Geophysics · Cross-listed from cs.LG
Using Seismic Statistical Features and VQ-VAE to Improve Spatiotemporal Seismicity Predictability
Wei Quan and Denise Gorse
In this paper we build upon a previous study in which we demonstrated, using XGBoost and earthquake catalogue data from Japan and Chile, that a set of 60 seismic statistical features (SSFs) had much greater predictive value than a set of 428 generic time series features from the tsfresh package. We here extend this previous work in two key ways, focusing on data from Japan as a large
dataset is necessary in order to allow for the training of a deep learning (autoencoder) model. First, we move from whole-region prediction (considering, for each candidate event, the likelihood of an event M $\geq$ 5.0 anywhere in the region in the next 15 days) to localised predictions in which both the region of feature computation and the region of prediction are restricted to a circle of radius 24 km around the candidate event, and we show that performance remains excellent, similar to our previous whole-region study for the same area. Second, we here couple this proven set of SSFs, based on one-dimensional (catalogue) data, with a novel feature based on magnitude-stratified two-dimensional seismic maps, obtained by training a VQ-VAE model to reproduce such maps as output and identifying a measure of its error in doing so with anomalies in magnitude distribution reflecting a localised build-up of crustal stress. We show that while localised prediction based only on a set of SSFs can be effective, with test AUC values as high as those obtained in the case of Japan in our previous whole-region study, the inclusion of the new natively-spatial VQ-VAE-derived feature, top-ranked by SHAP analysis, both improves prediction and points toward a new approach that might allow computationally time-demanding traditional seismic features to be entirely set aside.
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General Physics
New sum rules of the Koide type
Alejandro Rivero
We report a mass rule of Koide type with inverse shape, \[m_i=M^{(d)} (w_0+w_i)^{-2}.\] It applies to the down-
quark sector with numerical precision comparable to that of the direct charged-
lepton sum rule $m_i=M^{(l)} (z_0+z_i)^{2}$. For central mass values, Koide ratio reaches exactly $2/3$ near 280 TeV under Standard Model renormalisation-group running. We also review other rules of the direct kind involving
quarks.
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Chemical Physics
Electron Paramagnetic Resonance Study of Radical Species on NaNbO3@CeO2-Modified Carbon Vulcan XC72 Gas Diffusion Electrode for Electrochemical Degradation of Paracetamol via Electro-Fenton
Caio Machado Fernandes, Joao Paulo C. Moura, Aline B. Trench et al. (+4 more)
While electrochemical oxidation is a promising technology for water treatment, a fundamental understanding of the specific radical mechanisms involved in pharmaceutical degradation has remained limited. This study addresses this gap by employing Electron Paramagnetic Resonance (EPR) spectroscopy to directly quantify the radical species generated during the degradation of paracetamol using a novel gas
diffusion electrode (GDE) modified with NaNbO3 nanocubes and CeO2 nanorods. This approach provides a critical advancement beyond prior literature by moving from indirect inference to direct, quantitative analysis of reactive species. Results demonstrated that a boron-doped diamond (BDD) anode (65% OH radical, 35% aryl radicals) drastically outperformed a Platinum (Pt) anode (74% OH radical, 26% aryl radicals), achieving complete degradation in 15 minutes versus 45 minutes and 81.6% versus 67.8% mineralization. Consequently, this work provides a foundational mechanistic framework that fundamentally advances the field, offering not just a more effective material system (BDD/NaNbO3@CeO2-GDE) but also a validated methodology for rationally designing and optimizing electrochemical water treatment processes based on quantifiable radical pathways.
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All authors Caio Machado Fernandes, Joao Paulo C. Moura, Aline B. Trench, Rafael Sotana, Ana Maria P. Neto, Willy G. Santos, Mauro C. Santos
Computational Physics · Cross-listed from quant-ph
Absence of poor local minima in matrix product states
Hao-Kai Zhang, Chenghong Zhu, Shuo Liu et al. (+2 more)
Quantum circuits suffer from severe trainability issues: even shallow
circuits are swamped with poor local minima. Yet
matrix product states (MPS), which can be prepared by sequential
circuits, are remarkably trainable in practice – as demonstrated by decades of successful density
matrix renormalization group calculations. In this work, we resolve this apparent paradox by proving that the energy landscapes of MPS are free from poor local minima, under the same setting where brickwork
circuits are not. The key insight is that the gauge freedom of MPS creates an effective local overparametrization that causes local minima to concentrate near the global minimum, analogous to overparametrized classical neural networks. We rigorously prove that the local minimum distribution is invariant under moves of the orthogonality center of MPS representations. Numerical experiments further confirm that the optimization of sequential
circuits converges to near-optimal solutions even for random
Hamiltonians, in stark contrast to brickwork
circuits. Our findings establish a theoretical understanding of the trainability of MPS, providing a valuable guide for designing variational
quantum circuits and
algorithms with better trainability in the future.
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All authors Hao-Kai Zhang, Chenghong Zhu, Shuo Liu, Shi-Xin Zhang, Tao Xiang
Fluid Dynamics
Geometry-Aware Anisotropic Boundary Correction for Aerodynamic Simulation
Xin Zhang, Yipeng Huang, Shu Jiang et al. (+2 more)
Aerodynamic simulation is a key component of engineering shape design, where core quantities such as the surface pressure coefficient strongly depend on flow dynamics near solid boundaries. Neural operators provide an efficient alternative to expensive Computational Fluid Dynamics (CFD) solvers. However, conventional methods treat the boundary region isotropically, failing to account for the distinct physical behaviors along the boundaries. In reality, the aerodynamic process exhibits anisotropy: along the tangential direction, flow propagates along the wall; along the normal direction, physical quantities are constrained by the wall. To explicitly model the distinct physical behaviors, we propose GeoABC, a geometry-conditioned anisotropic boundary correction framework. GeoABC leverages the boundary geometries to introduce direction-aware boundary correction into the intermediate representations of neural operators, transforming boundary geometry from static input features into a structural prior that modulates physical prediction. On 2D airfoil and 3D car tasks, GeoABC consistently adapts to multiple neural operator backbones, reducing near-boundary relative $L_2$ error by $\sim$38% on average, narrowing the structural near-wall gap shared by mainstream neural operators, and advancing neural operators toward high-fidelity aerodynamic simulation.
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All authors Xin Zhang, Yipeng Huang, Shu Jiang, Zhenzhong Wang, Min Jiang
Medical Physics · Cross-listed from q-bio.QM
Adjusted trajectory of medication exposure taking into account the periodicity of dispensations and the number of dispensed packs and comparative analysis on EFEMERIS database
Cécile Chouquet, Anna-Belle Beau, Christine Damase-Michel et al. (+3 more)
We presented an adjustment method for the calculation of medication exposure trajectories based on the number of dispensed packs and the type of dispensations (occasional or regular). A comparative study based on the EFEMERIS data was carried out using three different scenarios of trajectory calculation depending on whether or not the number of packs and the periodicity of medication dispensations were taken into account. The impact of the scenario was highlighted using global indicators on the number of Define-Daily Dose (DDD) on all women exposed; the study of changes in individual trajectories from one scenario to another was carried out; we also compared the results of a clustering into four groups. If 65% of the trajectories remained unchanged, we could observe on the rest significant changes in number of DDD and/or on individual exposure profile. We observed 4% of trajectories that were attributed to a different cluster, and the clustering was of better quality with the adjustment method. Depending on the study context, an impact on cluster distribution could be observed for some maternal characteristics and neonatal outcomes. This was the case for a higher occurrence of neonatal pathology for neonates from mothers belonging to the cluster with high doses of psychotropics, thus reinforcing the conclusions of previous studies of a link between high exposure to psychotropic medications and presence of pathology for the newborn.
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All authors Cécile Chouquet, Anna-Belle Beau, Christine Damase-Michel, David Jeauneau, Isabelle Lacroix, Sabine Mercier
Computational Physics · Cross-listed from cs.LG
Integrating Out, Twice:The Open-System Case That Neural-Network Ensemble Theory Is Missing
Jin Lei
Averaging a neural network over its random parameters and marginalizing a Gaussian sector are the same operation, the Schur complement of the eliminated block, and when that block is closed it returns a covariance and its inverse. That is all a network ensemble produces, the closed case. The open case is missing, and
nuclear reaction theory has it worked out. Projecting a scattering problem onto a chosen set of channels, with the rest carrying probability irreversibly to a continuum, leaves a non-Hermitian effective generator that conserves and itemizes exactly what it loses: the
nuclear optical model and its generalized optical
theorem. I set the two cases side by side using only the moments of a distribution, the
algebra of Gaussians, and block inversion, no field theory, and give the closed-case dictionary in full: the neural tangent kernel is the Fisher sensitivity kernel, the infinite-width Gaussian limit is the Gaussian-process emulator, and the lazy-to-feature transition is the validity boundary of a reduced-basis emulator. I then test the open export on a truncated
attention map, a
token-level transfer operator, and a sparse expert router, and report a mostly negative result. The conserved flux ledger ports wherever openness is genuinely present, but its distinctive content is absent, an artifact of the chosen partition, or pinned near a floor by the training objective, and the operationally useful uncertainty turns out to be epistemic, living in the closed half of the correspondence, not the open one. The negative has a structural reason this note makes precise: the open case needs an eliminated sector with a continuous spectrum and wave-like, not relaxational, dynamics, which mainstream learning's finite or dissipative objects do not supply. This is a note, not a result; its main finding is that negative one, and its value is the map that locates it.
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Fluid Dynamics
A fast and consistent sharp-interface immersed boundary method for moving bodies of arbitrary thickness
Giovanni Vagnoli, Martino Andrea Scarpolini, Roberto Verzicco et al. (+1 more)
Immersed boundary methods (IBMs) are widely used to simulate flows around complex geometries and moving bodies, but they often involve a trade-off between precision and computational efficiency. Eulerian formulations require special treatments for moving walls and may generate spurious force oscillations, whereas Lagrangian formulations can suffer from slip errors at the immersed surfaces. We propose a novel sharp-interface IBM for incompressible flows involving moving, deformable, and arbitrary-thickness bodies. The method combines a fast tagging
algorithm, a two-sided Eulerian forcing strategy, and a consistent mass correction that reduces the splitting error of fractional-step schemes, while preserving the structure of the discrete
Laplacian operator. This formulation retains the efficiency of direct Poisson solvers, thus avoiding the overhead of cut-
cell, multigrid, and projection-based approaches. The method naturally handles moving boundaries, and yields small transpiration errors with second-order accuracy in the enforcement of the no-slip condition. Numerical tests using rigid, deformable, turbulent, and biologically inspired flows demonstrate the accuracy, robustness, and efficiency of the method, without compromising computational cost.
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All authors Giovanni Vagnoli, Martino Andrea Scarpolini, Roberto Verzicco, Francesco Viola
Atmospheric and Oceanic Physics
Deep learning reveals a stronger fossil fuel influence than biomass burning in shaping remote tropospheric ozone
Chaoqun Ma, Hang Su and Yafang Cheng
Tropospheric ozone (O3) is a key greenhouse gas and atmospheric oxidant, yet its sources in the remote troposphere remain strongly debated. Observation-based tracer analyses suggest that O3 attributed to biomass burning is much greater than that from fossil fuel sources (by a factor of ~2-10), contradicting state-of-the-art global models. Here we show that this discrepancy primarily arises from the strong sensitivity of tracer methods to differences in tracer lifetimes, especially after extended transport to the remote regions. To resolve this discrepancy, we develop a deep learning (DL) framework that synthesizes global observations and chemical transport model simulations. The DL approach accurately infers source contributions and reveals that fossil fuel emissions contribute over three times more O3 to the remote troposphere than biomass burning. Our findings underscore that phasing out fossil fuels remains the most powerful lever for mitigating remote tropospheric ozone.
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Plasma Physics
Fully-implicit Particle-in-Cell model of a Magnetic Nozzle with electromagnetic power deposition
Mario Merino, Juan Martín-Hernández, Pedro Jiménez-Jiménez et al. (+1 more)
A fraction of the electromagnetic power used to generate and heat the
plasma in helicon sources and electrodeless
plasma thrusters can leak into the outer expansion region, interacting with the
plasma in the
magnetic nozzle and affecting the performance of the device. This work analyzes the properties of the
plasma in a convergent-divergent
magnetic nozzle when right-hand polarized waves of varying amplitude propagate into it. This is accomplished with a 1D3V fully-implicit, Vlasov-Darwin particle-in-
cell model of the collisionless ion and electron
plasma in a
magnetic tube. The code exactly conserves charge locally and energy globally. It features a nonuniform grid and an enhanced substepping routine for the macroparticle trajectories. The requirement that the expansion be current-free is satisfied thanks to linear closed-loop controllers on the injection and downstream boundary conditions. Wave heating increases the electron perpendicular temperature, especially in the vicinity of an electron cyclotron resonance surface, always present inside the
magnetic nozzle of a helicon device. The energized electrons become anisotropic, and drive a more pronounced potential drop and a higher ion acceleration than in the absence of waves, at the expense of the wave power. The computed moments of the ion and electron distributions reveal the dominant balance of the electron thermal terms, electrostatic terms, and ion inertial terms in the momentum and energy equations. Wave heating helps populate otherwise-inaccessible regions of the electron phase space and modifies the doubly-trapped electron population found in the purely electrostatic case. The overall efficiency of the
magnetic nozzle increases mildly at low wave power, but drops quickly as it is increased beyond a threshold, as part of the energy is wasted...
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All authors Mario Merino, Juan Martín-Hernández, Pedro Jiménez-Jiménez, Luis Chacón
Chemical Physics
RPA as a Hessian Closure: Effective Functionals and Source-Variable Duality Across DFT, LR-TDDFT, 1RDMFT, and MBPT
Nan Sheng
We present a variational formulation of the random phase approximation (RPA) that places density functional theory (DFT), linear-response time-dependent density functional theory (LR-TDDFT), one-body reduced density
matrix functional theory (1RDMFT), and Green's function many-body perturbation theory (MBPT) into a common source-variable hierarchy. The central claim is that a broad class of RPA constructions can be organized, independently of any one problem-specific formula, diagrammatic resummation, or small-amplitude equation of motion, as closure approximations to the exact Hessian of an effective functional. In this
language, exact linear response is governed by the Hessian of the corresponding effective functional, while RPA is obtained by retaining a reference contribution together with an explicit interaction kernel and discarding the irreducible remainder. The hierarchy has two independent enrichments of the density-level description. One may enlarge the static local density to a time-dependent density, giving the dynamical density channel of LR-TDDFT, or enlarge it to an equal-time bilocal one-body reduced density
matrix, giving the static bilocal channel of 1RDMFT. The Green's function level combines both enrichments, since the one-particle Green's function is bilocal in both space and time. This picture clarifies the relation between DFT, LR-TDDFT, 1RDMFT, and MBPT through exact forward reductions and source restrictions, while emphasizing that the corresponding RPA closures need not commute under projection. The hierarchy also distinguishes the local branch-wise Legendre geometry common to all four levels from the stronger global convex duality that may emerge only in source sectors possessing additional positivity and global regularity.
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Atomic Physics
Suppressing the Motion of Rydberg Atoms in Inhomogeneous Electric Fields via Stark Echo
Dominik Jakab, Manuel Kaiser, Conny Glaser et al. (+3 more)
Rydberg atoms possess strong electric dipole transitions and tunable energy levels, making them promising candidates for microwave to optical conversion on integrated superconducting atom chips. Achieving strong coupling of the atoms to e.g. the microwave field of an on-chip resonator requires placing the atoms within tens of micrometers from the chip surface. However, inhomogeneous stray electric fields originating from the surface can induce position-dependent Stark forces, resulting in atomic motion and leading to time-dependent shifts of the Rydberg energy levels. We experimentally investigate these effects using time-of-flight and spectroscopic techniques, observing substantial level shifts and signal loss attributable to field-induced atomic motion. A theoretical model incorporating an exponentially decaying surface field with a superimposed bias accurately reproduces the observed dynamics. To mitigate the level shift, we introduce a Stark echo sequence that dynamically reverses the force. This approach suppresses the atomic motion and maintains the atomic resonance. The method relies solely on global field control and is compatible with atom-resonator coupling architectures, providing a robust strategy for preserving coherence of Rydberg atoms in inhomogeneous electric fields near surfaces.
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All authors Dominik Jakab, Manuel Kaiser, Conny Glaser, David Petrosyan, József Fortágh, Andreas Günther
Plasma Physics
The integral and correlation scales of solar wind turbulence
Jean C. Perez, Sofiane Bourouaine and Mason Dorseth
Many works have attempted to estimate the correlation and integral timescales associated with turbulent fluctuations in the
solar wind, which are interpreted as length scales based on Taylor's~Hypothesis. However, accurate estimates of these timescales from spacecraft observations heavily rely on the accurate estimation of autocorrelation functions (ACF), which have been recently shown to depend strongly on the interval length used to estimate them. In this Letter, we show that this dependence on interval length may be artificial because common ACF
estimators do not correctly capture the long-lag behavior of the true ACF of the underlying
turbulence. We introduce a new ergodicity-based methodology to unambiguously estimate the integral timescale, and a new ACF
estimator with better ergodic convergence than current ones. Due to its ergodic properties, the new ACF
estimator properly captures the long-lag behavior, and is independent of the interval length. We use this approach to estimate the integral and correlation scales of
magnetic fluctuations in the
solar wind near $1~{\rm au}$.
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Plasma Physics
A Numerical Experiment on Oscillatory Magnetic Reconnection in a Laboratory Plasma System Driven by Alternating Currents
Sripan Mondal, Abhishekh Kumar Srivastava and Eric R. Priest
Using the open source MPI-AMRVAC framework, we study oscillatory reconnection in a laboratory
plasma, which occurs when a
magnetic null is perturbed by incoming fast magnetoacoustic waves driven by an alternating current. The
magnetic null region collapses to first form a $y$-directed current sheet that later changes its orientation to the $x$-direction. The $x$-directed current sheet has smaller enhanced thermal pressure and out-of-plane current than the $y$-directed current sheet. The
Hall effect produces an out-of-plane
plasma flow that evolves with a time lag with respect to the enhanced thermal pressure and out-of-plane current density. Increasing the amplitude of the alternating current produces higher thermal pressure, out-of-plane current density, and out-of-plane
plasma flow, while the first peaks of thermal pressure and out-of-plane current density occur earlier.
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Atomic Physics
Long-term laser frequency stabilization with an FPGA-controlled scanning cavity
R. Forti, R. Panza, A. Muzi Falconi et al. (+5 more)
We present an FPGA-based implementation of a scanning transfer cavity lock (STCL) for
laser frequency stabilization, allowing for the simultaneous stabilization of multiple
laser sources with respect to a single reference
laser by means of a continuously scanned Fabry-Perot cavity. By exploiting the FPGA architecture to simultaneously perform cavity scanning, peak detection, and feedback actuation, we minimize latency and allow independent control loops for several
lasers within a single device, offering direct scalability. The system performance is analyzed through heterodyne measurements from short ($<1\,$s) to long ($\sim20\,$hours) timescales. The end-to-end locking performance is validated through atomic spectroscopy of ytterbium atoms in a magneto-optical trap, demonstrating sub-MHz absolute frequency stability over several hours for the stability transfer across $\sim 150\,$nm in the visible-wavelength range. Importantly, we demonstrate a novel fast-scanning approach based on acousto-optic modulator (AOM) frequency modulation, enabled by the low detection latency of our FPGA implementation. This increases significantly the effective locking bandwidth and reduces the intrinsic noise of the system with respect to standard piezo-actuated scanning of the cavity length, allowing to reach sub-$100\,$kHz long-term stability and offering perspectives for
laser-line narrowing. Owing to its modularity, low cost and ease of implementation within the open-source PyRPL firmware package for the STEMlab Red Pitaya platform, our architecture offers a compact and flexible alternative to existing STCL and locked-cavity implementations, providing a practical approach to the operation of a state-of-the-art cold-atom experiment without relying on any atomic references for
laser stabilization.
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All authors R. Forti, R. Panza, A. Muzi Falconi, S. Sbernardori, A. Vardé, M. Marinelli, A. Carini, F. Scazza
Computational Physics · Cross-listed from cond-mat.mtrl-sci
Bi-S network origin of cation-disorder stability and dispersive band edges in AgBiS2
Han-Pu Liang, Songyuan Geng, Heng Kang et al. (+9 more)
Cation-disordered AgBiS2 is a promising lead-free optoelectronic material, but both its ordered structure and the microscopic origin of its favorable electronic properties remain debated. Theory has proposed a mixed-coordination tendency with tetrahedral AgS4 and octahedral BiS6 units, whereas experiments mainly report octahedrally coordinated ordered and cation-disordered phases, together with local cation off-centering. Here, we combine a machine-learning interatomic potential with a deep-learning
Hamiltonian to resolve the coupled structural and electronic
evolution of AgBiS2 at large length scales. We identify the three-dimensional Bi-S network as the central structural motif governing both disorder stability and band-edge electronic states. At weak disorder, Ag/Bi exchange competes with the off-centering tendency of the Ag sublattice, producing strongly distorted local environments and convoluted diffraction signatures that hinder the identification of the ordered phase. With increasing disorder, BiS6-like units connect into a continuous Bi-S network, which stabilizes the rocksalt-like disordered phase. Despite strong cation disorder, AgBiS2 retains clear
semiconductor-like band dispersion and develops a direct band gap. The connected Bi:p-S:p states supported by the Bi-S network preserve a dispersive conduction-band edge and a small electron effective mass. In contrast, mobile Ag disrupts the long-range periodicity of Ag-S bonding, leading to strongly localized valence states. These results clarify the structural controversy in ordered AgBiS2 and establish a unified physical picture of disorder stability and optoelectronic response in nonisovalent
semiconductor alloys.
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All authors Han-Pu Liang, Songyuan Geng, Heng Kang, Chen Qiu, Xiao-Ping Yao, Qing'an Li, Bozhao Zhang, Lechuan Sun, Yuxuan Chen, Shan Zhang, Su-Huai Wei, Peng-Fei Guan
Chemical Physics · Cross-listed from quant-ph
Parahydrogen Cooling of Nuclear Spin Chains at Hypogeomagnetic Fields
Alexey Kiryutin, Danil Markelov, Ivan Zhukov et al. (+3 more)
Solution-state molecular
nuclear spin networks are promising
quantum simulators because their scalar-coupling
Hamiltonians are chemically programmable, precisely measurable, and coherent at room temperature. Their main limitation for
quantum information science is initialization: thermal Boltzmann polarization produces highly mixed, high-
entropy states. Here, we use parahydrogen-based Signal Amplification by Reversible Exchange (SABRE) at hypogeomagnetic fields (i.e.,
magnetic fields below Earth field) to hyperpolarize the chemically engineered 12-
spin chain [U-13C,15N]-butyronitrile. SABRE generates percent-level 13C and 15N polarization and prepares non-
equilibrium multi-
spin orders across the network. A von Neumann
entropy analysis of such a hyperpolarized system shows that, at the optimal transfer field of 0.52 uT, the full
spin system could reach S/k = 8.274, compared with S/k = 8.318 for the unpolarized reference, giving (S-Sth)/k = -0.043. Experimentally,
nuclear spin temperatures of 52 mK and 257 mK are achieved for 15N and 13C subensembles, respectively. The larger
entropy deficit of the full network than of individual subsystems indicates correlated multi-
spin order beyond single-
spin polarizations. Rapid field cycling to 9.4 T enables site-resolved NMR readout, while the precisely determined coupling network provides an experimentally benchmarked
Hamiltonian for testing
quantum-simulation,
quantum-control, and
Hamiltonian-learning protocols.
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All authors Alexey Kiryutin, Danil Markelov, Ivan Zhukov, Erik Van Dyke, Alexandra Yurkovskaya, Danila Barskiy
Computational Physics · Cross-listed from cond-mat.mtrl-sci
Ab initio parametrization of distributed polarizable force fields
Felix Post, Jean-Philip Filling, Toulik Maitra et al. (+3 more)
Polarizable force fields offer superior transferability and accuracy compared to classical force fields, enabling access to electronic response properties such as refractive index and electronic density of states. Here, we demonstrate two key improvements that significantly enhance their accuracy: (1) assigning atomic polarizability to individual atoms rather than atom types, and (2) employing atomic polarizability tensors instead of scalar values. These modifications extend the applicability of polarizable force fields to cations, anions, and excited states, while also providing more accurate descriptions of neutral molecules. We propose a first-principles-based parameterization procedure for atomic polarizability tensors and scalars, validated on a set of small organic molecules with conjugated building blocks. To overcome the computational cost of ab initio calculations, we train a message-passing graph neural network to predict polarizability parameters, enabling efficient and scalable parameterization. Crucially, this approach imposes no additional computational cost during simulations and provides a clear diagnostic criterion for identifying cases where polarizable force field models fail to accurately describe molecular polarizability.
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All authors Felix Post, Jean-Philip Filling, Toulik Maitra, Falk May, Michael Wand, Denis Andrienko
Computational Physics · Cross-listed from cs.LG
Data-driven discovery of governing differential equations across physical systems
Siyu Lou, Hao Xu, Wenguan Wang et al. (+6 more)
Differential equations play a critical role in scientific discovery because they provide a mathematical framework to describe the behaviour of physical phenomena. As a promising alternative to traditional first principles, data-driven differential equation discovery has attracted increasing
attention for its ability to infer governing laws directly from experimental or simulated data, especially when the underlying physics is unclear. However, the field has expanded rapidly along diverse methodological directions, particularly with the emergence of AI-based approaches, and still lacks a clear organizing perspective. In this Review, we propose a problem-oriented perspective on data-driven differential equation discovery. We first introduce a two-dimensional phase diagram of equation discoverability, where discovery problems are organized according to structural complexity and coefficient complexity. This phase diagram shows how the field has moved from the discovery of sparse equations with simple coefficients toward more complex governing laws with richer structures and more flexible parameterizations. It also clarifies why different methodological families succeed or fail in different problem settings. We then present the representation-evaluation-optimization (REO) framework as a fundamental abstraction of the discovery process. By identifying the core problems of equation discovery that persist across algorithmic variations, REO shifts the discussion from individual
algorithms to the fundamental principles that determine discoverability. We connect these perspectives to applications across physics and adjacent sciences, and argue that the next challenge is not merely recovering equations, but using them to revise existing theories, distil mechanisms and form new scientific concepts.
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All authors Siyu Lou, Hao Xu, Wenguan Wang, Lu Lu, Hao Sun, Yang Liu, Linfeng Zhang, Dongxiao Zhang, Yuntian Chen
Atomic Physics · Cross-listed from cond-mat.quant-gas
Mapping the parameter space of double microwave shielding
Hubert J. Jóźwiak, Ian Stevenson, Sebastian Will et al. (+1 more)
Double microwave shielding employs $\sigma^{+}$- and $\pi$-polarized microwave fields, tuned close to the lowest rotational transition, to engineer a long-range repulsive barrier between polar molecules. By preventing molecules from reaching the short range, this technique suppresses detrimental two-body losses and recently enabled the realization of molecular Bose-Einstein condensates and self-bound droplets. Yet, the optimal operating regimes of the shielding mechanism remain largely unexplored. Here, by leveraging the underlying universality of the scattering problem, we systematically map the four-dimensional microwave parameter space-spanned by the detunings and intensities of the two fields-to identify configurations that maximize both shielding efficiency and interaction tunability. We define optimal operating regimes as configurations that are strictly free of field-linked bound states while sufficiently suppressing two-body losses to exceed typical lifetimes of ultracold samples. In these regimes, we evaluate the elastic-to-inelastic collision ratios required for efficient evaporative cooling and explore the accessible tuning range of the effective dipolar interactions. Finally, to identify the best platforms for future
quantum simulation experiments, we conduct a global survey of candidate molecular species under realistic field constraints. We identify heavy, strongly dipolar molecules as the most promising candidates, demonstrating that they can achieve extreme loss suppression-reaching two-body loss rates as low as $10^{-18}$ cm$^{3}$/s-alongside robust interaction tunability using only moderate field strengths.
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All authors Hubert J. Jóźwiak, Ian Stevenson, Sebastian Will, Tijs Karman
Fluid Dynamics · Cross-listed from astro-ph.SR
Peristaltic Flow in Compressible, Ideal Magnetohydrodynamics: A Mechanism For Solar Spicules
D. Tsiklauri
We present analytical model for peristaltic transport within compressible, ideal magnetohydrodynamics (MHD). By employing small-amplitude perturbation expansion, under thin-tube long-wavelength approximation with a uniform axial background
magnetic field, we study non-linear coupling between thermodynamic pressure variations and Maxwell's
magnetic tension stresses. The resulting net time-averaged volumetric flow rate $\langle Q \rangle$ is calculated. When applied to
solar chromospheric spicules under equipartition constraints ($\beta \sim 1$), where sound speed matches the Alfvén speed, we find $\langle Q \rangle = 4\epsilon^2/(M^2-1)$. Because the denominator remains positive across all operational supersonic Mach numbers ($M \approx 2\text{--}10$), upward-propagating mechanical disturbances drive a highly directional, collimated upward flow which we interpret as a spicule. Estimates show that for observationally realistic magnetosonic waves with amplitudes of $\approx 10\%$, the peristaltic mechanism generates a localized mass flux $\approx 100$ times that of
solar wind. We propose an explicit observational signature of this mechanism, wherein the launch of individual spicular jets is directly preceded by magnetosonic wave trains detectable as localized intensity modulations. Beyond
solar chromospheric application, the model may be applicable to traveling
magnetic field pinches in laboratory
plasma devices and astrophysical mass-loading processes in
stellar winds and inner regions of magnetized
accretion disks.
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Chemical Physics
Revealing Wavelength- and Size-Dependent CO2 Reduction Selectivity via Operando Scanning Photo-Electrochemical Microscopy
Fatemeh Kiani, Milad Sabzehparvar, Priscila Vensaus et al. (+6 more)
Controlling product selectivity in plasmonic catalysis, particularly in CO2 reduction (CO2R), remains a central unsolved challenge with direct implications for light-driven fuel and chemical synthesis. Here, we deploy quantitative operando scanning photoelectrochemical microscopy (photo-SECM) to provide a direct demonstration that tuning
photon energy switches CO2R selectivity through an electronically driven pathway. On plasmonic Au/p-GaN photocathodes, interband excitation (460-560 nm) drives selective CO production while intraband excitation (640-800 nm) favors H2
evolution. By maintaining constant absorbed power across wavelengths and confirming linear power dependence, we isolate the role of hot-carrier energy from photonic and photothermal contributions. Density functional theory calculations reveal that higher-energy interband excitation progressively increases the overlap between hot-electron-accessible states and the CO-producing intermediate, selectively promoting CO over formate, in excellent agreement with experiment. We further show that selectivity is geometrically gated by hot-carrier transport: sub-100 nm nanostructures sustain CO2R activity, while ~300 nm nanodisks suffer transport losses that suppress it, consistent with ab initio hot-carrier transport calculations. Together, these results establish
photon energy, carrier transport, and nanostructure geometry as coupled design parameters for plasmonic CO2R selectivity, resolve a longstanding debate on the origin of plasmon-driven selectivity effects, and position photo-SECM as a broadly applicable operando platform for photo(electro)catalysis.
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All authors Fatemeh Kiani, Milad Sabzehparvar, Priscila Vensaus, Elif Nur Dayi, Olga D'Anania, Tarique Anwar, Nuria Lopez, Ravishankar Sundararaman, Giulia Tagliabue
Instrumentation and Detectors · Cross-listed from cond-mat.soft
ARTGEL: A temperature-regulated electrophoresis platform for quantitative studies of reversible association in gels
Rupam Saha and Seth Fraden
Here we present ARTGEL, an actively regulated-temperature gel electrophoresis platform designed for long-duration experiments under independently controlled thermal and electrical conditions. ARTGEL combines thermoelectric regulation of the gel temperature, a large heated and circulated buffer reservoir, and an automated electrode-wiping mechanism that stabilizes the voltage across the gel during runs exceeding 24 h. The platform was developed to address a limitation of conventional electrophoretic mobility shift assays, which are commonly used to analyze reversible biomolecular association but usually aim to suppress reaction during electrophoresis by dilution, competitors, or reduced temperature so that the gel reports a pre-equilibrated bulk solution. For temperature-sensitive systems, these strategies can alter the chemical state during loading and migration and obscure whether the measured band pattern reflects the original bulk sample or a re-equilibrated state inside the porous gel. Rather than attempting to quench reactions, ARTGEL enables electrophoresis to be performed at the same temperature as complementary bulk measurements, so that reversible association can be quantified directly in the gel and compared with matched measurements in solution. Using DNA origami assemblies, we show that ARTGEL preserves distinct temperature-dependent association states, resolves reaction-dependent distortions of migrating bands, and supports extraction of in-gel kinetic and thermodynamic parameters from reaction-
diffusion-advection modeling.
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Physics and Society
Neutrino monitoring of explosions for excluding fission yield
O. Benevides Rodrigues, N. S. Bowden, R. Carr et al. (+18 more)
Nuclear fission produces
neutrinos, so the absence of a
neutrino signal can be used to set a limit on the fission content of an explosion. This capability could be employed on former
nuclear test sites to assure regulators, international monitors, or other observers that activities involving chemical explosions do not exceed a designated limit for
nuclear fission. This paper quantifies the
neutrino detector masses that would be required to set fission yield limits at source-to-detector distances up to 100 km, assuming detection by inverse beta decay with realistic background levels. The analysis indicates that detectors with active mass in the ton- to tens-of-kiloton range can set potentially useful limits on the fission yield of large chemical explosions at the Nevada National Security Site. In contrast, inverse beta decay detectors are not well suited to excluding fission yield at longer range or in the subcritical
nuclear experiments that have occurred at some test sites following the cessation of explosive
nuclear testing.
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All authors O. Benevides Rodrigues, N. S. Bowden, R. Carr, A. Conant, M. Foxe, D. Hornback, P. Huber, A. Irani, L. Lebanowski, V. A. Li, J. M. Link, B. R. Littlejohn, F. Machado, M. P. Mendenhall, H. P. Mumm, J. Newby, I. D. Olusola, G. D. Orebi Gann, T. Papatyi, L. Pickard, X. Zhang
Plasma Physics
Nonlinear oscillations of the amplitude of energetic-particle induced geodesic acoustic modes
E. Sida, A. Biancalani, A. Bottino et al. (+2 more)
Energetic particle induced geodesic acoustic modes (EGAMs) are axisymmetric perturbations of the radial electric field in tokamak
plasmas. They are driven unstable by the phase space nonuniformity of a population of energetic particles (EP). In this paper, the nonlinear oscillation in the amplitude of the energetic-particle induced geodesic acoustic modes is studied by means of the gyrokinetic particle-in-
cell code ORB5. Similarities are discussed with the beam-
plasma instability (BPI), where a Langmuir wave is driven unstable by phase space nonuniformity of a population of energetic electrons. A similar scaling of the nonlinear oscillation frequency as a function of the mode amplitude is found for the EGAMs and for the BPI, confirming that their nonlinear dynamics is strongly determined by the same physical mechanisms. As a product of this study, a novel diagnostics is proposed for the evaluation of the EGAM intensity in tokamak
plasmas.
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All authors E. Sida, A. Biancalani, A. Bottino, F. Salvarani, R. Wu
Optics
Physical Bounds on Optical Micromanipulation: Maximal Stiffness in the Dipole Regime
Martin Zlabek, Jakub Liska, Lukas Jelinek et al. (+1 more)
Optical trapping and micromanipulation rely on carefully shaped electromagnetic fields to exert precise forces and torques on microscopic particles. Despite their widespread application in biology and nanotechnology, the absolute physical limits of trapping performance, specifically the maximum achievable optical force and trap stiffness, have not yet been rigorously quantified. This work establishes a general theoretical framework to determine these fundamental bounds in the dipole approximation. By relating the optical force and stiffness to a local Taylor expansion of the electromagnetic field at the particle location, we formulate the performance limit as a solution to a quadratically constrained quadratic program. To evaluate these bounds, we employ two complementary approaches. First, we utilize a complete basis of vector spherical wave functions to determine the absolute theoretical limits of optical force and stiffness permitted by Maxwell's equations in free space, revealing Pareto-optimal trade-offs between stable confinement and directional force. Second, we introduce an aperture-based formulation that restricts the incident fields to those realizable by finite planar apertures. This yields device-consistent bounds directly applicable to experimental setups which rely mostly on electromagnetic beams. The finding that optimized aperture fields can outperform standard Gaussian beams by removing the severe axial bottleneck is particularly important. By comparing these two regimes, we identify the specific spatial modes that contribute to stable trapping and quantify the performance trade-offs inherent to physical beam shaping. This dual framework provides provably optimal bounds for power-normalized optical tweezers and serves as a rigorous
benchmark for evaluating realistic beam designs.
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All authors Martin Zlabek, Jakub Liska, Lukas Jelinek, Miloslav Capek
Optics
An Adaptive Coherent Interferometric Oscillator Based on an Optoelectronic Magnonic Parametric Oscillator
Shihao Zhou, Junming Wu, Jiazhen Li et al. (+2 more)
We study a Mach-Zehnder interferometer (MZI)-based optoelectronic magnonic parametric oscillator (OEMPO) incorporating a YIG-loaded magnonic branch and a tunable phase-shifter branch, enabling systematic investigation of adaptive interferometric oscillator dynamics under distributed phase perturbations. Through analysis of nondegenerate OEPO mode pairs and frequency-pulling behavior, the loop free spectral range (FSR) and effective delay time were quantitatively extracted. Despite the nominally frequency-pinned parametric operation, weak frequency pulling and OEPO mode softening were observed, revealing an additional adaptive interferometric degree of freedom introduced by the MZI architecture. By comparing local and global sampling configurations, we demonstrate that the YIG branch behaves predominantly as a local dispersive resonant subsystem governed by the complex magnonic susceptibility, whereas the phase-shifter branch primarily controls the global interferometric redistribution geometry. Nevertheless, coherent recombination and adaptive regeneration within the loop produce finite cross-coupling between the two branches, resulting in partially synchronized interferometric dynamics and branch-dependent adaptive redistribution. Quantitative complex-Lorentzian analysis further reveals substantial phase-to-amplitude conversion and distinct differences between the OEO and OEPO regimes: the phase-pinned OEPO favors strongly dispersive local YIG response, while the frequency-adaptive OEO exhibits more mixed absorptive–dispersive behavior due to spectral relaxation through frequency pulling. Broadly, the present platform establishes a versatile framework for exploring adaptive nonlinear interferometric physics, coherent phase redistribution, and branch-dependent synchronization phenomena in hybrid magnonic-photonic oscillator systems.
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All authors Shihao Zhou, Junming Wu, Jiazhen Li, Qing Gu, Wei Zhang
Applied Physics
Automating the Expert Eye: A System-Agnostic Deep Learning Framework for Rare Event Discovery in Imbalanced Force Spectroscopy
Jorge Rodriguez-Ramos
Single-Molecule Force Spectroscopy (SMFS) provides unprecedented insights into biomolecular mechanics, yet the high-throughput generation of force-extension trajectories creates a severe data curation bottleneck. Identifying rare molecular unbinding events within thousands of noise-dominated curves traditionally relies on tedious, non-scalable manual
auditing. Here, we present a system-agnostic, interpretable deep learning framework tailored to overcome extreme class imbalance in automated SMFS triage. Utilizing 1D-to-2D rasterized
geometric matrices, we deployed a modified ResNet18 architecture governed by an asymmetric Focal Loss objective function. We evaluated this framework on the complex mechanical unfolding pathways of the R. champanellensis cellulosome. Under hyper-imbalanced test conditions where the target interaction constituted only 1.34% of the
dataset (13 true events out of 970 traces), the model achieved an overall accuracy of 0.9196 and a remarkable True Positive Rate (Recall) of 0.9231. By implementing an empirically calibrated dual-threshold triage system, the pipeline automatically discarded 880 unambiguous background noise traces , reducing the manual curation workload by over 90% while safely preserving high-value rare data. Finally, Gradient-weighted Class Activation Mapping (Grad-CAM) visually validated that the network's decisions are firmly anchored in the relevant
geometric features of the force curves, specifically localizing on the structural unbinding regions, effectively mitigating 'black-box' skepticism. Built for free cloud-based execution, this open-source tool democratizes scalable, highly precise molecular discovery across the biophysics community.
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Optics
Single plasmon transport in one dimensional nanowire
A. A. D{\i}az-Valles, B. Rousseaux, S. Guérin et al. (+3 more)
We introduce a unified theoretical framework for single-plasmon transport in one-dimensional nanowires, bridging the quantized electromagnetic Green's tensor formalism with effective non-Hermitian
Hamiltonian models. This approach naturally incorporates propagating surface plasmon polaritons, high-order modes dissipative channels, and intrinsic losses. We investigate both the stationary regime and the spatio-temporal dynamics of a single-plasmon pulse travelling through an atomic chain coupled to a dispersive nanowire. We analyze modal contributions to reflection and transmission spectra for
quantum emitter coupled to a silver nanowire, a configuration proposed as a single-plasmon transistor, and we demonstrate that optimized multi-emitter systems offer significant advantages. In case of one
quantum emitter coupled to a silver nanowire at telecom wavelengths, we predict a single-plasmon transmittivity down to 7% under realistic conditions, and an atomic
qubit population of 12%. Extension to multi-emitter systems using a Löwdin orthogonalization procedure enables a consistent treatment of collective interactions. We show that optimized positioning with just five emitters enhances plasmon modulation, achieving a transmittivity of 2% but also reduces coupling losses to one-third compared to the single-emitter case. Our results establish a robust foundation for analyzing and designing plasmonic waveguide
quantum electrodynamics systems.
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All authors A. A. D{\i}az-Valles, B. Rousseaux, S. Guérin, H. Jauslin, A. Leray, G. Colas des Francs
Chemical Physics
Closing the Prior-Posterior Loop: Self-Reflective Molecular Design with Analysis-Driven LLM Iteration
Junyi Gong, Zijie Qiu and Ben Zhong Tang
Can a general-purpose large
language model design molecules with the precision of a seasoned chemist? Current LLM-based frameworks answer this question with scalar feedback loops - generate, score, reject - that amount to informed trial-and-error. Here we show that replacing a single number with the full physicochemical rationale from first-principles calculations transforms the LLM from a
stochastic sampler into a causal reasoner. Our system couples
retrieval-augmented generation with a self-reflection module that feeds orbital energies, atomic charges, and electron densities - rather than compressed scores - back into the design loop. On HOMO-LUMO gap targets from 2.0 to 5.0 eV, this structure-property-relationship (SPR) reflection achieves a deviation as low as 0.0014 eV with a 100% success rate under the SPR+RAG configuration, consistently outperforming scalar-feedback and non-reflective baselines in median and mean deviation. The framework generalizes seamlessly to dipole-moment design, synthetic accessibility optimization, and molecular docking, and proves robust across 7 distinct LLM backbones. These results establish a new paradigm: when the model understands not only that a molecule fails, but why, iterative molecular design becomes genuinely mechanistic.
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