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Physics Physics

physics Watch Physics

Monday 6 July

Atmospheric and Oceanic Physics · Cross-listed from eess.SY

Short-Horizon Sparse Model Predictive Control for Precipitation Reduction Using Numerical Weather Prediction

Yuta Tanikawa, Yuga Tomita and Toshiyuki Ohtsuka

This study proposes a precipitation control framework integrating a realistic Numerical Weather Prediction (NWP) model with model predictive control (MPC). At each control instant in MPC, a finite-difference sensitivity matrix is constructed from the NWP model and used as a local linear model of how perturbations to the atmospheric state affect future precipitation. A sparse convex optimization problem is then solved to compute the control input, which is implemented as a perturbation to the atmospheric state. To reduce computational cost in sensitivity analysis, multiple grid points in the NWP model are treated collectively as a single block, and a uniform perturbation is applied to all points within each block. Moreover, a tailored convex optimization problem is introduced to effectively control the accumulated precipitation at the end of a weather event, using a prediction horizon much shorter than the entire event duration while promoting spatially sparse atmospheric perturbations. To evaluate the proposed MPC method, four control methods are compared: (i) initial-only open-loop optimal control (IO-OL), (ii) full-horizon open-loop optimal control (FH-OL), (iii) shrinking-horizon optimal control (SHOC) with a fixed terminal time, and (iv) single-move MPC with a fixed prediction-horizon length. Numerical experiments on a warm bubble benchmark demonstrate that MPC achieves precipitation reduction comparable to SHOC while reducing the total computational time relative to FH-OL and SHOC. Moreover, despite using a linear prediction model, MPC successfully achieves a challenging level of precipitation reduction, even when open-loop optimal control methods, namely, IO-OL and FH-OL, fail because of nonlinear atmospheric evolution. These findings suggest that MPC is a promising control framework for NWP-based precipitation reduction in complex weather events.
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Optics

Characterization of Event-Based Vision Sensors for High-Speed Optical Instrumentation

Tomás Lopes, Joana M. Teixeira, Tiago D. Ferreira et al. (+3 more)

Event-based vision sensors provide asynchronous event generation and microsecond timestamp resolution, which may be useful for high-speed optical measurements. However, precise event timestamps do not necessarily guarantee accurate reconstruction of temporally varying optical signals, particularly under dense and spatially extended illumination, imposing operational limits when used as optical interrogators that remain underexplored in the literature. To address this knowledge gap, this work presents a systematic, quantitative characterization of the temporal response and waveform reconstruction fidelity of an IMX636-based event camera under both controlled sinusoidal and pulsed optical excitation. For this, frequency-domain measurements are first used to evaluate modulation response, event-rate behavior, polarity balance, and spectral reconstruction fidelity over a wide range of illumination conditions and region-of-interest geometries. Then, complementary pulse-based measurements quantify first-event latency, response duration, recovery dynamics, and pulse-width reconstruction accuracy under rapidly repeated excitation, showing that optical transitions can be detected with first-event latencies below 5 microseconds. However, the complete event response extends over significantly longer timescales due to photoreceptor dynamics, refractory behavior, and readout serialization. Under high-frequency modulation and short-pulse excitation, the reconstructed waveforms progressively degrade because of temporal spreading and imbalance between positive and negative event generation. The measurements further demonstrate that the temporal fidelity of the reconstructed signal depends strongly on the geometry and spatial activity of the selected region of interest.
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All authors Tomás Lopes, Joana M. Teixeira, Tiago D. Ferreira, Catarina S. Monteiro, Pedro A.S. Jorge, Nuno A. Silva

Applied Physics · Cross-listed from cond-mat.mes-hall

Tunable Nonlinear Landscapes in Graphene Nanoelectromechanical Systems

Ateesh K. Rathi, Rajan Singh, Javed A. Mondal et al. (+4 more)

Nonlinear nanomechanical resonators give convenient solid-state access to classical analogs of extreme nonlinear optics and to phononic signal processing. Here we report integer high-harmonic generation and phononic frequency combs in a suspended monolayer graphene drum. A gate voltage breaks the out-of-plane symmetry of the membrane and tunes its fundamental flexural mode onto a 1:2 internal resonance with a higher mode at twice the frequency, where the quadratic coupling between the two modes becomes large. A single drive tone then generates phase-locked integer harmonics in sequence, and at larger drive these fill in to a dense frequency comb. Raising the drive further, we find a reverse period-doubling transition: the comb spacing doubles, the line density halves, and energy flows back into the even-order comb lines. The measured spectra yield the quadratic ($\zeta$) and cubic ($\beta$) nonlinear coefficients of the membrane. These results show how the tunable nonlinear landscape of graphene supports distinct dynamical regimes on demand, allowing a single gated device to act in turn as a frequency multiplier, a broadband comb source, and a chaotic generator.
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All authors Ateesh K. Rathi, Rajan Singh, Javed A. Mondal, Arnab Sarkar, Ryan J.T. Nicholl, Kirill I. Bolotin, Saikat Ghosh

Data Analysis and Statistics

Hybrid pattern recognition for charged particle tracking: Hough transform and convolutional neural efficiency networks

Carlo Varni, Krzysztof Ciesla, Marcin Wolter et al. (+3 more)

Reconstructing charged-particle tracks in silicon detectors is a central task in high-energy physics experiments and a key component of both offline reconstruction and online event selection. Within the reconstruction chain, the efficient and high-purity formation of track candidates plays a critical role in the overall performance. Among the many approaches developed over the years, the Hough transform (HT) has been widely studied as a fast geometry-driven method for track finding. However, in high-occupancy environments such as those expected at the High-Luminosity LHC (HL-LHC), the HT tends to produce a large number of spurious candidates, leading to increased computational overhead in subsequent reconstruction stages. In this work, we present a hybrid approach in which the HT serves as a first-stage data preparation step, providing its parameters space image as an input to a neural network trained to suppress false track candidates. The method combines the speed of the HT with the discriminative power of machine learning to achieve both efficiency and purity. In addition no data transformations are involved when combining these steps resulting in a simpler and more performant algorithm. Performance studies using the Open Data Detector simulated in the ACTS framework under realistic HL-LHC pileup conditions will be presented.
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All authors Carlo Varni, Krzysztof Ciesla, Marcin Wolter, Radoslaw Karol Mienkowski, Noemi Calace, Tomasz Bold

Computational Physics · Cross-listed from quant-ph

Mapping open quantum dynamics onto graphs

Kyuho Kim, Dayeong Lee, Seungkyun Park et al. (+3 more)

Graph-theoretic frameworks have been widely employed in quantum physics to address the high-dimensional complexity of quantum systems. Although open quantum dynamics incorporates system-bath coupling via numerous interacting operators, it has been formulated algebraically with a partial set of jump operators or statistically universal reservoirs, leaving the underlying connectivity structure largely unexplored. Here, we propose a universal graph-theoretic framework for Markovian quantum dynamics. The framework maps open quantum dynamics onto two uniquely defined graphs, where the quantum master equation is rigorously interpreted as the average wave characteristic of operator-valued signals across the graphs. Applying this framework to the open quantum Rabi model, we demonstrate an open-system generalization of Fock-state lattices, characterize graph-topological signatures of dissipation, and classify the weak-to-ultrastrong coupling transition. Building on these representations, graph pruning reveals the backbone of open quantum dynamics, which enables superior graph neural-network learning. Our results bridge graph theory and open quantum dynamics, achieving efficient data-driven analysis of high-dimensional complexity.
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All authors Kyuho Kim, Dayeong Lee, Seungkyun Park, Xianji Piao, Namkyoo Park, Sunkyu Yu

Optics

All-optical control of coherent perfect absorption via frequency conversion

Rikizo Ikuta, Hirokazu Kobayashi and Hiroki Takahashi

Coherent perfect absorption (CPA) extinguishes optical fields through interference and dissipation, but conventional implementations rely on material loss that is largely fixed after fabrication. Here we demonstrate all-optically controllable CPA based on frequency conversion in a periodically poled lithium niobate waveguide resonator. Pump-driven frequency conversion couples a resonant signal field at 1581 nm in the main system to a non-resonant environmental mode at 780 nm, creating a dynamically tunable effective loss channel. The nonlinear cavity acts as a tunable lossy beamsplitter without intrinsic material absorption. Under coherent two-sided signal injection, we observe up to 92 % absorption. We further introduce environment-assisted CPA by injecting an external field into the frequency-converted environmental mode, turning the environment from a passive loss reservoir into an addressable coherent control port. Our results establish a frequency-conversion-based platform for all-optical control of dissipation in CPA, combining pump-tunable loss with environment-assisted coherent control.
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Computational Physics · Cross-listed from cs.LG

A Physics-Regulated Neural Framework for Learning 3D Grain Growth Dynamics

Zhihui Tian, Kang Yang, Michael Tonks et al. (+2 more)

Grain growth is governed by the reduction in grain boundary energy and exhibits well-established statistical scaling laws. Developing data-driven surrogates that preserve these physical invariants while remaining computationally scalable remains challenging, especially in 3D. We present 3D-PRIMME (Physics-Regulated Interpretable Machine Learning for Microstructure Evolution) for learning three-dimensional grain growth dynamics. The model is trained using only two consecutive time steps yet accurately reproduces the linear coarsening law and preserves topological statistics over extended time scales. Despite being trained on a $100^3$ grid points with 512 grains, the learned evolution operator is applied to domains up to $1024^3$ grid points with 550000 grains without retraining, maintaining consistent kinetics and grain topology across orders-of-magnitude increases in system size. These results demonstrate that 3D-PRIMME learns a scale-independent and temporally stable local evolution rule, enabling efficient and robust large-scale surrogate prediction of 3D microstructure evolution.
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All authors Zhihui Tian, Kang Yang, Michael Tonks, Amanda R. Krause, Joel B. Harley

Optics

Critically coupled zeroth-order resonance for ultrathin nonlinear photonics

Hae-Seok Jeong, Soon-Jae Lee, Young-Ho Jin et al. (+2 more)

Ultrathin active materials are essential for compact nonlinear and quantum photonic devices, yet no general principle exists to link their optical constants to the cavity designs required for simultaneous field buildup and reflection suppression. Consequently, achieving extreme optical confinement currently relies on trial-and-error optimization for every new material. Here, we establish a design rule for metal-backed cavities that maximizes light-matter interaction by ensuring the simultaneous satisfaction of zeroth-order resonance and critical coupling. We derive a closed-form analytical condition that partitions the (n, k) plane into critically coupled, over-coupled, and under-coupled regimes, each mapping to a specific minimal architecture. The critical curve admits a three-layer open cavity, the over-coupled region a closed cavity with a semi-transparent top mirror, and the under-coupled region a spacer-assisted geometry. For low-loss materials, the closed cavity spatially separates dissipation from field accumulation, allowing the quality factor to be controlled by the external mirror rather than intrinsic medium absorption. We validate this framework with 3R-MoS2, demonstrating a second-harmonic enhancement of 1.19 x 10^5 relative to a monolayer, accompanied by the near-complete suppression of reflected pump waves. These results provide a universal framework for efficient light-matter interaction in ultrathin nonlinear and quantum photonics.
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All authors Hae-Seok Jeong, Soon-Jae Lee, Young-Ho Jin, Su-Hyun Gong, Q-Han Park

Optics

Non-Hermitian Dirac Vortex: Minimal Theory for Topological-Cavity Surface-Emitting Laser

Zong-Liang Li, Guang-Rui Li, Le-Chen Yang et al. (+2 more)

We construct a non-Hermitian Dirac-vortex model that combines a complex-mass winding with an infinite-imaginary-potential boundary, extending the Jackiw-Rossi and neutrino-billiard models to the dissipative regime. Moreover, this model serves as a minimal theory for the recently proposed topological-cavity surfaceemitting laser (TCSEL): the imaginary mass encodes vertical radiation loss and the absorbing boundary defines the active region. We derive closed-form expressions for the modal frequencies, thresholds, and tunable vectorbeam polarizations, which are validated experimentally. Our work provides a rare example in which an analytical non-Hermitian topological theory captures the essential physics for engineering practical optoelectronic devices.
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All authors Zong-Liang Li, Guang-Rui Li, Le-Chen Yang, Zhong Wang, Ling Lu

Chemical Physics

Differentiable OPLS Force Field Parameterization for Ionic Electrolytes and High-Throughput Application to Lithium-ion Batteries

Haichao Huang, Zilin Chen, Qi Liu et al. (+14 more)

The rational design of ionic electrolytes for lithium-ion batteries (LIBs) is severely constrained by the vast solvent-salt combinatorial space and low efficiency of empirical trial-and-error. While molecular dynamics (MD) bridges microscopic solvation structures and macroscopic physicochemical properties, classical force fields often lack sufficient accuracy for multicomponent systems. To address these challenges, we develop an automated differentiable OPLS-AA force field parameterization workflow tailored for general ionic electrolytes. It employs topology-guided atom typification to reduce parameter redundancy and optimizes Lennard-Jones parameters via the DMFF framework, with experimental density as the fitting target and ionic conductivity as an independent validation metric. Rigorous convergence tests yield a standardized simulation protocol with $\sim$100,000-atom systems and 35-40 ns NVT runs to ensure reliable transport property quantification. High-throughput MD simulations of over 10,000 formulations spanning 67 solvents and 15 lithium salts are conducted on the Tianqiong platform, generating a comprehensive dataset covering five core properties: density, dielectric constant, viscosity, diffusion coefficient, and ionic conductivity. t-SNE visualization reveals partial clustering of distinct salt chemistries, continuous property gradients with concentration and temperature, and internal physical self-consistency, with solvent composition identified as another key performance regulator. Together, the accurate transferable force field and large-scale dataset provide a solid foundation for data-driven rational design of ionic electrolytes.
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All authors Haichao Huang, Zilin Chen, Qi Liu, Tianqi Zhao, Yunpei Liu, Guotao Qiu, Jianhui Chen, Zhen Li, Wenshuo Liang, Minsung Cho, Manxue Zhang, Feiyu Kang, Xiaolong Zou, Yidan Cao, Xushan Zhao, Ziqi Cheng, Ye Mei

Fluid Dynamics · Cross-listed from astro-ph.EP

Self-Consistent Evolution Models Show Weak Double-Diffusive Mixing in Jupiter and Saturn

J. R. Fuentes, Ankan Sur, David J. Stevenson et al. (+1 more)

Double-diffusive convection in the “fuzzy” cores of giant planets has been widely discussed as a mechanism for redistributing heavy elements, but its efficiency in evolutionary models remains uncertain. Previous estimates rely on idealized compositional structures and have not treated double-diffusive transport self-consistently in planetary evolution calculations. Here we implement a prescription for transport across convective staircases in the planetary evolution code APPLE and apply it to post-formation interior models of Jupiter and Saturn containing compositional gradients produced during formation. These models are evolved for 4.56 Gyr including convection, diffusion, and double-diffusive transport. We find that double-diffusive convection produces limited mixing between the deep interior and the envelope. In both Jupiter and Saturn, less than $\sim 1\,M_\oplus$ of heavy material is redistributed over the full cooling history, leaving the primordial compositional gradients largely intact. This inefficiency arises because the buoyancy work available to drive compositional transport is constrained by the thermal energy budget of the deep interior, in contrast to idealized Boussinesq simulations that operate in regimes more favorable to layer merging and efficient mixing. As a result, double-diffusive convection alone cannot significantly erode the compositional gradients generated during formation. The observed heavy-element distributions in Jupiter and Saturn therefore likely require additional transport mechanisms or formation pathways, including large collisional events, that produce broader initial mixing than standard accretion models predict.
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All authors J. R. Fuentes, Ankan Sur, David J. Stevenson, Peter Bodenheimer

Fluid Dynamics · Cross-listed from cond-mat.soft

Synchro-nematic and -antinematic ordering of spheroidal circle swimmers

Anson G. Thambi, Alexander N. Dodge and William E. Uspal

Chirality gives a microswimmer something a straight-line swimmer lacks: a phase. This variable both modulates, and is affected by, the hydrodynamic interactions between microswimmers. Here we ask what collective order emerges when many such chiral swimmers are free to move, and how the shape and actuation anisotropies of an individual swimmer dictate the outcome. Using a kinetic theory for hydrodynamically interacting circle swimmers, we show that the interplay between intrinsic rotation, stresslet flows, and Jeffery-like reorientation generates effective phase-locking interactions. Asymmetries in the actuation are encoded through a non-axisymmetric stresslet tensor. At the pair level, pusher swimmers select one of two synchronized states depending on particle shape and actuation asymmetry: in-phase/anti-phase locking, or quarter-shifted locking. Extending the analysis to many-body systems, we find that these pair-level synchronization mechanisms drive emergent collective phases. The swimmers develop global synchro-nematic order when the hydrodynamic coupling favors parallel or anti-parallel phase locking, and synchro-antinematic local order where quarter-shifted locking prevails. A coarse-grained field theory predicts the onset of nematic order through a hydrodynamic instability criterion. In addition, we find that the collective states exhibit crystalline or disordered hyperuniform structure arising from period-averaged hydrodynamic interactions that are effectively repulsive between swimmers. Lattice Boltzmann simulations of chiral oblate squirmers, resolving finite-size and near-field flows, recover the synchro-nematic ordering. Together, these results show how a swimmer's geometric and actuation anisotropies can be leveraged to program synchronization and spatiotemporal order in chiral active matter.
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Physics and Society

Infinite Drive: Optimal Urban Location of Dynamic Wireless Charging at Signalized Intersections

Yudai Honma, Daisuke Hasegawa, Katsuhiro Hata et al. (+3 more)

Dynamic Wireless Power Transfer (DWPT) could eliminate plug-in charging in cities, but optimal urban deployment is complex. This paper develops a mixed-integer programming model that optimizes DWPT location under signalized intersection dynamics – acceleration, deceleration, and queue-position-dependent dwell time – through probabilistic signal patterns and saturation headway-based modeling. A case study of Kawagoe City, Japan, shows that electrifying 1.5% of the road network is sufficient to sustain continuous urban EV operation without plug-in charging for the baseline scenario, and at most 2.9% suffices across all tested assumptions. Monte Carlo simulations of continuous trip chains averaging approximately 600 km and reaching up to approximately 800 km confirm that optimized 12 kWh-battery deployments sustain operation in all simulated runs, revealing an infrastructure-battery tradeoff corresponding to roughly 1.7-3.0 tonnes CO2e of avoided battery manufacturing emissions per vehicle relative to a conventional 40 kWh urban EV. These findings position DWPT deployment as an environmentally efficient pathway for sustainable urban mobility when deployed optimally.
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All authors Yudai Honma, Daisuke Hasegawa, Katsuhiro Hata, Xuesong Zhou, Michael J. Kuby, Takashi Oguchi

Computational Physics

Accelerating Multi-scale Simulations of Nuclear Components via PCYS Interpolation Tables

Fabrizio Aguzzi, Martin S. Armoa, Cesar I. Pairetti et al. (+2 more)

Zirconium alloy core components in nuclear reactors, such as spacer grids and fuel cladding, undergo anisotropic dimensional changes driven by coupled irradiation creep and growth. While micromechanical crystal plasticity frameworks like the Viscoplastic Self-Consistent (VPSC) formulation capture these microstructurally driven phenomena, their integration into macroscopic Finite Element Method (FEM) solvers is computationally prohibitive for engineering-scale components. To bridge this gap, this work presents a multi-scale framework implemented within the open-source FEM solver Code_Aster. The developed interface uses a 5D Interpolation Table (IT) as a static material surrogate to govern instantaneous viscoplastic responses, coupled with a periodic recalibration and first-order Taylor series linearization scheme to track microstructural drift due to radiation damage without on-the-fly database updates. The predictive accuracy, numerical stability, and performance of this Polycrystal Yield Surface (PCYS) interpolation approach are benchmarked against VPSC-FEM simulations under continuous high-dose irradiation scenarios. Material-level assessments demonstrate that the linearization scheme bounds relative errors below 1% for representative deformation paths, maintaining structural compatibility. Furthermore, structural simulations of a spacer grid domain revealed meaningful computational savings, overcoming the multi-scale computational penalty while preserving microstructural fidelity. The proposed framework shows potential for multiphysics structural assessments and safety margin evaluations of core internals over operational lifespans.
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All authors Fabrizio Aguzzi, Martin S. Armoa, Cesar I. Pairetti, Cesar M. Venier, Alejandro E. Albanesi

Sunday 5 July

Physics and Society

Insurance perspectives on climate-related risks

Michelle Dong, Aaron Bruhn, Francis K.C. Hui et al. (+1 more)

Emerging climate risks are bringing pressing challenges for insurers globally, as they face growing uncertainty in quantifying climate impacts from physical and transition risks. Climate risks are broad and can manifest through short-term changes and events, as well as long-term and sometimes irreversible changes, such as sea level rise and temperature increases. Significant economic changes are inevitable as countries transition and adapt to mitigate climate-related risks. Given the wide range of potential implications for insurers, we conducted a series of interviews with insurance practitioners and academic researchers to explore the diverse landscape of current perspectives on climate-related risks. The interviews considered how insurers can manage emerging climate risks and the role of pricing as a signal of risk. In the context of unknown unknowns for insurers in understanding long-term impacts of climate change, we identify the importance of scenario analysis in informing forward-looking drivers of change and the importance it plays in meeting evolving regulatory requirements.
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All authors Michelle Dong, Aaron Bruhn, Francis K.C. Hui, Han Lin Shang

General Physics

The quantum state of graphene

Boris I. Ivlev

A stationary solution of quantum mechanical wave equation is the superposition of eigenfunctions. Each of them corresponds to a vector in the Hilbert space. In a graphene sample one can choose expansion coefficients to get the series convergent solely within the certain circle in the two-dimensional space. Outside this circle the analytic continuation is required in the form of a different series. The exact wave function is referred to as anomalous. It is not a superposition of conventional eigenfunctions and gets ouside the Hilbert space. Anomalous electron and antielectron are possible. The antielectron is not a vacancy in the conventional valence band. The anomalous electron-antielectron pair is created from the anomalous vacuum like the electron-positron pair is created from the electron-positron vacuum. Formation of the anomalous vacuum is not a single electron effect but the collective quantum phenomenon. In the film of graphene the anomalous states are located at the film edge and are expected to be of high conductivity.
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Plasma Physics

Benchmarking Sensor Robustness in Plasma Diagnostic Models: A Systematic Evaluation on TokaMark

Neerav Gupta

Plasma diagnostic models for tokamak fusion devices are almost universally evaluated on clean, complete sensor data. In practice, fusion diagnostics fail regularly: acquisition systems start late, individual sensors die, and signal dropouts cluster precisely when a plasma disruption is approaching. We present the first systematic robustness benchmark for plasma diagnostic ML using the TokaMark dataset of 11,573 MAST shots, evaluating XGBoost, LSTM, Transformer, and the TokaMark CNN baseline across six physically-grounded failure scenarios and three imputation strategies. We introduce the Robustness Score (RS) for standardized cross-architecture comparison. Our central finding is that disruption-proximate sensor failure (corruption injected in the final window timesteps) collapses sequence model performance (LSTM +212% NRMSE) while a statistical feature model remains comparatively stable (XGBoost +37%). Forward-fill imputation eliminates nearly all degradation from random dropout for sequence models (LSTM +57% to ~0%), but offers little help when the end of the window is corrupted. Shot-level alarm evaluation using ground-truth disruption timestamps reveals that LSTM alarm detection collapses to TPR=0.00 under proximate sensor failure, while mean-fill imputation recovers it to TPR=1.00, a reversal of the pattern observed in NRMSE. Plasma current emerges as the single most critical diagnostic across all architectures (+73% to +140% upon removal). Code, data, and trained checkpoints are available at https://github.com/Neerav-Gupta/tokamark-robustness.
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Chemical Physics

The application of theory of probability to the modelling of chemical kinetics systems

Maxim Nazarov

We consider a model of chemical kinetics for which the derivation of equations does not rely on the law of mass action, but is rather based on such principles as the joint probability and the geometric probability. For this model a generalisation is constructed for the case of reaction-diffusion systems in heterogeneous medium with respect to the convective and diffusive transfer of heat. The construction of this generalisation is carried out by an alternative methodology which is based fully on a systems of ordinary differential equations, without a transition to the partial derivatives. The description of this new method is a bit similar to the finite volume method, except that it uses statistical simplifying positions and geometric probability to describe the diffusion processes. Such approach allows us to greatly simplify the numerical implementation of the resulting model, as well as to simplify the quantitative analysis of it with dynamical systems theory. Moreover, the efficiency of the parallel implementation of the numerical method is increased for the resulting model. In addition, we will consider an application of this model for the description of some example reaction with quasi-periodic regime, as well as consider an algorithm for the transition from standard models with dimensional kinetic constants to its formalism.
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Classical Physics

Phase-space structure and nonlinear dynamics of a charged particle on a helicoidal manifold under a magnetic field

Abdullah Guvendi, Hassan Hassanabadi, Semra Gurtas Dogan et al. (+1 more)

We analyze the classical dynamics of a charged particle constrained to a helicoidally embedded Riemannian manifold in $\mathbb{R}^3$ under a uniform magnetic field in the ambient space. The induced metric $ds^2=du^2+(1+w^2u^2)dv^2$ and the pulled-back symmetric gauge yield an exact reduction to a one-dimensional nonlinear Hamiltonian system. The resulting effective potential couples geometry and magnetic field, producing transitions between bounded and unbounded motion and a reorganization of phase-space topology. In the asymptotic regime, the dynamics reduces to a harmonic oscillator with $\omega_{\mathrm{eff}}=\omega_c/2$ and $\ell=\sqrt{2}\,\ell_\mathcal{B}$. The system admits a Landau-type semiclassical spectrum and exhibits a geometry–magnetic control parameter $\Lambda=q\mathcal{B}+\hbar k_v w$ governing a chirality transition.
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All authors Abdullah Guvendi, Hassan Hassanabadi, Semra Gurtas Dogan, Omar Mustafa

Fluid Dynamics · Cross-listed from cs.CV

CoFINN: Conservation Flux Informed Neural Networks for Physics Problems Governed by Conservation Laws

Adnan Harun Doğan, Mert Deniz, Hande Alemdar et al. (+1 more)

We present CoFINN (Conservation Flux Informed Neural Networks), a physics-informed deep learning framework for predicting compressible flow fields governed by conservation laws. Unlike conventional data-driven convolutional neural networks (CNNs), which optimize only pixel-wise similarity metrics, CoFINN embeds finite-volume conservation physics directly into the training process. Unlike classical physics-informed methods which enforce differential-equation residuals at collocation points through automatic differentiation, CoFINN adopts a finite-volume perspective consistent with modern CFD methodology. CoFINN interprets CNN output fields as structured computational grids, where each pixel represents a finite-volume cell, and enforces conservation consistency through sophisticated numerical flux calculations. The framework is evaluated on transonic flow prediction around airfoils at (M=0.7, Re=6 * 10^6), including challenging conditions involving shock waves and high angles of attack. Results show that CoFINN improves aerodynamic force prediction accuracy, reducing drag prediction error by up to 34% at extreme angles of attack and by approximately 15% on average across the test set. Improvements are particularly significant in limited-data regimes, demonstrating that the conservation-based loss acts as an effective physical regularizer. The proposed approach maintains the computational efficiency advantages of CNN surrogates while significantly improving physical consistency and conservation behavior. The framework is architecture-agnostic and extensible to broader classes of conservation-law-governed physical systems.
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All authors Adnan Harun Doğan, Mert Deniz, Hande Alemdar, Özgür Uğraş Baran

Popular Physics

A Term-Rewriting Semantics for Pure Quantum States

Dan-Adrian German

In 2017, Terry Rudolph introduced an elementary rewriting system that relies on a representation of quantum states as misty states to accurately describe the basics of quantum circuits and quantum computation to high-school and middle-school students. The accessibility and effectiveness of the system are remarkable: every calculation can be done to good-enough accuracy, and perhaps with a small overhead, using just a tiny, universal set of gates chosen to take advantage of a remarkable mathematical result by Yaoyun Shi, leveraging another powerful result by A. Y. Kitaev. The misty formalism greatly simplifies calculations and makes them accessible to first-time learners using only simple arithmetic, and without sacrificing accuracy; it, too, is universal, inasmuch as you can use it to do any quantum calculation with maybe just a small overhead. We don't advocate that we should recast all of quantum theory into this formalism. The misty state picture is a good way of getting people to the heart of some nontrivial quantum theory without having to first absorb a huge amount of (what might initially seem largely) irrelevant math. Our argument is that the misty formalism can effectively be used to facilitate a transition to the full, conventional quantum-mathematical apparatus. To this end, we start by reviewing the original proposal, consider its strengths and limitations, and show it in action via entanglement swapping. We then extend the formalism through a new category of (irreducible) misty states acting as fixed points, and present the GHZ game in this new, general setting and representational semantics.
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History and Philosophy of Physics · Cross-listed from quant-ph

Against Totalitarianism – Introduction to Tractatus Quanticum

Jenann Ismael and Huw Price

Tractatus Quanticum (arXiv:2512.06034 [quant-ph]) is described by its authors as 'a re-editing, which takes quantum mechanics into account, of Wittgenstein's famous Tractatus.' The original Tractatus appeared with an introduction by Bertrand Russell. For Tractatus Quanticum, that role fell to us. This is the result.
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Chemical Physics

Speed limits on biomolecular processes from fundamental physical constants

Dmitrii E Makarov

Many of the timescales of life have speed limits set by quantum-mechanical constraints along with non-fundamental quantities, such as the temperature of the environment, which are however bounded by anthropic considerations. Here, some of such speed limits are examined, including those for the rates of elementary chemical reactions and biomolecular folding. Limitations of simple back-of-envelope estimates are also discussed.
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Fluid Dynamics

Near-real-time, meter-scale 3D urban wind modeling for low-altitude micrometeorology: numerical verification of a GPU-accelerated lattice Boltzmann framework

Shuai Han, Huanxia Wei, Yue Cao et al. (+8 more)

This study presents a near-real-time, meter-scale three-dimensional urban wind simulation framework for low-altitude flight events in complex urban meteorological environments. It reconstructs high-resolution wind fields by combining sparse observations with efficient microscale flow modeling. The framework integrates lattice Boltzmann method large-eddy simulation (LBM-LES), high-fidelity urban morphology reconstruction that explicitly resolves real building details, and observation-driven boundary assimilation into a rapid end-to-end pipeline for realistic urban domains. Multi-site Doppler lidar measurements from dense urban Guangzhou, China, are used for evaluation. The system reconstructs three-dimensional wind fields at 5 m resolution over kilometer-scale domains within minutes. Robustness and accuracy are tested through controlled observation reduction, independent validation against withheld lidar stations, and sensitivity analyses of grid resolution and precursor domain extent. Results show stable reproduction of vertical wind structures and key local flow features under complex morphology and limited observations, providing a scalable pathway for near-real-time urban wind reconstruction.
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All authors Shuai Han, Huanxia Wei, Yue Cao, Dalin Liu, Lin Wen, Chao Xia, Shuolin Xiao, Yingying Xing, Qing Jia, Wenguang Liang, Zhigang Yang

Medical Physics

Comparison of accuracy and efficiency of three-dimensional discrete ordinates and voxel-scoring Monte Carlo dose calculations for an $I^{125}$ seed

George M. Daskalov, Randall S.Baker, David W.O. Rogers et al. (+1 more)

This study compares the efficiency of three-dimensional deterministic discrete-ordinates calculations and voxel-based Monte Carlo simulations of the dose distribution around a model 6702 125I seed in a homogeneous water phantom. The computational efficiency of the PARTISN multigroup discrete ordinates neutral particle transport code is compared to the most efficient Monte Carlo voxel-scoring computer code available to us at the time, which was developed specifically for brachytherapy. The difficulties of such comparisons from the fact that the Monte Carlo and discrete ordinates method exhibit stochastic and systematic errors of different origins. To address this problem, we propose a parametric model for separating the systematic and stochastic error components of each method. Based on this model, a procedure for assessing efficiency of the two methods is proposed consisting of the following steps: (i)-apply the developed error model to evaluate the accuracy of each method relative to unbiased and accurate Monte Carlo point estimator calculations in the same phantom geometry; (ii)-develop systematic and stochastic error criteria which specifies equivalent accuracy of the two methods; and (iii) determine the relative efficiency of two methods by taking the ratio of CPU times required to satisfy these accuracy criteria. This process demostrates that three-dimensional PARTISN discrete ordinate simulations is a factor-of-two more efficient than our voxel-based Monte Carlo code in the 125I energy range. This suggests that discrete ordinates simulations can support efficiency competitive with that of Monte Carlo in three-dimensional geometries and can serve as an efficient and accurate dose-calculation engine in treatment planning for brachytherapy and other treatment modalities utilizing low energy photon fields.
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All authors George M. Daskalov, Randall S.Baker, David W.O. Rogers, Jeffrey F. Williamson

Fluid Dynamics

U3DWind: A Low Altitude Wind Field Dataset and Benchmark for Urban Air Mobility

Shixiong Zhou, Huanxia Wei, Chao Xia et al. (+4 more)

Urban Air Mobility (UAM) requires reliable assessment of low-altitude wind hazards, because winds, gusts, and building-induced turbulence have been recognized as critical factors affecting vehicle stability, route feasibility, vertiport siting, and airspace management. While wind-tunnel experiments, computational fluid dynamics (CFD), multiscale downscaling, reduced-order models, and UAV planning datasets have advanced wind-aware analysis, public resources for data-driven, city-scale UAM planning remain limited in geographic coverage, scenario diversity, vertical extent, building realism, and task-oriented benchmarking. To address this gap, we introduce U3DWind, a building-resolved low-altitude wind-field dataset generated using our GPU-accelerated Lattice Boltzmann Method–Large-Eddy Simulation (LBM-LES) framework for rapid urban flow simulation. U3DWind covers five megacities in China: Beijing, Shanghai, Guangzhou, Shenzhen, and Hong Kong. It contains 720 simulations, with 16 inflow directions, three reference wind speeds, and three seasonal atmospheric scenarios (annual, summer, and winter) for each city. At a 10 m grid resolution, the dataset provides three-dimensional three-component (3D3C) velocity, turbulent kinetic energy (TKE), flow density, and fluid–solid masks. To support operationally relevant evaluation, we further define five baseline tasks: wind-field prediction, sparse-sensor wind-field reconstruction, site wind-exposure ranking, airworthiness wind-compliance risk scoring, and noise propagation modeling. As a multi-city, building-resolved 3D urban wind-field dataset, U3DWind enables systematic evaluation of wind-induced impacts in low-altitude traffic scenarios and provides an open benchmark for urban airspace management and data-driven high-fidelity urban flow simulation.
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All authors Shixiong Zhou, Huanxia Wei, Chao Xia, Yingying Xing, Changmin Jiang, Hai Yang, Shuai Jia

Fluid Dynamics

Generalizable turbulence closures across bluff-body shapes by PINN-based solver-agnostic training

Zhen Zhang, Theo Käufer, Louise Ronglan et al. (+2 more)

Data-driven turbulence closures are commonly calibrated by embedding a CFD solver within an inverse procedure that requires a converged flow solution at every optimization iteration, which is fragile and costly. We instead formulate closure identification as a PDE-constrained, data-driven inverse problem within a physics-informed neural network (PINN), and subsequently stabilize the inferred closure for deployment in a numerical solver. This separation eliminates the training cost of repeated CFD solves and associated adjoint or ensemble evaluations, providing substantial flexibility to investigate alternative closure forms. Using this flexibility, we develop four closures: three model the Reynolds stress on a realizable tensor basis—a local map, a non-local model transporting the turbulent kinetic energy and recovering the out-of-plane normal stress, and the same with a learned length scale $\ell$—and a fourth models the Reynolds force $\mathbf{F}=-\nabla\cdot\boldsymbol{\tau}$ directly, free of the realizability constraint. All four are trained across six two-dimensional bluff-body wakes at $Re=10^4$ and deployed frozen in a standard finite-element solver, stabilized primarily by input-gradient smoothing. Under a strict leave-one-shape-out (LOSO) protocol, all four reduce the field errors of a steady SST $k$-$\omega$ baseline, most strongly on the mean velocity. The learned-length-scale closure is most accurate on the stress fields, while the force model generalizes best on the mean velocity and drag (LOSO drag error $\sim 7\%$). The closures also train efficiently on Particle Image Velocimetry data, enabling geometries intractable for DNS.
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All authors Zhen Zhang, Theo Käufer, Louise Ronglan, Michael S. Triantafyllou, George Em Karniadakis

Computational Physics

Hyper Boris integrators for kinetic plasma simulations and their connection to 3D rotation representations

S. Zenitani and T. N. Kato

Particle-in-cell (PIC) simulation is one of the most important research tools in theoretical plasma physics. To solve the motion of charged particles, the Boris method (a.k.a. the Boris integrator/pusher/solver) has been used for more than a half century. Although the Boris solver has good accuracy, the demand for high-accuracy numerical solvers has been increasing, and new integrators have been actively developed. In this contribution, we present novel high-accuracy particle integrators, the hyper Boris integrators, for nonrelativistic kinetic simulations. We further discuss their connection to 3D rotation representations.
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Optics

Where Does Surface $\chi^{(2)}$ Come From? A Systematic Derivation of Nonlinear Surface Susceptibilities from Bulk Nonlocal Response

Zolla Frederic

We extend the distributional framework developed in the companion paper [Zolla, arXiv:2605.15716] to the nonlinear case, focusing on the second-order ($\chi^{(2)}$) response responsible for second-harmonic generation (SHG). Starting from the most general tensorial nonlocal second-order constitutive relation and combining a spatial moment expansion with a distributional thin-layer limit, we show that the full complexity of the nonlinear interfacial response condenses, at leading order, into two scalars, the nonlinear surface susceptibilities $\chi^{(2),s}_\parallel$ and $\chi^{(2),s}_\perp$, associated with the tangential and normal components of the electric field, respectively. A key structural result is established: via a marginal integration over one field argument, the nonlinear surface problem reduces recursively to an effective linear one, whose surface susceptibility is determined by the bulk nonlinear kernel alone. Generalized nonlinear Maxwell boundary conditions are derived explicitly for planar and spherical interfaces, and curvature corrections are obtained systematically. The formalism is illustrated on Gaussian, Yukawa, and tensorial Lorentz kernels.
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Medical Physics

PTCOG Treatment Efficiency Subcommittee Risk Assessment Report on Patient-Specific Quality Assurance

Frank Emert, Christina Vallhagen Dahlgren, Chin-Cheng Chen et al. (+3 more)

Patient-specific quality assurance (PSQA) in pencil beam scanning proton therapy (PBS-PT) is often treated as a purely technical verification task. This PTCOG Treatment Efficiency Subcommittee White Paper instead frames PSQA as a workflow-embedded risk-control strategy and asks how different PSQA approaches reshape the same clinical risk landscape. Using a generic PBS-PT process-driven Failure Mode and Effects Analysis (pFMEA), 44 validated PSQA-relevant failure modes across 20 process steps were scored under a common no-PSQA baseline and three PSQA pathways: measurement-based PSQA, log file-based PSQA, and independent secondary dose calculation. A staged mathematical formalism separates preparatory data-stage effects, method-specific full-stage verification, cumulative endstate effects, and a Data-to-Cum bridge that quantifies additional verification benefit on the baseline scale. In this expert-scored, baseline-anchored model, log file-based PSQA produced the largest cumulative workflow-level risk-score reduction, followed by measurement-based PSQA and independent secondary dose calculation. The ranking is not a winner-takes-all rule or probability-calibrated risk estimate; instead, each method shows distinct risk-control strengths in different workflow regions. The White Paper therefore supports a risk-informed hybrid PSQA architecture, where log file-based PSQA, measurement-based PSQA, and independent secondary dose calculation are assigned to the workflow segments in which their signatures are strongest. It provides a transparent, semi-quantitative, stage-resolved framework for institutions seeking to evaluate, implement, or evolve PSQA in PBS-PT and emphasizes that log file-based PSQA must itself be supported by validated and governed log data and treatment records.
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All authors Frank Emert, Christina Vallhagen Dahlgren, Chin-Cheng Chen, Heng Li, Jay Flanz, Daniel Robertson