate stiffness, indicating the emergence of an optimal stiffness for migration. In addition, by introducing a memory effect that allows feedback from cell mechanics to the intracellular chemical reactions, the persistence time increases with substrate stiffness on a substrate softer than the optimal. We further investigate theoretically the origin of the non-monotonic dependence, that is comparable to the experimental observations, in terms of cell deformation and symmetry breaking in substrate adhesion. We believe that our study provides a unifying framework to understand complex durotactic cell migration.
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Computational Physics · Cross-listed from cs.LG

CarCrashNet: A Large-Scale Dataset and Hierarchical Neural Solver for Data-Driven Structural Crash Simulation

Mohamed Elrefaie, Dule Shu, Matthew Klenk et al. (+1 more)

Crash simulation is a cornerstone of modern vehicle development because it reduces the need for costly physical prototypes, accelerates safety-driven design iteration, and increasingly supports virtual testing workflows. At the same time, modeling structural crash mechanics remains exceptionally challenging: the response is governed by nonlinear contact, large deformation, material plasticity, failure, and complex multi-body interactions evolving over space and time on high-resolution finite-element meshes. In this work, we introduce CarCrashNet, a public high-fidelity open-source benchmark for data-driven structural crash simulation. CarCrashNet combines component-scale and full-vehicle simulations in a multi-modal format, including more than 14,000 bumper-beam pole-impact simulations with varying geometry, materials, and boundary conditions, together with 825 full-vehicle crash simulations built from three industry-standard vehicle models of increasing structural complexity: Dodge Neon, Toyota Yaris, and Chevrolet Silverado. To establish the reliability of the benchmark, we validate our open-source finite-element workflow based on OpenRadioss against both experimental crash data and the commercial solver Ansys LS-DYNA. We also introduce CrashSolver, a machine-learning model designed for full-vehicle crash prediction from high-resolution finite-element crash data. We further perform extensive benchmarking across the released datasets and evaluate CrashSolver against state-of-the-art geometric deep learning and transformer-based neural solvers. Our results position CarCrashNet as a foundation for reproducible research in structural simulation, crashworthiness modeling, and AI-driven virtual crash testing. The dataset is available at https://github.com/Mohamedelrefaie/CarCrashNet.
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All authors Mohamed Elrefaie, Dule Shu, Matthew Klenk, Faez Ahmed

Optics

Fragility of Unidirectional Transport in Weakly Disordered Photonic Chern Insulators

Xiaoxuan Shi, Tiantao Qu, Xianbin Wu et al. (+3 more)

Photonic Chern insulators enable unidirectional light transport protected by nontrivial band topology – essential for robust photonic integrated circuits and error-free communication. However, disorder from impurities or defects inevitably exists in practical applications, yet how weak disorder affects topological chiral edge states remains insufficiently understood. Here, we reveal a previously unrecognized mechanism by which weak disorder can disrupt robust propagation of chiral edge states in photonic Chern insulators, despite the preservation of global topological invariants. By randomly replacing a small number of magnetized rods with nonmagnetized impurities in a magnetic photonic crystal, we find that when the excitation frequency approaches the single impurity defect state frequency, weak coupling between spatially extended defect states forms a topologically trivial impurity band inside the topological gap. This enables coexistence and coupling of defect states and chiral edge states. The reciprocal "necklace state" transport channels formed by coupled defect states break the expected unidirectional propagation in topological Chern insulators with weak disorder. Our work reveals that topological chiral edge state and disorder interactions are more intricate than previously understood and provides new insights into stability and control of topological transport in realistic applications.
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All authors Xiaoxuan Shi, Tiantao Qu, Xianbin Wu, Mudi Wang, Lei Zhang, Jun Chen

History and Philosophy of Physics · Cross-listed from quant-ph

Decoherence without the state: A causal quantum Darwinist approach

Nick Ormrod, Tein van der Lugt, Yìlè Yīng et al. (+1 more)

The consistent histories formalism can be used to describe histories comprised of events across many systems, times, and places, plausibly rich enough to describe our experiences of the classical world; however, many consistent history sets are nonclassical and thus not obviously relevant to our experiences. Meanwhile, the program of environmentally induced decoherence identifies dynamically privileged classical degrees of freedom, but provides no general account of when or how many such degrees of freedom consistently combine to form histories. This work shows that the strengths of these two approaches can be combined by adopting a dynamics-first perspective on decoherence. Inspired by quantum causal models and quantum Darwinism, we define the process of decoherence in terms of the causal influences through unitary dynamics required for the proliferation of information about observables. We characterise decoherence as a property of the unitary dynamics, without presupposing the existence of any quantum state. Instead, we show that the state emerges from dual decoherence, related to decoherence by time-reversal of the unitary dynamics. Indeed, for any set of systems in an arbitrary unitary circuit, decoherence and its dual single out a privileged consistent history set – and we demonstrate through examples that states emerge from dual decoherence while outcomes emerge from decoherence. Hence the idea that quantum states emerge from the process of decoherence turns out to be the key missing ingredient for unifying environmentally induced decoherence and consistent histories. Taking this idea ontologically seriously leads to a recently proposed causal interpretation of quantum theory or a dynamics-first version of the Everett interpretation. The causal approach also sheds light on the suppression of off-diagonal terms, time asymmetry, and robustness of the pointer basis.
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All authors Nick Ormrod, Tein van der Lugt, Yìlè Yīng, Jarosław K. Korbicz

Plasma Physics

Full-gap kinetic limitation of thermionic-electron transport for electron transpiration cooling

Wushun Zhang, Weixing Zhou and Yinjian Zhao

Electron transpiration cooling (ETC) can reduce aerothermal loads on sharp hypersonic leading edges, but its performance is governed by whether thermionically emitted electrons escape the hot surface or return as cathode-directed backflow. Here, a one-dimensional-in-space, three-dimensional-in-velocity electrostatic particle-in-cell/Monte Carlo collision model is developed for a full cathode–anode plasma diode, resolving thermionic emission, collisional plasma transport, emitted-electron backflow, and downstream collection. A helium benchmark is used to examine emitted-electron transport and backflow-limited current flow. With increasing imposed emission, the diode first remains in a weak-backflow regime, where net emitted-electron transport and downstream collection both increase with emission. Further increasing the emission produces a sharp transition to backflow-limited transport between $7.0\times10^{19}$ and $7.5\times10^{19},\mathrm{m^{-2},s^{-1}}$. At $7.25\times10^{19},\mathrm{m^{-2},s^{-1}}$, the backflow ratio reaches $54.03%$, while the net transport and downstream collection efficiencies fall to about $46%$. Above this transition, added backflow overcompensates the imposed emission increase, reducing useful emitted-electron transport rather than causing saturation. Boundary energy diagnostics show that stronger emission may still increase the nominal cathode-side cooling metric, but after transition this metric no longer indicates improved emitted-electron escape or full-gap transport. These results show that the present PIC-MCC framework captures the key kinetic processes governing ETC-relevant emitted-electron escape and backflow limitation.
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Geophysics

Functional-prior-based approaches to Bayesian PDE-constrained inversion using physics-informed neural networks

Ryoichiro Agata and Tomohisa Okazaki

Physics-informed neural networks (PINNs) provide a mesh-free framework for solving PDE-constrained inverse problems, but their extension to Bayesian inversion still faces a fundamental difficulty: prior distributions are typically defined in the weight space of neural networks, whereas physically meaningful prior assumptions are more naturally expressed in function space. In this study, we introduce a unified framework, termed functional-prior-based approaches to Bayesian PDE-constrained inversion using physics-informed neural networks (fpBPINN), to incorporate functional priors into Bayesian PINN-based inversion. We consider two complementary approaches. The first is a functional-prior-informed Bayesian PINN (FPI-BPINN), in which a neural network weight prior is learned to be consistent with a prescribed functional prior, and Bayesian inference is subsequently performed in weight space. The second is function-space particle-based variational inference for PINNs (fParVI-PINN), which performs Bayesian estimation using ParVI directly in function space. We also show that random Fourier features (RFF) play an important role in representing Gaussian functional priors with neural networks and in improving posterior approximation. We applied the proposed approaches to one-dimensional seismic traveltime tomography and two-dimensional Darcy-flow permeability inversion. These numerical experiments showed that both approaches accurately estimated posterior distributions, highlighting the significance of introducing physically interpretable functional priors into Bayesian PINN-based inverse problems. We also identified the contrasting advantages of FPI-BPINN and fParVI-PINN, namely flexibility and accuracy, respectively.
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Thursday 7 May

Optics

Comment on "Angle insensitive filters based on Fabry-Perot resonance structures" [J. Appl. Phys. 136, 193102 (2024)]

Michele Cotrufo

In a recent paper, Cao et al. investigated numerically a structure made of two cascaded metasurfaces to realize angle-independent filters. Their calculations show a remarkable performance, with a perfect angle-independent response up to 70 degrees. However, despite several attempts and interactions with the authors, we have been unable to replicate these simulations. This Comment discuss (1) our failed attempts at reproducing the authors' calculations, (2) the lack of any physical explanation for such angle-independent behavior, (3) the fact that the results of Cao et al. can, in fact, be well reproduced by simulations which use incorrect boundary conditions.
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Physics and Society

A Novel Urban Flood Dynamical System Model and a Corresponding Nonstandard Finite Difference Method

Yongfu Tian, Shan Ding, Guofeng Su et al. (+1 more)

Urban flood disaster is one of the most serious natural disasters. Numerous flood simulation models have been proposed and relatively matured. However, two major challenges persist: excessive simplification of the city system and high computational complexity. To break these limitations, this paper develops an Urban Flood Dynamical System Model (UFDSM) based on the concept of the Cellular Automata Urban Flood Model. This model allows flexible customization of cell types and selection of water motion or distribution rules based on actual urban environments to incorporate as much the urban system data as possible. The water motion and distribution rules can be simple, which could reduce the computational complexity, but not arbitrary. So, a sufficient condition is provided so that solutions of dynamical system align with macroscopic physical conditions governing water movement. Then, to preserve the evolutionary properties of the UFDSM, we propose a first-order conservation nonstandard finite difference algorithm. This numerical method ensures positive solutions and conservation of water while maintaining the same fixed-point characteristics as the dynamical system. And, this numerical method is validated by comparing it with an analytical solution.Furthermore, to verify the applicability of our model, we performed an urban flood simulation experiment and compared it to HEC-RAS. There is approximately a 2mm discrepancy in distance dp' and 0.02mm discrepancy in distance d2' , with the relative distance Rp about 7.5% and the relative distance R2 approximately 0.06%. Additionally, the proposed model is easily coupled with other hydrological processes and facilitates data assimilation, thereby offering promising practical applications.
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All authors Yongfu Tian, Shan Ding, Guofeng Su, Jianguo Chen

Biological Physics · Cross-listed from cond-mat.stat-mech

Anomalous Diffusion as Structural Memory: An Extended Structural Dynamics Approach

Patrick BarAvi

Sub-diffusion in biological systems is conventionally treated as anomalous, requiring fractional derivatives, heavy-tailed waiting times, or fitted memory kernels. We argue that this anomaly is an artifact of an incomplete phase space. Standard frameworks model diffusing particles as points. Biological molecules are not points. They are three-dimensional deformable entities whose position, orientation, and internal structure are irreducible physical properties, not modeling conveniences appended to a point mass. Within the Extended Structural Dynamics (ESD) framework, each particle is a primitive structured entity with translational, orientational, and deformational degrees of freedom. When dynamics on this full phase space are projected onto the translational subspace alone, a memory kernel emerges from the projection without phenomenological postulate. The subdiffusion exponent is determined by the internal mode spectrum, independently measurable from B-factors, NMR order parameters, or molecular dynamics simulations, without fitting to transport data. Four falsifiable predictions follow: subdiffusion strength correlates with molecular flexibility; temperature drives crossover to normal diffusion at a characteristic energy scale set by internal mode frequencies; a non-zero rotation-translation cross-correlation spectrum encodes internal dynamics, identically zero in point-particle models; and memory timescales scale as the square of particle size. Quantitative consistency with experimental observations for proteins in crowded media is demonstrated using independently estimated structural parameters. What appears anomalous from the point-particle perspective is the expected behavior of structured matter projected onto an impoverished description. The anomaly is not in the physics. It is in the phase space.
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Biological Physics

Modeling Optical Polarization Evolution in Myelinated Axon Waveguides with Realistic Imperfections

Ethan Davies, Rishabh and Christoph Simon

Biophotonic signaling via axons has been proposed as a potential mode of neural communication, where information might be encoded not only in photon number and wavelength but also in polarization. Although earlier computational studies have examined how structural imperfections influence optical transmission, their effects on polarization fidelity remain unexplored; previous modeling of polarization fidelity in myelinated axons has largely focused on idealized geometries. This study incorporates three structural imperfections characteristic of axons in vivo: variation in myelin thickness, non-circular cross-sectional geometry, and axonal bending, within a model that includes four nodes of Ranvier. We find that variation in myelin thickness alone has minimal impact on fidelity, while non-circular cross-sections show strong mode dependence. Axonal bending has the most significant influence, generating large fluctuations and deep fidelity dips. When all imperfections are combined in a single axon model, the simulations show substantial drops in fidelity, yet certain modes exhibit recovery, with repeated revivals reaching values of around 0.8, which exceeds the revivals observed in the single imperfection cases. Overall, the results indicate that although structural imperfections affect polarization, polarization-based biophotonic signals might remain recoverable even in realistic axons, lending support to the plausibility of polarization-based biophotonic signaling in the brain.
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Atmospheric and Oceanic Physics

Growth of small localized perturbations in Surface Quasi-Geostrophic turbulence

V.J. Valadão, M. Cencini, F. De Lillo et al. (+2 more)

The butterfly effect, namely the amplification of an initially localized infinitesimal perturbation, is a fundamental hallmark of chaotic systems. While this phenomenon is well understood in low-dimensional chaotic systems, its implications become more complex in spatially extended systems characterized by multiple temporal and spatial scales, such as geophysical flows. In this Letter, we investigate the butterfly effect by examining the evolution of infinitesimal localized perturbations in a minimal model of mesoscale geophysical turbulence. We find that localized perturbations exhibit highly variable dynamics, including an initial transient regime during which perturbation energy decreases while the error spectrum shifts from the injection scale toward the system's most unstable configuration. The duration of this transient regime spans a broad range and may persist for several characteristic small-scale times, depending on the perturbation's size, its initial location, and the Reynolds number.
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All authors V.J. Valadão, M. Cencini, F. De Lillo, S. Musacchio, G. Boffetta

Accelerator Physics

The Case for Space-Based Particle Colliders: Orbital Infrastructure as a Path to Grand Unification Energy Scales

Viktor Danchev, Alex Dyer, Sebastian Grau et al. (+1 more)

The Standard Model of particle Physics has been validated to extraordinarily high precision by the Large Hadron Collider (LHC). Yet it leaves some of the most fundamental questions in Physics unresolved: the nature of dark matter, the hierarchy problem, and the unification of forces. Multiple next-generation terrestrial colliders have been proposed such as the Future Circular Collider (FCC) which will reach centre-of-mass energies of $\approx$100 TeV, yet the energy scales at which hints of Grand Unified Theories (GUTs) and string theory are expected to be observed ($10^{11}-10^{13}$ TeV) remain orders of magnitude beyond the reach of any terrestrial facility. We argue that the path to these energy frontiers inevitably leads to Space. By examining the fundamental scaling law for circular proton colliders, we establish that colliders of radius $10^3-10^5$ km are required to enter the PeV-EeV regime. In addition, Space-based colliders benefit from virtually free ultra-high vacuum ($< 10^{10}$ particles/m$^3$ above 1000 km altitude), passive cryogenic cooling, reduction of geological and political constraints, and perhaps most importantly – the substantial reduction of the thermodynamic penalty that dominates terrestrial cryogenic power budgets. We survey existing proposals for beyond-Earth colliders, derive order-of-magnitude requirements for an orbital collider constellation, and assess feasibility against current and near-term spacecraft capabilities in formation flying, power generation, and precision attitude control. We conclude that recent developments in orbital infrastructure – particularly gigawatt-scale orbital power architectures being developed for Space-based data centers – are converging with the needs of a Space-based mega collider, making serious feasibility studies warranted and promising a more certain path towards the core questions of modern Physics.
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All authors Viktor Danchev, Alex Dyer, Sebastian Grau, Guillaume Vazeille

Plasma Physics

Accelerating integrated modeling with surrogate-based optimization: the MAESTRO workflow

P. Rodriguez-Fernandez, N. T. Howard, J. Hall et al. (+14 more)

This paper introduces the MAESTRO workflow, that enables the coupling of the PORTALS framework [P. Rodriguez-Fernandez et al, Nucl. Fusion 2024] with external solvers for the plasma equilibrium, pedestal physics, divertor constraints and heating. The surrogate-based optimization nature of the transport solver is ideally suited for external coupling, allowing efficient steady-state predictions of plasma profiles with full physics models. Improvements in the surrogate modeling of quasilinear transport models with PORTALS are presented, which enable the efficient handling of discontinuities in the transport fluxes that can arise from numerical issues or physical instabilities with extreme stiffness. The combination of physics-informed methods and advanced numerical techniques allows the MAESTRO workflow to provide accurate and efficient predictions of steady-state plasma profiles, which are critical for fusion reactor design and optimization.
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All authors P. Rodriguez-Fernandez, N. T. Howard, J. Hall, A. Saltzman, A. Martin-Sanabria, A. Ho, G. Snoep, J. Pimentel-Aldaz, C. Holland, M. Muraca, P. de Lara Montoya, K. Yanna, A. E. White, T. Body, A. J. Creely, J. C. Hillesheim, P. B. Snyder

Physics Education

Using Large Language Models to Analyze Engagement in Computational Thinking via Computational Physics Essays

Sean Savage, Amir Bralin, Paul Hur et al. (+1 more)

As computational thinking (CT) becomes increasingly important to physics education, the need for authentic, project-based assessments has grown. While open-ended multimodal assignments, such as Computational Physics Essays (CPEs), help capture student reasoning and encourage active learning, they introduce a significant evaluation bottleneck. Manually grading these complex notebooks across a complex taxonomy of computational practices is resource-intensive and limits scalability in large-enrollment courses. In this study, we investigated the viability of using a multimodal Large Language Model (LLM) to automate the evaluation of 100 student-generated CPEs. Using a human-coded baseline, we systematically evaluated the model's capacity to detect student engagement across 20 distinct CT sub-practices and a holistic overall quality score. The results showed that the LLM performs very well on clearly defined tasks, achieving an 84% exact agreement with human raters on the binary sub-practices. However, more subjective constructs proved challenging, with the model reaching only a 71% agreement for the holistic quality analysis. Our findings demonstrated that while LLMs can reliably automate the detection of specific computational practices, subjective evaluation remains a hurdle.
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All authors Sean Savage, Amir Bralin, Paul Hur, N.Sanjay Rebello

Applied Physics · Cross-listed from cs.CE

Scalable Active Metamaterials for Shape-Morphing

Jipeng Cui and Wei "Wayne" Chen

Shape-morphing materials allow structures such as morphing wings, soft robots, and deployable devices to change their shape on demand without mechanical joints or motors. Aperiodic active metamaterials, in which every unit is designed individually, can realize complex programmed shape changes, but their design by structural optimization requires repeated analyses of the full structure, at a cost that grows rapidly with the number of units. This paper presents a scalable active metamaterial (SAM) framework that designs such structures without simulating the assembled structure. The design domain is discretized into unit cells composed of a stiff frame with compliant hinges and a soft, thermally active infill. The stiff frame limits the interaction between neighboring unit cells, which approximately decouples the design problem into two scales. At the macroscale, the deformation required for each unit cell is obtained by a constrained Laplacian mesh-editing formulation with soft data-driven feasibility constraints. At the microscale, the infill geometry of every unit cell is generated by a conditional diffusion model trained on finite-strain finite element simulations of unit cells. All designs are verified by geometrically nonlinear finite element simulations of the assembled structures. The design cost grows approximately linearly with the number of unit cells. The framework is demonstrated on single-point actuation, multi-point control, and shape matching tasks. We further analyze the sources of the remaining error and discuss possible ways to mitigate it. This work establishes a foundation for the systematic, scalable design of highly complex shape-morphing active metamaterials and for understanding the mechanisms governing their target-matching accuracy.
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Physics and Society · Cross-listed from q-bio.PE

Quo nomine vis vocari? Random-copying, conformity, and anti-conformity in the temporal sequence of papal names

Egor Lappo and Noah A. Rosenberg

The study of cultural evolution seeks to understand the processes by which behavioral variants are chosen in human cultures over time. The selection of new popes, each of whom chooses a papal name – typically reusing previous names in reference to previous popes – is among the longest ongoing cultural processes taking place in a single human institution. Here, we test the papal naming sequence against predictions of models developed in population genetics and stochastic processes – Ewens sampling theory and the Chinese restaurant process – which in the case of papal names amount to randomly copying an existing name in proportion to its frequency, with the possibility of innovation. Although papal name choices are careful individual decisions, we find that the sequence of papal names often accords with a random-copying model, with long periods in which a model of conformity in name choices more closely fits the name sequence and other periods in which anti-conformity provides a closer fit. Hence, despite the consideration that enters into individual choices, aggregate cultural behavior in a 2000-year old human process can potentially be described with simple laws. Our methods can potentially be used to deepen quantitative analyses of conformity and anti-conformity in other cultural processes that involve discrete cultural variants.
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Biological Physics · Cross-listed from q-bio.CB

Essential Role of Extrinsic Noise in Models of E. coli Division Control

Mattia Corigliano, Kuheli Biswas, Matteo Bocchiola et al. (+3 more)

Our understanding of cell division control in bacteria still relies largely on interpreting correlations between phenomenological variables, with limited connection to the underlying molecular mechanisms. Here, we analytically solve a stochastic threshold-accumulation model in which a size-dependent divisor protein triggers division upon reaching a noisy, autocorrelated threshold, quantifying within a unified framework the combined effects of intrinsic and extrinsic noise and key mechanistic parameters such as protein reset and threshold memory. We show that incorporating these elements yields behavior far richer than the commonly assumed adder, spanning a continuum of division strategies from timer to sizer while modulating size fluctuations in a nontrivial fashion. Comparison with single-cell E. coli data shows that extrinsic noise and additional mechanistic ingredients are required to account for the observed size fluctuations. The adder emerges when threshold correlations balance protein reset, generalizing the hypothesis that full reset is necessary to maintain adder control. Our results establish a unified analytical framework linking stochastic molecular processes to emergent division laws, to be used in more complex bacterial cell-cycle models.
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All authors Mattia Corigliano, Kuheli Biswas, Matteo Bocchiola, Daniele Montagnani, Ariel Amir, Marco Cosentino Lagomarsino

Physics and Society

Societal Complexity and Physical Power

Jerome Lewandowski

As the current thermo-industrial civilization expands, its technological and societal complexities increase. We suggest that physical power, economic activity and societal complexity are linked. A simple, intuitive model based on Systems Dynamics is used as an illustration.

Atmospheric and Oceanic Physics

AIMIP Phase 1: systematic evaluations of AI weather and climate models

Brian Henn, Christopher S. Bretherton, Nikolay Koldunov et al. (+18 more)

We present the AI weather and climate model intercomparison project (AIMIP), phase 1. Drawing from the rich tradition of intercomparisons in climate model development, we specify a common experiment, output data format, and training constraints (namely, training against historical reanalysis data) for AIMIP Phase 1 models. We aim to identify differences in modeling frameworks and AI architectural choices that influence model behavior, and build trust in AI weather and climate models through open data and evaluation. AIMIP Phase 1 models must simulate the atmosphere given specified historical sea surface temperatures over 1979-2024. We evaluate the models' performance using five major evaluation criteria: biases, trends, response to El Niño-related sea surface temperature anomalies, temporal variability, and out-of-sample generalization tests. We find that the AI models are able to simulate the historical climate and response to forcing as well as a conventional physically-based model, but some AI models underestimate historical warming trends, and their predictions diverge in the out-of-sample generalization tests. We describe the AIMIP Phase 1 dataset that is publicly available for additional evaluations.
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All authors Brian Henn, Christopher S. Bretherton, Nikolay Koldunov, Christian Lessig, Maria J. Molina, Troy Arcomano, Oliver Watt-Meyer, Guillaume Couairon, Renu Singh, Robert Brunstein, Yana Hasson, Antonia Jost, Noah Brenowitz, Peter Manshausen, Nathaniel Cresswell-Clay, Dale Durran, Kyle Joseph Chen Hall, Janni Yuval, Dmitrii Kochkov, Stephan Hoyer, Ignacio Lopez-Gomez

Optics

Physics-Based Flow Matching for Full-Field Prediction of Silicon Photonic Devices

Joseph Quaratiello and Anthony Rizzo

Designing photonic integrated circuits requires accurate electromagnetic field simulations, which remain computationally expensive even for simple device geometries. We present PIC-Flow, a generative neural surrogate that predicts electromagnetic field distributions for photonic devices given their geometry and operating wavelength as an alternative to costly finite-difference time-domain (FDTD) simulations. Our approach combines three key ideas: (i) conditional flow matching as the generative framework, learning a velocity field that transports Gaussian noise to physically valid field solutions; (ii) a real-valued U-Net operating on split real and imaginary field channels; and (iii) physics-constrained training through a Helmholtz residual loss enforcing $\nabla^2 E_z + k_0^2 \varepsilon E_z = 0$. We introduce an interface-aware masking scheme for the Helmholtz residual that excludes dielectric boundary pixels where finite-difference stencil errors dominate, yielding a physically meaningful compliance metric. The data set consists of 22,500 ground-truth FDTD simulations split evenly between multimode interferometers, Y-branches, and directional couplers at $\lambda=1.55\,\mu$m in an 80/10/10 split between training, validation, and test sets. We evaluate ablations on the network against the held out test devices and also show that the model generalizes to held out device classes such as S-bends, tapers, and cascaded Y-branches. Rather than a drop-in replacement for FDTD, this work establishes a foundation that, with broader data coverage, more compute, and further training optimization, could scale toward broadband, device-agnostic field prediction with dramatically improved runtime for rapid design-space exploration of complex photonic devices and circuits.
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Chemical Physics

Many-body theory predictions of positron binding energies in five-membered heterocycles involving N, O, S and NH substituents

S. K. Gregg and D. G. Green

Positron binding energies and Dyson orbitals for five-membered heterocycles with N, O, S and NH substituents are predicted ab initio via many-body theory. The positron-molecule correlation potential (self energy) is calculated via solution of Bethe-Salpeter equations that describe the positron-induced polarization of the target and screening of the electron-positron Coulomb interaction at the $GW$@BSE level, the infinite electron-positron ladder series that describes the crucially important process of virtual positronium formation, and the analogous positron-hole ladder series. The all-order calculations employ Gaussian-orbital bases and are implemented in the EXCITON+ code. The effect of substituting combinations of N, O and S atoms, and the NH group in the molecule's ring is studied, and the role of individual molecular orbitals, many of which are found to significantly contribute to the correlation potential, quantified. Analysis of the positron bound-state Dyson orbitals shows that the positron is typically localized next to one or two of the substituents in the ring, with the order of preference N, S, O, then NH, and is also influenced by aromaticity and the presence of double ($\pi$) bonds in the ring.
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Physics and Society

Climate and dengue synchronization in southern Brazil: a municipal analysis with cross-state validation

Enrique C. Gabrick, Antonio M. Batista, Iberê L. Caldas et al. (+2 more)

Dengue transmission is rapidly expanding beyond its historical tropical range, raising concerns about how climate change may alter the collective dynamics of epidemics. While most studies focus on transmission risk, much less is known about how climate affects the synchronization of outbreaks. In this work, we investigate dengue synchronization using epidemiological and climate data from 74 municipalities in the state of Paraná (southern Brazil) between 2010 and 2024. We quantify outbreak coherence using the Event Synchronization (ES) method. Our results reveal a transition from a low-transmission regime to a high-transmission regime accompanied by a marked increase in synchronization across cities. We also show that climate anomalies increase the number of permissive days for dengue transmission. Our results suggest that such days are significantly associated with outbreak synchronization. We identify a two-stage climate mechanism: conducive climatic conditions first reduce the probability of asynchronous states and coincide with the emergence of synchronized outbreaks, and subsequently sustain higher synchronization levels. Extending the analysis through comparative analyses in Ceará and Minas Gerais, we uncover that climate consistently amplifies synchronization, although its role in the onset of synchronization depends on regional climatic regimes. These findings highlight climate-driven synchronization as an emerging feature shaping dengue dynamics.
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All authors Enrique C. Gabrick, Antonio M. Batista, Iberê L. Caldas, Jürgen Kurths, Maíra Aguiar

Optics

Monocular passive event-based range-finding of airborne objects using the Scheimpflug principle

Nathan Meraz, Ronan Taneja, Rachel Chan et al. (+4 more)

Passive 3D sensing is increasingly critical for early detection and tracking of small aerial vehicles (UAVs), where traditional active ranging can be tactically undesirable. We present SCHeimpflug for Optical Ranging TechnologY (SCHORTY), a single-aperture passive and active ranging architecture that exploits the Scheimpflug principle to encode range along a tilted object space plane by tilting the sensor relative to the imaging optics. SCHORTY requires only a one-time geometric calibration to map pixel coordinates to range and is inherently sensor and waveband agnostic. We implement SCHORTY using both a visible frame-based camera and an event-based camera (EBC) with closely matched pixel sizes for comparable horizontal resolutions and range binning. Controlled flights of an octocopter and a fixed-wing UAV equipped with GPS provide ground truth distances out to 1.1 km. Experimental results show that SCHORTY achieves deterministic range assignment limited primarily by the projected pixel size, which grows squared distance, while avoiding computationally intensive inverse reconstructions common in coded aperture and PSF engineered systems. In the EBC configuration, EBC-SCHORTY inherently suppresses static background and emphasizes motion, improving UAV detectability in cluttered natural scenes and under turbulence and motion blur. Additionally, we observe an asymmetric defocus blur about the object plane that depends on UAV trajectory, suggesting an extra cue for localization and trajectory inference. These results demonstrate SCHORTY as a practical and Size, Weight, and Power (SWaP) efficient passive ranging solution for medium-range UAV observation and motivate future integration with 2.5D/3D PSF engineering and event-based deconvolution to enhance 3D sensing performance.
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All authors Nathan Meraz, Ronan Taneja, Rachel Chan, Alisha Whitehead, Gabriella Mayrend, Megan Birch, Joseph L. Greene