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Physics Physics

physics Watch Physics

Wednesday 4 February

Medical Physics

Angle-Dependent Dose Transformer Algorithm for Fast Proton Therapy Dose Calculations

Mikołaj Stryja, Danny Lathouwers and Zoltán Perkó

Monte Carlo (MC) dose engines are the gold standard for pencil beam scanning proton therapy, but their computational cost limits adaptive workflows and other tasks requiring repeated dose recalculation. Existing deep learning beamlet dose engines require each beamlet to be sampled in its beam's-eye-view, which implies a local resampling of the computed tomography (CT) patch for every beamlet. We propose the Angle-dependent Dose Transformer Algorithm (ADoTA), which adds an input channel encoding a fast analytical beamlet shape projection that supplies the beam direction explicitly. The CT is resampled only once per irradiation field to an isotropic $2\times2\times2$ mm$^3$ grid, and each beamlet is obtained by cropping this shared grid. The model was trained on CT data from 108 patients for initial energies of 70 to 150 MeV (thoracic) and 70 to 270 MeV (abdominal/pelvic) and evaluated on an independent test cohort of 50 patients. ADoTA achieved $\Gamma(1\%,3\,\mathrm{mm},0.1\%)$ pass rates of $99.40\pm0.86\%$ (thoracic) and $99.87\pm0.23\%$ (abdominal/pelvic), with an effective batched throughput of $3.14\pm0.31$ ms per beamlet. In a 5640-spot prostate plan benchmark with both routes optimized on the GPU, the analytical projection reduced the plan's direction-encoding cost by $9.6\times$ and its total compute time by $1.48\times$ compared with local beam's-eye-view sampling. For full treatment plans, $\Gamma(2\%,2\,\mathrm{mm},10\%)$ pass rates averaged 95.1% (lung) and 96.1% (prostate), while the target $D_{98\%}$ was underestimated by 1.8 to 2.5% of prescription in prostate plans and 4.2 to 5.9% in thoracic plans. ADoTA achieves close to MC accuracy at the beamlet level without beamlet-specific resampling, with a clinically relevant target underdosage in thoracic plans that should be resolved before clinical use.
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Chemical Physics · Cross-listed from astro-ph.GA

Atom Addition Formation of Thionylimide (HNSO) on Interstellar Dust Grains: Chemical routes requiring oxygen and nitrogen atom surface diffusion

Juan Carlos del Valle, Miguel Sanz-Novo, Johannes Kästner et al. (+4 more)

We investigate the formation of the recently detected HNSO molecule using quantum chemical calculations on ices and astrochemical models in tandem. Our results indicate that HNSO is efficiently produced on grain surfaces through reactions involving atomic oxygen and nitrogen atoms with the radicals NS and SO, forming NSO as a key intermediate. Subsequent hydrogenation of NSO leads to HNSO, with a clear preference for the lowest energy cis conformer, while the trans form is metastable and may be short-lived under typical interstellar conditions. The models predict that solid HNSO can reach abundances comparable to icy OCS, placing it among the major sulfur-bearing species in interstellar ices. Gas-phase abundances, in contrast, remain lower than those of OCS. The implementation of a multibinding scheme in our models clarifies the role of diffusive chemistry in the production of HNSO at early times, improving agreement with observations. These findings suggest that reactions involving diffusing O and N atoms on icy grains contribute significantly to sulfur chemistry and beyond in dense clouds and motivate further searches for molecules containing simultaneously H, N, O and S in other astronomical environments.
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All authors Juan Carlos del Valle, Miguel Sanz-Novo, Johannes Kästner, Kenji Furuya, Víctor M. Rivilla, Rafael Martín-Domńech, Germán Molpeceres

Chemical Physics · Cross-listed from cond-mat.mtrl-sci

Revealing the interfacial kinetic mechanisms in high-entropy doped Na$_3$V$_2$(PO$_4$)$_3$ through electrochemical investigation and distribution of relaxation times

Manish Kr. Singh and Rajendra S. Dhaka

We designed a high-entropy doped NASICON cathode, Na$_3$V$_{1.9}$(CrMoAlZrNi)$_{0.1}$(PO$_4$)$_3$ and investigate its electrochemical performance for sodium-ion batteries (SIBs) to understand the diffusion mechanism including distribution of relaxation times analysis of interfacial kinetics. This trace doping induces high-entropy mixing at the vanadium site, tuning the lattice and enhancing specific capacity, activating V$^{4+}$/V$^{5+}$ redox couple 3.95~V. Interestingly, it delivers a reversible capacity of 119~mAh~g$^{-1}$ at 0.1~C, and demonstrate excellent stability of 68% after 1000 cycles at 10~C. The calculated diffusion coefficient values are found within the range of \(10^{-11}\)–\(10^{-13}~\mathrm{cm^2\,s^{-1}}\). The systematic investigation of temperature and voltage-dependent impedance data using the distribution of relaxation times provides deeper insights into the underlying charge-transfer and transport processes. The full cells with hard carbon delivers 326~Wh~kg$^{-1}$ (with respect to cathode mass) at $\approx$3.2~V and retained $\sim$79% capacity after 100 cycles at 2~C. Our study opens new avenues for developing high-entropy doped cathodes for enhanced structural stability, extended redox activity, and optimized electrochemical kinetics for practical implementation of SIBs.
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Plasma Physics

Improved Fluid Modeling of Space Debris Generated Ion-Acoustic Precursor Solitons

Ajaz Mir, Abhijit Sen, Pintu Bandyopadhyay et al. (+3 more)

This study reexamines the excitation of ion-acoustic precursor solitons by a supersonically moving charged debris object, incorporating two previously overlooked physical factors: the dynamic charging of the debris and the impermeable nature of its surface. The influence of charging dynamics is explored using an enhanced one-dimensional fluid-Poisson model, where the source charge is treated as a dynamical variable and solved self-consistently alongside the core plasma equations. By comparing these results with prior fixed-charge models, we evaluate the effects on soliton onset and propagation, finding that charging dynamics does not hinder soliton generation or evolution. To assess the impact of the impermeability of debris surface, a two-dimensional fluid model simulates the interaction between an electrostatically biased, impenetrable object and a flowing plasma. Modeling the object as an infinite wall disconnects the upstream and downstream plasma regions, forming a sheath without solitons – consistent with earlier fluid and particle-in-cell simulations. However, replacing the wall with a finite object enables plasma flow around it, restoring upstream-downstream connectivity and naturally generating precursor solitons.
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All authors Ajaz Mir, Abhijit Sen, Pintu Bandyopadhyay, Sanat Tiwari, Chris Crabtree, Gurudas Ganguli

Optics

Impact of aberrations in SLM-based far-field holography

Markus Zimmermann, Andreas Brenner, Tobias Haist et al. (+1 more)

We use camera-in-the-loop calibration to calibrate a phase-only spatial light modulator (SLM) in a far-field hologram setup. The recorded intensity distributions achieve a high degree of consistency with the calculated results, indicating a precise calibration and sufficient modeling of the most prominent aberrations. In this work, we discuss the modeled aberrations and examine the improvement or loss in image quality and diffraction efficiency that is obtained by including or excluding the modeled aberrations in the calibration. We further show the influence of aberrations on speckle-reduced holograms and evaluate the speckle contrast.
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All authors Markus Zimmermann, Andreas Brenner, Tobias Haist, Stephan Reichelt

Optics · Cross-listed from cond-mat.mes-hall

Coherent electronic Raman excitation of valley-orbit split states of phosphorus dopants in silicon

Adam Gindl, Martin Čmel, František Trojánek et al. (+4 more)

In this study, we demonstrate coherent optical excitation of the electronic Raman transition between the $1s\left(A_1\right)$ and $1s\left(E\right)$ split states of phosphorus donor in crystalline silicon. The dynamics of the generated wavepacket is characterized in the time domain using a degenerate pump-probe technique with mid-infrared femtosecond pulses via transient polarization anisotropy of the probe pulse. In addition, we study the role of resonantly excited carriers, and we show that the amplitude and coherence time of the electronic wavepacket depend on the pre-excited carrier density. Further, we demonstrate that under certain conditions, the Raman-type excitation changes to displacive impulsive excitation, which allows us to address the Raman-forbidden transition between $1s\left(A_1\right)$ and $1s\left(T_1\right)$.
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All authors Adam Gindl, Martin Čmel, František Trojánek, Petr Malý, Zbyněk Šobáň, Alexandr Pošta, Martin Kozák

Optics

Massive coherent equipartition of light by the geometric phase of null space

Xiangrui Hou, Dongyi Wang, Fangyu Wang et al. (+3 more)

Light source is a foundational to photonic science and technology. However, a significant challenge remains in generating and distributing coherent light from a single on-chip source with high phase stability across multiple channels. Integrated lasers typically operate independently, and conventional splitters (e.g., multi-mode interferometers) do not guarantee the phase coherence required for advanced applications. Here, we report a purely geometric scheme for achieving massive equipartition of coherent light on a photonic chip by leveraging the geometric phases of a null space spanned by degenerate states with zero eigenvalue. The evolution of the null space maps to real-space rotation described by the special orthogonal group SO(N), thus enabling precise and scalable control over light distribution by engineering the system parameters. We experimentally realize up to one-to-nine equipartition of light on a waveguide array fabricated on a glass-based photonic chip. The framework can be upscaled for one-to-N light distribution. This work establishes a versatile and scalable platform for integrated coherent light sources, paving the way for integrated photonic applications such as quantum photonics and optical computing.
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All authors Xiangrui Hou, Dongyi Wang, Fangyu Wang, Congwei Lu, Zhaoju Yang, Guancong Ma

Chemical Physics · Cross-listed from quant-ph

Constructing Compact ADAPT Unitary Coupled-Cluster Ansatz with Parameter-Based Criterion

Runhong He, Xin Hong, Qiaozhen Chai et al. (+5 more)

The adaptive derivative-assembled pseudo-trotter variational quantum eigensolver (ADAPT-VQE) is a promising hybrid quantum-classical algorithm for molecular ground state energy calculation, yet its practical scalability is hampered by redundant excitation operators and excessive measurement costs. To address these challenges, we propose Param-ADAPT-VQE, a novel improved algorithm that selects excitation operators based on a parameter-based criterion instead of the traditional gradient-based metric. This strategy effectively eludes redundant operators. We further develop a sub-Hamiltonian technique and integrate a hot-start VQE optimization strategy, achieving a significant reduction in measurement costs. Numerical experiments on typical molecular systems demonstrate that Param-ADAPT-VQE outperforms the original ADAPT-VQE in computational accuracy, ansatz size, and measurement costs. Furthermore, our scheme retains the fundamental framework of ADAPT-VQE and is thus fully compatible with its various modified versions, enabling further performance improvements in specific aspects. This work presents an efficient and scalable enhancement to ADAPT-VQE, mitigating the core obstacles that impede its practical implementation in the field of molecular quantum chemistry.
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All authors Runhong He, Xin Hong, Qiaozhen Chai, Ji Guan, Junyuan Zhou, Arapat Ablimit, Guolong Cui, Shenggang Ying

Chemical Physics

Consistent GMTKN55 and molecular-crystal accuracy using minimally empirical DFT with XDM(Z) dispersion

Kyle R. Bryenton and Erin R. Johnson

Density-functional theory (DFT) has become the workhorse of modern computational chemistry, with dispersion corrections such as the exchange-hole dipole moment (XDM) model playing a key role in high-accuracy modelling of large-scale systems. All previous production implementations of XDM have used the two-parameter Becke–Johnson damping function based on atomic radii. Here, we introduce and implement a new XDM variant that uses a one-parameter damping function based on atomic numbers, recently proposed by Becke. Both this new Z damping and the canonical BJ-damping variants of XDM are benchmarked on the comprehensive GMTKN55 database using minimally empirical generalised-gradient-approximation, global hybrid, and range-separated hybrid functionals. This marks the first time that the XDM (and many-body dispersion, MBD) corrections have been tested on the GMTKN55 set. Using the new WTMAD-4 metric, an outlier analysis is performed for all new data, as well as for top-ranking functionals from the literature at each rung, providing insight into both performance and consistency across the dataset. We also extended our analysis to the DM21 and Skala machine-learned functionals that have garnered recent attention. To test Z damping's transferability to the solid state, four benchmarks involving molecular crystals are also considered. Across these molecular and solid-state benchmarks, the revPBE0 and B86bPBE0 hybrid functionals, paired with the Z-damped XDM variant, show excellent performance.
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Optics

A universal waveguide mass–energy relation for lossy one-dimensional waves in nature

Huayang Cai and Bishuang Chen

Finite, lossy waveguides are ubiquitous: distributed attenuation with partial reflections produces feedback, resonance, delays and decay across electromagnetic, acoustic, photonic, quantum-transport and electrochemical interfaces. Yet standard impedance/scattering tools and weak-loss resonator approximations do not provide low-dimensional invariants that remain predictive under intrinsic asymmetry and realistic boundaries, nor do they cleanly separate total absorption from useful power delivered to a load. Here we develop a unified mass–energy framework for linear, single-mode, one-dimensional systems, in which energy-like and power-flow variables $(\mathcal{U},\mathcal{S})$ satisfy the universal invariant $\mathcal{U}^2-\mathcal{S}^2=|\Gamma_g|^2$, with the effective standing-wave “mass” $|\Gamma_g|$ becoming state-dependent under asymmetry. The Cai–Smith chart gives a bounded state-space map of stability, feedback proximity and operating states, and makes explicit the divergence between maximum absorption and useful delivery under loss. We derive four laws governing absorption and emission, validate the invariant in multiport optics via coherent perfect absorption at exceptional points using a basis-independent SVD criterion (reconciling quadratic vs quartic near-zero scaling), and map electrochemical polarization onto the same geometry by extracting $|\Gamma_g|$ from two-mode orthogonal fits to reveal a universal storage-to-transfer transition across pH. This framework provides a transferable design language for dissipative, boundary-controlled systems.
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Applied Physics · Cross-listed from cond-mat.mes-hall

Observation of Orbit-Orbit Torques: Highly Efficient Torques on Orbital Moments Induced by Orbital Currents

Hongyu Chen, Han Yan, Xiaorong Zhou et al. (+10 more)

We study the current-induced torques in bilayers composed of a light 3d metal, chromium, and a rare-earth ferromagnet with finite orbital moments, terbium, utilizing second-harmonic Hall-response measurements. The dampinglike torque efficiency of chromium is found to be positive and reaches ~3.66 in this system, in sharp contrast to the negative and subtle dampinglike torque efficiency in general Cr/ferromagnet heterostructures with quenched orbital moment. We suggest that the orbital currents generated by the orbital Hall effect in Cr can be injected into Tb with negligible loss at the interface and then efficiently interact with the orbital moments. We term such an exotic effect as the orbit-orbit torque (OOT). Our work implies that orbital currents could be harnessed to manipulate the orbital magnetization of materials, which would advance the development of orbitronics.
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All authors Hongyu Chen, Han Yan, Xiaorong Zhou, Xiaoning Wang, Ziang Meng, Li Liu, Guojian Zhao, Zhiyuan Duan, Sixu Jiang, Jingyu Li, Xiaoyang Tan, Peixin Qin, Zhiqi Liu

Chemical Physics

Probing the structure of cyclic hydrocarbon molecules with X-ray-induced Coulomb explosion imaging

Kurtis D. Borne, Rebecca Boll, Thomas M. Baumann et al. (+27 more)

Coulomb explosion imaging (CEI) is a powerful experimental technique that maps a molecule's geometric structure onto the momenta of ionic molecular fragments produced by rapid multiple ionization. Here, we apply CEI induced by pulses from an X-ray free-electron laser in order to image and distinguish complex hydrocarbon isomers with the chemical formula C7H8: toluene, cycloheptatriene, and 1,6-heptadiyne. The measured fragment-ion momentum distributions show discernible differences between the three isomers and provide signatures of specific carbon and hydrogen sites in the molecule. In contrast to previous work, we demonstrate that distinct 'marker atoms' are not strictly required for constructing a meaningful molecular frame of reference for the interpretation of the momentum-space data. Our work paves the way for tracking the ultrafast motion of nuclei during isomerization reactions in pure hydrocarbons.
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All authors Kurtis D. Borne, Rebecca Boll, Thomas M. Baumann, Surjendu Bhattacharyya, Martin Centurion, Keyu Chen, Benjamin Erk, Alberto De Fanis, Ruaridh Forbes, Markus Ilchen, Edwin Kukk, Huynh V. S. Lam, Xiang Li, Lingyu Ma, Tommaso Mazza, Michael Meyer, Terence Mullins, J. Pedro F. Nunes, Asami Odate, Shashank Pathak, Daniel Rivas, Philipp Schmidt, Florian Trinter, Sergey Usenko, Anbu S. Venkatachalam, Enliang Wang, Peter M. Weber, Till Jahnke, Artem Rudenko, Daniel Rolles

Computational Physics · Cross-listed from cs.LG

Generative Neural Operators through Diffusion Last Layer

Sungwon Park, Anthony Zhou, Hongjoong Kim et al. (+1 more)

Neural operators provide a powerful framework for learning discretization invariant mappings between function spaces, but standard deterministic models do not capture predictive uncertainty. We introduce diffusion last layer (DLL), a modular probabilistic output head for neural operator backbones. DLL represents target fields through an input dependent low rank expansion inspired by the Karhunen-Loéve expansion and learns a conditional diffusion model over the corresponding coefficient space. This design enables efficient distributional modeling while preserving the structural advantages of operator learning. On stochastic PDE benchmarks with random forcing, DLL achieves strong distributional fidelity and performs competitively with pixel space and conventional latent diffusion baselines. In deterministic long horizon rollout tasks, DLL improves rollout stability over the underlying backbone and provides useful estimates of predictive uncertainty under compounding autoregressive errors. These results suggest that diffusion modeling in learned coefficient spaces offers a practical route to uncertainty aware neural operators.
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All authors Sungwon Park, Anthony Zhou, Hongjoong Kim, Amir Barati Farimani

Optics

Advanced Superdirective Antennas

Alex Krasnok

Superdirective (supergain) antennas aim to produce a narrow main beam from radiators that are electrically small compared with the wavelength. Instead of enlarging the physical aperture, they rely on strongly coupled currents, near-field energy storage, and controlled modal interference so that a compact structure radiates with enhanced directivity. This review emphasizes link-relevant evaluation and reporting: realized gain referenced to a stated impedance plane, clearly stated bandwidth definitions (impedance and performance), and robustness to fabrication spread and platform/environmental loading. Two practical implementation routes are surveyed. The first uses resonant, tightly coupled arrays, including fully driven arrays and single-chain designs based on parasitic or reactively loaded elements. The second uses single-body radiators that enforce a targeted mixture of multipoles or resonant/characteristic modes with one or a few feeds, including symmetry-broken dielectric resonators and mixed electric–magnetic designs. Across RF, microwave, and optical regimes, the same penalties recur as superdirectivity is pushed: reduced radiation resistance, rapid impedance variation, narrow usable bandwidth, and strong sensitivity to small perturbations. Beyond geometric synthesis and multi-resonant stacking, the review highlights emerging levers that can shift these trade-offs in specific system contexts: low-loss materials and cryogenic operation to improve efficiency and frequency stability, and time-varying loading and matching (Floquet/parametric approaches) that can relax linear time-invariant bandwidth constraints, at the cost of added control complexity and spectral conversion.
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Tuesday 3 February

Physics and Society · Cross-listed from econ.GN

Localisation and Circularity in Apple Supply Chains: An Algorithmic Exploration

Baraa Alabdulwahab and Ruzanna Chitchyan

Localisation and circularity in perishable food supply chains are essential for sustainability. Poor allocation of time-sensitive food leads to waste, higher transport emissions, and unnecessary long-distance sourcing. Algorithms used in digital trading platforms and allocation systems can help address these problems by improving how local supply is matched with demand under real operational constraints. This paper examines localisation and circularity in the UK apple supply chain. Apples are an informative case because they are perishable, consumed fresh as dessert fruit, used as inputs across multiple food industries, and generate valuable by-products. We present a weighted-sum mixed-integer linear programming formulation for supply-demand allocation. The model encodes a single global objective with explicit weights on four operational criteria: price matching, quantity alignment, freshness requirements, and geographic distance. These weights make priorities explicit and adjustable, enabling transparent balancing between economic and sustainability considerations. The framework also supports the circulation of unallocated supply across allocation cycles. Using a realistic apple supply-demand dataset, we evaluate allocation outcomes under different priority settings. Results indicate that allocation outcomes are strongly shaped by both priority settings and the structure of the underlying supply network characteristics.
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Optics

Non-Hermitian Complex Coupling for Magnetic Resonance Imaging

Zheng Siyong, Wu Maopeng, Chi Zhonghai et al. (+4 more)

Strong coupling in wave-based systems often causes level repulsion, leading to mode splitting and reduced response at the target frequency. This problem is pronounced in magnetic resonance imaging (MRI), where strong mutual inductance between a receive coil (RC) and a metamaterial (MM) degrades B1 performance. Here, we introduce a non-Hermitian complex-coupling decoupling strategy based on a dual-resonator model. By engineering a phase delay in the coupling pathway, an imaginary coupling component is created, driving the system from the PT-symmetric to the anti-PT-symmetric phase and enabling eigenmode degeneracy without added dissipation. Implemented through a high-permittivity ceramic layer, this mechanism restores single-mode resonance in the MM - RC system and suppresses frequency splitting. Simulations show a ~14-fold B1 enhancement compared with the strongly repulsive regime. This passive, compact, and hardware-compatible approach offers a general route for coupling control in electromagnetic, acoustic, optical, and quantum systems.
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All authors Zheng Siyong, Wu Maopeng, Chi Zhonghai, Chen Zewen, Lim Chwee Teck, Zhao Qian, Zhou Ji

Applied Physics

A Multi-Fidelity Bayesian Neural Operator for Mechanics of Spinodal Metamaterial

Pu You, Hongshun Chen, Bahador Bahmani et al. (+1 more)

Cellular metamaterials offer a vast design space for tailoring nonlinear mechanical responses, yet exploring this space with conventional modeling approaches is often infeasible or not scalable. To fully exploit their nonlinear behavior for inverse design, it is essential to learn the full stress-strain response rather than relying on bulk quantities, motivating the use of neural operators for function-to-function mapping. However, data-driven modeling of nonlinear response for metamaterials is severely constrained by the limited availability of costly experimental data. Here, we propose a Bayesian multi-fidelity deep operator network that aggregates abundant low-fidelity finite element simulations with sparse high-fidelity experimental data from in-situ nanomechanical experiments on spinodal metamaterials, enabling heterogeneous information aggregation. A hybrid Bayesian active learning strategy is introduced to select informative samples by jointly maximizing epistemic uncertainty and geometric diversity of the microstructure, substantially reducing the cost of 3D nonlinear simulations. This approach adaptively trains the low-fidelity operator, which is then augmented by a high-fidelity Bayesian residual learner. We demonstrate that only 22 strategically selected samples from a design pool of 3000 are sufficient to achieve an 84.1 percent reduction in MSE compared to the high-fidelity baseline. The framework significantly outperforms single-fidelity baselines, providing superior predictions for full nonlinear stress-strain responses as well as stiffness, strength, and energy absorption. This work provides a robust, data-efficient pathway for the inverse design and constitutive modeling of cellular metamaterials.
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All authors Pu You, Hongshun Chen, Bahador Bahmani, Horacio D. Espinosa

Physics and Society

Fiscal Dynamics in Japan under Demographic Pressure

Goshi Aoki

Japan's population is shrinking, the share of working-age people is falling, and the number of elderly is growing fast. These trends squeeze public finances from both sides–fewer people paying taxes and more people drawing on pensions and healthcare. Policy discussions often focus on one fix at a time, such as raising taxes, reforming pensions, or boosting productivity. However, these levers interact with each other through feedback loops and time delays that are not yet well understood. This study builds and calibrates an integrated system dynamics model that connects demographics, labor supply, economic output, and public finance to explore two questions: (RQ1) What feedback structure links demographic change to fiscal outcomes, and how do different policy levers work through that structure? (RQ2) Which combinations of policies can stabilize key fiscal indicators within a meaningful timeframe? The model, grounded in official statistics, tracks historical trends reasonably well. Policy experiments show that productivity improvements and controlling per-person costs offer the most effective near-term relief, because they act quickly through revenue and spending channels. In contrast, raising fertility actually worsens the fiscal picture in the medium term, since it takes decades for newborns to grow up and join the workforce. A combined scenario pairing moderate productivity gains with moderate cost control nearly eliminates the deficit by 2050. These findings underscore the importance of timing when evaluating demographic policy. Stabilizing finances within a practical timeframe requires levers that improve the budget directly, rather than those that work through slow demographic channels. The model serves as a transparent testing ground for designing time-aware fiscal policy packages in aging, high-debt economies.
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Geophysics

Investigation on the inclination angle of undrained shear slip surface in saturated soils based on mixture theory

Hu Yayuan and Zhou Jian

The inclination angle of the undrained shear slip surface in saturated soils is analyzed based on mixture theory. First, starting from the property that the bulk strain of soil skeleton is equal to the flow ratio of water discharged from soil skeleton, the energy conservation equation of saturated soil is obtained. According to state variables of energy equation and non-equilibrium thermodynamics, the mechanical mechanism underlying effective stress principle is revealed that Gibbs free energy of saturated soil is only expressed as a function of effective stress under isothermal process. Consequently, the deformation and strength of saturated soil are uniquely determined by the effective stress and not directly related to Newton's equilibrium equations, which governs the movements of solid-fluid two-phase components. The instability of soil skeleton is related to the applied forces and requires analysis based on the Newtonian equilibrium condition. The interaction between the solid-water components and the equilibrium equation of solid component are investigated under two working conditions: when permeability tensor equals zero and when it equals infinity. Combined with the Mohr-Coulomb strength theory, the inclination angle of the undrained shear slip surface is explored under Rankine's passive earth pressure in saturated soil. The results indicate that, when the permeability equals infinity, the uncoupled hydro-mechanical analysis is recommended, and the inclination angle of slip surface is ; when the permeability equals zero, the fully coupled hydro-mechanical analysis is recommended, and the inclination angle of slip surface is . The permeability of actual saturated soil falls between the two extremes, the inclination angle of slip surface must be analyzed on a case-by-case basis.
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Computational Physics · Cross-listed from cs.LG

Lagged backward-compatible physics-informed neural networks for unsaturated soil consolidation analysis

Dong Li, Shuai Huang, Yapeng Cao et al. (+3 more)

This study develops a Lagged Backward-Compatible Physics-Informed Neural Network (LBC-PINN) for simulating and inverting one-dimensional unsaturated soil consolidation under long-term loading. To address the challenges of coupled air and water pressure dissipation across multi-scale time domains, the framework integrates logarithmic time segmentation, lagged compatibility loss enforcement, and segment-wise transfer learning. In forward analysis, the LBC-PINN with recommended segmentation schemes accurately predicts pore air and pore water pressure evolution. Model predictions are validated against finite element method (FEM) results, with mean absolute errors below 1e-2 for time durations up to 1e10 seconds. A simplified segmentation strategy based on the characteristic air-phase dissipation time improves computational efficiency while preserving predictive accuracy. Sensitivity analyses confirm the robustness of the framework across air-to-water permeability ratios ranging from 1e-3 to 1e3.
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All authors Dong Li, Shuai Huang, Yapeng Cao, Yujun Cui, Xiaobin Wei, Hongtao Cao

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

Entangled criticality and irreversibility in random Markov dynamics

Faheem Mosam and Eric De Giuli

We introduce a two-parameter ensemble of random discrete-time Markov models that simultaneously captures critical slowing down and broken detailed balance. Extending a previously studied heterogeneous Markov ensemble, we incorporate correlations between forward and backward transition rates through a single asymmetry parameter $\gamma$, while heterogeneity is controlled by $\epsilon$. Using results from random matrix theory, we identify a critical locus $\epsilon_c(\gamma,N)$ at which relaxation times diverge and spectral universality breaks down, in Markov models with $N$ states. We characterize the behavior of entropy production, predictive information, and relaxation dynamics across the ensemble, showing that many observables depend strongly on heterogeneity but only weakly on asymmetry, except near the symmetric limit. Applying maximum-likelihood inference to human fMRI and EEG data, we find that both modalities operate near the predicted critical locus and occupy a similar region of the $\epsilon-\gamma$ plane, supporting a super-universality of human brain dynamics. While ensemble averages are well captured by the null model, empirical data exhibit substantially enhanced variability, indicating subject-specific structure beyond random expectations. Our results unify criticality and nonequilibrium measures within a single framework and clarify their intertwined role in the analysis of complex biological dynamics.
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Physics and Society

Coalition Structure and Polarization in Parliamentary Voting Networks: Evidence from the Italian Parliament

Francesca Collu, Antonio Scala and Emilia La Nave

Ensuring legislative accountability in multi-party systems requires quantitative tools that reveal actual voting behavior beyond formal party affiliations. We present a network-based framework for analyzing parliamentary dynamics at multiple scales, capturing coalition structure, group coherence, and individual influence. Applied to over 4 million vote expressions from the Italian Parliament across three government formations (2018-2021), the methodology combines network modularity, voting distance metrics, and betweenness centrality to map the structure of collective decision-making. Using this framework, we show that system-level polarization, as captured by network modularity, varies systematically with coalition structure rather than coalition size. Technical governments display paradoxically lower global polarization despite broader formal support, reflecting structurally mixed voting patterns rather than unified blocs. On polarizing issues such as immigration, network polarization depends strongly on the fragmentation or cohesion of the opposition, even when the governing coalition votes cohesively. Betweenness analysis reveals that mediator roles are highly concentrated, with only about 2% of parliamentarians acting as structural bridges between communities. Community detection further uncovers implicit coalitions that are not apparent from declared alliances. The framework relies exclusively on public roll-call data, enabling reproducible analysis and direct applicability to any legislature with transparent voting records. By linking individual voting behavior to emergent system-level structure, the methodology provides quantitative infrastructure for comparative analysis of legislative voting networks and coalition monitoring, enabling systematic assessment of legislative behaviour.
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Chemical Physics

Rethinking Balance Sheets: A Poisson-Nernst-Planck Based Approach for Modeling Concentration and Flux Profiles Inside an Electrochemical Cell

Grace Origer, Ritu R. Raj, Nathan Jarvey et al. (+3 more)

Electrochemical cells serve as a building block for producing and storing electrical energy from chemical reactions. The analysis of ion transport in these systems forms the foundation for understanding more complex electrochemical systems that are becoming increasingly present in the broader societal energy infrastructure. From a pedagogical perspective, the “balance sheets" introduced in Chapter 4 of Electrochemical Methods: Fundamentals and Applications by Alan J. Bard, Larry R. Faulkner and Henry S. White (hereafter referred to as BFW) provides a first-pass approach to analyze ion transport in electrochemical cells. However, the balance sheet approach lacks first-principles justifications from the underlying equations that describe the transport processes in electrochemical cells. In this work, we compare a first-principles approach via the Poisson-Nernst-Planck equations to describe ion transport in electrochemical cells to that of the balance sheet approach. By re-working the examples presented in BFW, we illustrate that the balance sheet approach is only valid in limited scenarios. Furthermore, we show that the PNP equations provide a more physical route to analyze ion transport in electrochemical systems. We hope the approach outlined here will be adopted by electrochemical engineering researchers and instructors.
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All authors Grace Origer, Ritu R. Raj, Nathan Jarvey, P. N. Romero Zavala, Wilson A. Smith, Ankur Gupta

Atmospheric and Oceanic Physics

Data Driven Air Entrainment Velocity Parameterization by Breaking Waves

Xiaohui Zhou, Anton S. Darmenov and Kianoosh Yousefi

Wave breaking injects turbulence and bubbles into the upper ocean, modulating air-sea exchange of momentum, heat, gases, and sea-spray aerosols. These fluxes depend nonlinearly on sea state but remain poorly represented in coupled atmosphere-wave-ocean models, where air-entrainment velocity is often parameterized using wind speed or significant wave height alone. We develop a global machine-learning parameterization of Va trained on a 43-year WAVEWATCH III simulation that resolves the breaker-front distribution and associated energetics. A multilayer perceptron with seven physically motivated predictors (wind speed, wave height, wave age, steepness, direction, and depth) reproduces spectral-reference Va with high skill. The model reduces longstanding biases in bulk formulas, notably overestimation in swell-dominated low latitudes and underestimation in storm tracks. Applied globally, it improves bubble-mediated CO2 transfer velocity and sea-salt aerosol emission, reducing errors by an order of magnitude. Validation against independent HiWinGS observations supports robust deep-water performance.
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Plasma Physics

Eigenmodes in an ultra-relativistic ultra-magnetized pair QED-plasma

Ryan T. Low and Mikhail V. Medvedev

Ultra-relativistic quantum-electrodynamic (QED) plasmas, characterized by magnetic field strengths approaching and even exceeding the Schwinger field of approximately $B_{Q} \approx 4 \times 10^{13}$ gauss, hold significant interest for laser-plasma experiments and astrophysical observations of neutron stars and magnetars. In this study, we investigate the joint modification of normal plasma modes in ultra-relativistic electron-positron plasmas, both charge neutral and non-neutral, by the super-strong magnetic field and plasma relativistic temperature. Our analysis shows that the most substantial modification concerns the reduction of the plasma frequency cutoff, resulting in relativistic and field-induced transparency. Additionally, we observe a temperature-independent modification of the index of refraction of electromagnetic waves, which coincides with the behavior observed in a cold QED plasma.
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Computational Physics

At the Top of the Mountain, the World can Look Boltzmann-Like: Sampling Dynamics of Noisy Double-Well Systems

Abir Hasan and Nikhil Shukla

The success of the transistor as the cornerstone of digital computation motivates analogous efforts to identify an equivalent hardware primitive, the probabilistic bit or p-bit, for the emerging paradigm of probabilistic computing. Here, we uncover a fundamental ubiquity in the stochastic dynamics of double well energy systems when initialized near the barrier top. Using a topological framework grounded in Morse theory and singularity theory, we make use of the result that all smooth, even double well potentials reduce near the saddle point to a canonical quartic normal form. Within this regime, the interplay of noise, synaptic bias, and potential curvature produces a topologically robust short time evolution characterized by a tanh like response. This enables Boltzmann like sampling that is largely independent of the detailed shape of the potential, apart from its effective temperature scaling. Analytical derivations and numerical simulations across multiple representative systems corroborate this behavior. Our work provides a unifying foundation for assessing and engineering a broad class of physical platforms, including oscillators, bistable latches, and magnetic devices, as p-bits operating within a synchronous framework for stochastic sampling and probabilistic computation.
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Space Physics

Effects of the May 2024 Solar Storm on the Earth's Radiation Belts Observed by CALET on the International Space Station

Anthony Ficklin, Alessandro Bruno, Lauren Blum et al. (+6 more)

In May 2024, extraordinary solar activity triggered a powerful solar storm, impacting Earth and producing the extreme geomagnetic storm of May 10-11, the most intense since 2003. This had significant effects on the magnetosphere, leading to the creation of a new long-lasting component of relativistic electrons and to flux changes in the South-Atlantic Anomaly. Here we present radiation-belt observations made by the Calorimetric Electron Telescope (CALET) on the International Space Station. Specifically, we took advantage of the count rates from three layers of the CALET charge detector and imaging calorimeter. We show that the new electron storage ring extended to energies in the multi-MeV range and down to L=2.2, well below the nominal slot-region barrier of L=2.8, and persisted for several months, depending on energy. The evolution of the new radiation-belt configuration over time was characterized by estimating the decay rates as a function of energy and L.
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All authors Anthony Ficklin, Alessandro Bruno, Lauren Blum, Nicholas Cannady, T. G. Guzik, Ryuho Kataoka, Kazuoki Munakata, Yosui Akaike, Shoji Torii

Computational Physics

Topology- and Geometry-Exact Coupling for Incompressible Fluids and Thin Deformables

Jonathan Panuelos, Eitan Grinspun and David Levin

We introduce a topology-preserving discretization for coupling incompressible fluids with thin deformable structures, achieving guaranteed leakproofness through preservation of fluid domain connectivity. Our approach leverages a stitching algorithm applied to a clipped Voronoi diagram generated from Lagrangian fluid particles, in order to maintain path connectivity around obstacles. This geometric discretization naturally conforms to arbitrarily thin structures, enabling boundary conditions to be enforced exactly at fluid-solid interfaces. By discretizing the pressure projection equations on this conforming mesh, we can enforce velocity boundary conditions at the interface for the fluid while applying pressure forces directly on the solid boundary, enabling sharp two-way coupling between phases. The resulting method prevents fluid leakage through solids while permitting flow wherever a continuous path exists through the fluid domain. We demonstrate the effectiveness of our approach on diverse scenarios including flows around thin membranes, complex geometries with narrow passages, and deformable structures immersed in liquid, showcasing robust two-way coupling without artificial sealing or leakage artifacts.
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Physics Education

The Landscape of Undergraduate Astronomy and Astrophysics Degree Requirements

Kate Follette, Carl Ferkinhoff, Michael Foley et al. (+5 more)

In this document we summarize the results of a survey of undergraduate degree-granting programs conducted by the 2024-2025 American Astronomical Society Education Committee's Subcommittee on UndeRgraduate and Graduate Education (SURGE). Individuals from 78 institutions completed the survey, representing approximately 1000 majors annually and a majority of undergraduate Astronomy and Astrophysics degree-granting institutions. Information collected from participants include: degree names, degree types, course requirements, elective course options, and learning goals. Our report presents 9 key findings and 10 recommendations, and these are summarized in the preamble to the report. The recommendations are directed primarily to degree-granting departments and the American Astronomical Society, as the principal relevant professional organization, though we earnestly invite all members of the Astronomy and Astrophysics community to contribute to a broader discussion about these findings and recommendations. Appendix A of the report contains detailed descriptions of survey data analyses. Appendices B and C contain recommended undergraduate course requirements and learning goals, respectively. Our survey results show clearly that there is not currently community consensus about what knowledge and competencies an undergraduate Astronomy or Astrophysics degree should instill. This lack of cohesion is a problem for our community, as it dilutes the significance and interpretability of the credential for employers and graduate schools. We view this report as just the beginning of an important dialog, and we look forward to engaging with the Astronomy and Astrophysics community about our findings and recommendations through our feedback form at bit.ly/49c4FYb.
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All authors Kate Follette, Carl Ferkinhoff, Michael Foley, Meridith MacGregor, Melissa Morris, Karen Masters, Tom Rice, Colin Wallace

Atmospheric and Oceanic Physics · Cross-listed from cs.LG

WIND: Weather Inverse Diffusion for Zero-Shot Atmospheric Modeling

Michael Aich, Andreas Fürst, Florian Sestak et al. (+3 more)

Deep learning has revolutionized weather forecasting, but many challenges remain, including climate modeling. Moreover, the current landscape remains fragmented: highly specialized models are typically trained individually for distinct tasks. To unify this landscape, we introduce WIND, a single pre-trained foundation model capable of replacing specialized baselines across a vast array of tasks. Crucially, in contrast to previous atmospheric foundation models, we achieve this without any task-specific fine-tuning. To learn a robust, task-agnostic prior of the atmosphere, we pre-train WIND with a self-supervised video reconstruction objective, utilizing an unconditional video diffusion model to iteratively reconstruct atmospheric dynamics from a noisy state. At inference, we frame diverse domain-specific problems strictly as inverse problems and solve them via posterior sampling. This unified approach allows us to tackle highly relevant weather and climate problems, including probabilistic forecasting, spatial and temporal downscaling, reconstruction of spatial fields from sparse observations and enforcing global dry air mass conservation. We further demonstrate how WIND can be applied to explore extreme weather events under prescribed out-of-distribution thermodynamic perturbations. By combining generative video modeling with inverse problem solving, WIND offers a computationally efficient alternative for AI-based atmospheric modeling.
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All authors Michael Aich, Andreas Fürst, Florian Sestak, Carlos Ruiz-Gonzalez, Niklas Boers, Johannes Brandstetter