Chemical Physics · Cross-listed from cond-mat.mtrl-sci
MANDALA: An E(3)-Equivariant Graph Neural Network Framework for Learning Electronic-Structure Operators with Observable Guidance
Bartosz Brzoza, Wiktoria Szopa, Zakaria Elabid et al. (+5 more)
Electronic-structure calculations based on Kohn-Sham density functional theory remain indispensable in computational materials science and chemistry. Their computational cost, however, limits accessible system sizes and simulation times. At the same time, conventional machine-learning interatomic potentials (MLIPs), which are becoming the workhorse of large-scale materials modeling, usually target only energies and forces. They therefore leave out the
quantum-operator-level information required to reconstruct band structures, densities of states, spatial charge distributions, and other electronic observables. Mandala fills this methodological gap. It is a modular software framework for learning block-sparse electronic-structure
matrices with E(3)-equivariant graph neural networks. The framework is built around a unified representation of atom-resolved
Hamiltonian, overlap, and density
matrices, together with reusable abstractions for basis conversion, sparse block handling, irreducible representation mapping, graph construction, model definition, and training. This design allows Mandala to support heterogeneous chemical compositions, a wide range of neural architecture variants within one workflow, and multiple electronic-structure backends. Mandala evaluates selected observables directly from the predicted operators, including band energy, electron count, density of states, and band structure. This connects electronic-structure learning and observable-guided modeling while retaining a representation tied to
quantum-mechanical operators rather than only scalar or vector targets as in MLIPs. In this form, Mandala is intended to complement atomistic interatomic potential workflows by resolving electronic structure and operator-derived observables within one scalable implementation.
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All authors Bartosz Brzoza, Wiktoria Szopa, Zakaria Elabid, Vincent Martinetto, Varadarajan Rengaraj, Mani Lokamani, Thomas D. Kühne, Attila Cangi
Optics
A generalized Kirchhoff's law of thermal radiation for Floquet media
Sander A. Mann, Dimitrios L. Sounas and Andrea Alù
Kirchhoff's law fundamentally relates thermal emission to absorption. For linear, static, reciprocal media, it equates the emissivity and absorptivity for each direction and frequency, while in nonreciprocal systems emission and absorption are equal when the bias is time-reversed. In time-varying media, however, temporal modulation breaks time-translation invariance, converts frequencies, and enables energy exchange with the modulation drive. As a result, a same-frequency relation between absorptivity and emissivity can no longer be expected. Here, we derive a generalized Kirchhoff's law for linear time-varying Floquet media. We show that the emissivity at a given frequency equals a weighted sum of harmonic-resolved absorptivities of the adjoint system, with weights accounting for thermal occupation and
photon-flux conversion. This relation has both practical and fundamental consequences. In practical terms, it allows emissivity to be calculated from absorption, simplifying the design of time-varying thermal emitters. More fundamentally, it reveals thermal radiation regimes inaccessible in static media. In particular, we identify time-varying structures that exhibit strong emission with negligible absorption at the same frequency for all directions, yielding a near-maximal violation of the conventional form of Kirchhoff's law.
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Geophysics
Automatic Knowledge Graph Construction and Query for Earthquake Catalogs
Yuxin Zhou, Huai Zhang and S. Mostafa Mousavi
In recent years, the number of events in earthquake catalogs has significantly increased due to the utilization of more effective deep learning based detectors and phase pickers but answering open ended questions such as what characterizes this sequence? remains constrained by rigid spatiotemporal windowing and subjective expert interpretation. We present the first systematic application of graph based
retrieval augmented generation GraphRAG directly to raw, tabular catalog records across three independently featured catalogs, a reservoir adjacent swarm, the 2019 Ridgecrest tectonic sequence, and the 2021 Maduo Mw7.4 aftershock sequence. Without the need for manual data structuring, the pipeline builds structurally complete, queryable knowledge graphs for all three. Rigorous evaluation individually verified against catalog derived ground truth and a rule based reference graph exposes failure modes, and four seismology informed
prompt fixes eliminate all targeted fabrications while sharply improving mechanism
reasoning. A vector RAG baseline demonstrates the graph layers distinctive value, catalog wide summarization and temporal stage comparison. In addition, we have identified two main pitfalls that need
attention. GraphRAG thus offers a practical, transferable, near zero cost query interface for earthquake catalogs, where careful prompting ensures the results are consistently accurate and trustworthy.
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Physics and Society
Cumulative suspicion and absorption dynamics in an agent-based Mafia game
Eduardo Velasco Stock and Roberto da Silva
The Mafia game, also known as Werewolf, describes a competition between a coordinated minority whose identities are concealed and a larger good faction composed primarily of uninformed civilians attempting to identify and eliminate them. We introduce an
agent-based formulation in which the daytime decision is not represented by uniform random voting. Instead, randomly paired
agents modify player-specific public suspicion scores according to their roles, and one surviving player is subsequently eliminated with probability proportional to their accumulated suspicion. These scores persist throughout the game, producing a history-dependent
stochastic process with two competing absorbing outcomes: elimination of the mafia or numerical parity between the mafia and the good faction. We investigate the effects of population size, initial mafia size, and the presence of perfectly informed detectives through extensive Monte Carlo simulations. In the absence of detectives, a random-execution approximation predicts that, in the dilute-mafia and early-time regime, the cumulative probability of mafia extinction behaves as $F(\tau)\sim (\tau/N)^{N_m}$, in agreement with simulations as $N_m/N$ decreases. The winning-probability curves also exhibit an empirical finite-size crossover characterized by a population scale $N_c$. Rescaling the population by $N_c$ approximately collapses the curves obtained for different initial mafia populations. Perfectly informed detectives substantially shorten mafia-survival times and introduce an additional dependence on population composition for which the same one-parameter rescaling is insufficient. The model provides a minimal connection between microscopic histories of accusation and the macroscopic absorption statistics of hidden-role games.
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Chemical Physics
Ultrafast configuration changes and anomalous diffusion of an aromatic adsorbate on rare-gas nanoparticles
Arne Morlok, Philipp Elsässer, Yilin Li et al. (+5 more)
Nanoparticles (NPs) exhibit tunable catalytic properties and serve as nanoreactors for controlled multimolecular chemistry. The kinetics and reactivity of such systems are critically governed by the surface binding configurations of adsorbates, their
stochastic fluctuations, and the adsorbate mobility across the nanosurface. However, resolving these properties with sufficient structural, spatial, and temporal resolution remains a major experimental challenge. Here, we study phthalocyanine adsorbates on rare-gas clusters as a test case. By combining high-resolution two-dimensional electronic spectroscopy and molecular dynamics simulations, we reveal the configurational dynamics of the adsorbates and establish a direct relation between these dynamics and the nanoscale properties of the clusters. Our findings indicate sub-diffusive surface motion and trapping of the adsorbate within single surface facets. Such dynamical behavior seems unexpected considering the weak adsorbate-surface interaction and cluster temperatures close to the sublimation point. These results provide direct insight into the ultrafast binding dynamics of molecular adsorbates on nanoscale objects, which is critical for our understanding of the chemistry of such systems.
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All authors Arne Morlok, Philipp Elsässer, Yilin Li, Ulrich Bangert, Felix Riedel, Tanja Schilling, Frank Stienkemeier, Lukas Bruder
Computational Physics · Cross-listed from astro-ph.CO
EFT-Ramses: a code to simulate the effective field theory of dark energy
Nathaniel Ota Woodcock, Sownak Bose, Yunhao Gao et al. (+1 more)
While the standard $\Lambda$CDM paradigm is in excellent agreement with most current cosmological observations, theoretical challenges surrounding the cosmological constant ($\Lambda$) have strongly motivated the exploration of dynamical dark energy (DE) and modified
gravity (MG) models. Investigating the physical nature of the
cosmic acceleration requires N-body simulations to probe the non-linear growth of
cosmic structure and prepare for the high-precision data from Stage-IV surveys. In this paper, we present EFT-RAMSES, a comprehensive extension of the ECOSMOG cosmological simulation code designed to explore non-linear structure formation in DE and MG scenarios. We embed the effective field theory of dark energy (EFTofDE) framework into this new numerical pipeline, utilising the $\alpha$-basis parameterisation to provide a versatile, model-agnostic, computational engine. By consolidating diverse scalar and vector-tensor theories—including the normal and self-accelerating Dvali-Gabadadze-Porrati (DGP) models, cubic Galileons (cubic scalar Galileon (csG), cubic vector Galileon (cvG) and generalised cubic covariant Galileon (GCCG)), and generic effective field theory (EFT) parameterisation—into a single "master" Vainshtein equation, this pipeline bypasses the need for model-specific solvers and easily specialises to any particular model. As validations, we perform high-resolution N-body simulations for the normal-branch DGP (nDGP), csG, GCCG, and EFT models, comparing the resulting matter power spectra against dependent and independent codes such as legacy ECOSMOG and HiCOLA, as well as linear theory, and find excellent agreement. EFT-RAMSES provides a robust and versatile computational tool for precision cosmological tests of DE and MG using upcoming cosmological surveys. The code is available for download from the GitHub EFT-RAMSES repository.
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All authors Nathaniel Ota Woodcock, Sownak Bose, Yunhao Gao, Baojiu Li
Atomic Physics · Cross-listed from hep-ph
Bound-state beta decay of tritium: Path to first observation and novel approach to direct neutrino mass measurement
Evgeny Akhmedov, Thierry Lasserre, Ferenc Glück et al. (+1 more)
Bound-state $\beta$-decay of tritium, the process, in which the final-state electron is created in a bound atomic state of the produced ${\rm ^3He}$ atom instead of freely flying away, is predicted by the standard theory of weak interactions but has not been observed so far. We study the possibility of its experimental observation through the detection of
photons from radiative decay of the excited atomic states of neutral ${\rm ^3He}$ populated by this process. We also propose a novel approach to direct
neutrino mass measurement and sterile
neutrino search based on accurate determination of the speed of the produced ${\rm ^3He}$ atoms through Doppler broadening of the emitted
photon lines.
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All authors Evgeny Akhmedov, Thierry Lasserre, Ferenc Glück, Alejandro Saenz
Biological Physics · Cross-listed from cond-mat.soft
Controlling Turbulent Flows in Compressible Active Liquid Crystals
Dimitrios Krommydas, Paarth Gulati, Aparna Baskaran et al. (+1 more)
Motivated by experiments on light-patterned, quasi-2D active suspensions that exhibit large density variations, we develop a continuum theory of compressible active nematics–suspensions of apolar rods whose orientation is invariant under $\pi$ rotations. Under spatially patterned activity, the extensile isotropic active pressure expels material from high-activity regions and accumulates it in low-activity ones; an exactly solvable scalar reduction shows that the resulting density contrast is governed by a single dimensionless parameter, linear in the compressibility. Using compressibility as a tuning knob, we then steer active
turbulence from high- to low-activity regions and, at sharp activity interfaces, stabilize an analytically tractable dynamical steady state: a one-dimensional chain of
vortices held by soft, activity-induced confinement. Our results establish compressibility as a control parameter for density variations and turbulent flow in active nematic suspensions.
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All authors Dimitrios Krommydas, Paarth Gulati, Aparna Baskaran, M. Cristina Marchetti
Computational Physics · Cross-listed from quant-ph
Efficient computation of real-time correlators using Pauli Propagation
Alexander F. Kemper, Raghav G. Jha, Arnab Bachhar et al. (+3 more)
Pauli propagation has shown promise for classically simulating
quantum dynamics by evolving observables directly in the Heisenberg picture. In this work, we investigate its use for computing real-time two-point time-ordered correlators in one- and two-dimensional
quantum systems. One major limitation of Pauli propagation is the rapid growth in the number of Pauli strings beyond short times. We overcome this limitation by combining accurate short-time Pauli-propagation data with time extension methods based on a positivity condition and the observation that the dynamics are often dominated by a small number of characteristic frequencies. This combined approach extends correlation functions far beyond the directly accessible time window while avoiding the exponential proliferation of Pauli operators. We demonstrate that the resulting correlators retain the relevant dynamical and spectral information. Our results broaden the regime in which classical methods can reliably probe the real-time dynamics of interacting
quantum many-body systems.
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All authors Alexander F. Kemper, Raghav G. Jha, Arnab Bachhar, Goksu C. Toga, Mariano Guerrero Perez, Nicholas J. Mayhall
History and Philosophy of Physics
The geometry-first formulation of gauge theory is not equivalent to the symmetry-first one
Henrique Gomes
This paper argues that the geometry-first and
symmetry-first formulations of gauge theory are not equivalent. They differ in three respects. First, the geometry-first formulation—in which gauge groups arise as
automorphism groups of structured fundamental vector bundles—admits fewer theories when its generating structures are restricted to finite tensorial data. Charged theories with additive structure group $\mathbb{R}$ have no such presentation. Second, even when a
symmetry-first theory has a geometry-first presentation, its principal bundle and matter bundles do not determine which structured vector bundles generated the gauge group. Third, the natural functor from geometry-first generating objects to principal bundles with matter is faithful but neither essentially surjective nor full. Essential surjectivity fails for the diagonal quotient of the Standard Model gauge group on what I call `the classical menu', and for the additive-$\mathbb{R}$ examples on every finite tensorial menu. Fullness fails for a real oriented fibre $\mathbb{R}^{2m}$: the principal-bundle category admits the outer
automorphism of $SO(2m)$ induced by an improper orthogonal map, but no structure-preserving fibre map induces it.
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Optics · Cross-listed from astro-ph.IM
Demonstrating the integration of a photonic lantern with an all-fiber-based nulling interferometer
Jordan Diaz, Rebecca Jensen-Clem, Philip M. Hinz et al. (+12 more)
High-contrast imaging of
Solar System scale
exoplanets and protoplanets demands advancements in instrumentation to access deeper starlight suppression at smaller angular separations than today's state-of-the-art. The multi-mode to single-mode conversion capabilities of photonic lanterns (PLs) provide new avenues to implement techniques such as nulling interferometry due to the inherent spatial filtering of single-mode waveguides. In this work, we present laboratory results on an all-fiber-based focal plane nulling interferometer using off-the-shelf components operating at 1550 nm. We demonstrate the implementation of a PL for coupling light into the instrument, and compare it to the case when
laser light is directly fed into the interferometer. The integration of a PL with the interferometer evidences their potential for feeding photonic-based science instruments. Additionally, we discuss expanding the concept of the instrument for the detection of accreting protoplanets.
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All authors Jordan Diaz, Rebecca Jensen-Clem, Philip M. Hinz, Daren Dillon, Pradip Gatkine, Aditya R. Sengupta, Dan Sirbu, Sarah Tedder, Kevin Bundy, Brian Vyhnalek, Steph Sallum, Matthew C. DeMartino, Stephen Eikenberry, Peter Delfyett, Rodrigo Amezcua-Correa
Optics · Cross-listed from quant-ph
Coincidence free certification and quantification of spatial entanglement with stimulated parametric down conversion
M. G. Damaceno, G. H. dos Santos, N. Rubiano da Silva et al. (+2 more)
Using stimulated emission, a
photon pair source can be characterized by seeding the signal mode with a bright classical beam and measuring the stimulated idler field, thus replacing two-
photon coincidence counting with classical intensity detection. We apply this approach to the continuous transverse spatial degrees of freedom of a down conversion source and show that it is possible to certify spatial
entanglement with only intensity measurements. We demonstrate this capability through variance-based
entanglement and steering witnesses, as well as the Fedorov ratio. This method is useful for studying
entanglement properties of
photon pair sources in conditions where
alignment and
photon counting measurements are difficult and time-consuming.
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All authors M. G. Damaceno, G. H. dos Santos, N. Rubiano da Silva, S. P. Walborn, P. H. Souto Ribeiro
Fluid Dynamics · Cross-listed from nlin.CD
The real butterfly effect: from the pop culture to mathematics and physics
Victor de Jesus Valadão, Erik Aurell, Guido Boffetta et al. (+3 more)
The "butterfly effect", introduced over half a century ago by Edward Lorenz, has shifted from a cornerstone of dynamical systems to a popular metaphor, yet its true physical manifestation in fully developed
turbulence spans a spectrum of phenomena from standard chaotic sensitivity to the recently established concept of Eulerian spontaneous stochasticity. This paper presents an attempt at a systematic synthesis that brings these different but interconnected ideas together within the unifying framework of the Finite Size Lyapunov Exponent (FSLE). The FSLE describes the growth rate of perturbations as a function of their scale, enabling a comprehensive characterization of the multiscale physics of turbulent flows. Using the FSLE and the Sabra shell model, extended to include thermal noise, we bridge the classical, small-scale Lyapunov regime with predictability at large scales and its interpretation in terms of Eulerian spontaneous stochasticity. Moreover, using the FSLE and the Kraichnan model, we also illustrate the closely related phenomenon of Lagrangian spontaneous stochasticity. To complete the spectrum of butterfly effects, we also examine the "literal butterfly" scenario of localized, sub-dissipative perturbations. Ultimately, this synthesis clarifies the physical mechanisms that dictate the fundamental boundaries of
forecasting in high-Reynolds-number flows.
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All authors Victor de Jesus Valadão, Erik Aurell, Guido Boffetta, Massimo Cencini, Stefano Musacchio, Angelo Vulpiani
Data Analysis and Statistics · Cross-listed from hep-ph
Symbolic Extraction of Non-Perturbative Transverse-Momentum-Dependent Distributions from Drell-Yan Data
Cole Granger, Alessandro Bacchetta, Valerio Bertone et al. (+6 more)
We present an analytical parametrization of the non-perturbative transverse-momentum-dependent (TMD) parton distribution function of unpolarized
quarks, extracted from Drell-Yan data using a combination of neural-network fitting and symbolic regression. A factorized neural network is trained directly against experimental cross-section data from fixed-target, Tevatron, RHIC, and LHC experiments at next-to-next-to-next-to-leading logarithmic accuracy, and symbolic regression is subsequently applied to each network component to discover compact analytical expressions. The final formula is selected from a Pareto front in the space of expression complexity and experimental $\chi^2$, yielding a closed-form non-perturbative function with 9 free numerical constants that achieves $\chi^2/\mathrm{ndf}=1.040$ over 482 data points. A non-trivial $x$-$b_T$ cross term is retained even under a sparsity prior that biases it toward zero, indicating a mild but genuine correlation between the longitudinal momentum fraction and the transverse momentum. This work demonstrates that symbolic regression is a viable tool for bridging flexible machine-learning fits and interpretable analytical TMD parametrizations, and opens a systematic path toward data-driven discovery of specific features of non-perturbative QCD.
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All authors Cole Granger, Alessandro Bacchetta, Valerio Bertone, Chiara Bissolotti, Matteo Cerutti, Marco Radici, Simone Rodini, Lorenzo Rossi, Cristiano Fanelli
Physics Education
From Prompt to Embodied Simulation: Using Generative AI to Create AR Physics Learning Tools
Ofek Levy, Joshua Glazer, Noah D. Finkelstein et al. (+1 more)
Spread your thumb and index finger in the air, and a virtual lamp in the room changes color. Computer simulations have a long and well-documented record of supporting physics learning. They can support the understanding of abstract physical concepts by making them interactive and by inviting students to play with parameters and explore them. In this paper, we show how a structured natural-
language prompt can generate a browser-based, hand-controlled augmented-reality (AR) physics simulation, and we describe its use in an introductory physics class.
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All authors Ofek Levy, Joshua Glazer, Noah D. Finkelstein, Yossi Ben-Zion
Accelerator Physics
A corrective agentic hybrid RAG and an operations-grounded evaluation for a scientific facility
Rajat Sainju, Dariusz Jarosz, Hairong Shang et al. (+5 more)
Scientific user facilities accumulate decades of operational knowledge that no single search index covers: electronic logbooks, technical documents, internal wikis, operations chat messages, maintenance records, and live control-system data. We present APS-RAG, Advanced
Photon Source
Retrieval Augmented Generation, a deployed platform that makes the institutional knowledge at the Advanced
Photon Source (APS) accessible to staff through natural-
language queries, along with an operations-grounded evaluation. The
retrieval engine fuses dense, sparse, and knowledge-graph (KG) channels with query-type-adaptive reciprocal-rank fusion, adds a corrective agentic loop, and runs a native-tool ReAct executor over a Model Context Protocol (MCP) tooling layer. We construct APS-Bench, a 50-question, question-answering (QA)
dataset with auditable gold answers. Every
retrieval-augmented variant numerically improves strict vital-nugget recall over a naive BM25 baseline (63.8%), with the full corrective Agentic GraphRAG scoring (70.3%). The cross-encoder reranker contributes significantly to answer quality: removing it and allowing the LLM to score relevance drastically reduces strict vital recall by 32.8%. The graph channel and corrective loop contribute positively as expected, but the performance gains are marginal. Additionally, we also compare the performance of open-source and closed-source LLMs in final answer synthesis. We release the APS-Bench construction methodology, the six-layer evaluation harness, and the underlying codebase, along with the '/aps-rag'
retrieval agent skill framework, to support reproduction and adoption at other facilities. Together, the deployed platform and its operations-grounded evaluation present a promising workflow for trustworthy, statistically grounded AI assistance in facility operations, transferable to other large scientific instruments.
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All authors Rajat Sainju, Dariusz Jarosz, Hairong Shang, Michael Prince, Ryan M. Aydelott, Mathew J. Cherukara, Yine Sun, Michael D. Borland
Chemical Physics
Recommended Second Virial Coefficients for Nitrogen and Oxygen
Robert Hellmann, Giovanni Garberoglio and Allan H. Harvey
We provide recommended values for the second virial coefficient, $B(T)$, and its uncertainty, for molecular nitrogen and oxygen. The temperature range covered is $20-3000$ K for nitrogen and $20-2000$ K for oxygen. The recommendations are based on tuning previously published state-of-the-art ab initio pair potentials so that the $B(T)$ calculated from the potentials match selected high-accuracy experimental data; for nitrogen the tuning utilizes values of $B$ derived from literature density data with greatly reduced uncertainty by analyzing the data with the aid of ab initio calculated higher virial coefficients.
Quantum effects on $B$ are fully included with the path-integral Monte Carlo method. The resulting $B(T)$ have uncertainties similar to those of the best experimental data, but cover a much wider temperature range.
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Optics
Universal Statistics of Energy and Information Flow in Random Electromagnetic Fields
Yuchen Ke, Nandini Bhattacharya, Stefan Rotter et al. (+1 more)
We establish a universal statistical description for the local flow of energy and information in random electromagnetic fields. The longitudinal Poynting flux, written as a Hermitian quadratic form of the transverse electric and
magnetic field components, follows a probability distribution that is completely determined by four
eigenvalues of an electromagnetic covariance
matrix. These flux
eigenvalues quantify forward transport, optical backflow, and polarization mixing, and reduce to the known paraxial and isotropic limits in the appropriate regimes – including strongly nonparaxial fields, where no universal description was known so far. Full-vector simulations of continuous and discrete disordered media confirm this universality. The same framework applies to the recently introduced Fisher-information flux, with the fields replaced by their sensitivity to a parameter, thereby unifying the statistics of local energy and information transport in random light and revealing the reversal of information flow across a parameter-dependent object.
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All authors Yuchen Ke, Nandini Bhattacharya, Stefan Rotter, Fabian Maucher
Fluid Dynamics
A Modified Moving Reference Frame Method for Propeller Resolution
Denis Andreev, Georgios Bletsos, Antonios Kritikos et al. (+2 more)
Accurate resolution of propeller-hull interaction is essential for predicting the self-propulsion point in ship CFD, yet motion-resolving methods such as sliding interfaces (SI) are computationally expensive, while the classical Moving Reference Frame (MRF) approach cannot capture unsteady interaction effects. Partially rotating grid methods bridge this gap by splitting the propeller rotation into a grid-resolved and an MRF component, but the abrupt transition between the rotating and stationary domains introduces discontinuities in the velocity field. This work presents a modified MRF (mMRF) formulation in which the reference-frame rotation rate is scaled by a spatially varying function that decays smoothly from unity near the propeller to zero at the domain interface, restoring velocity and pressure continuity across the boundary. The governing equations are derived and implemented in the RANS solver FreSCo$^+$, verified against the analytical Taylor–Couette solution, and applied to open-water propeller and Japan Bulk Carrier self-propulsion simulations at model scale. Both MRF and mMRF reproduce the principal integral propulsion quantities ($n$, $K_{\mathrm{T}}$, $K_{\mathrm{Q}}$, $1-t$, $1-w_{\mathrm{T}}$, $\eta_{\mathrm{R}}$) accurately, but the mMRF markedly reduces interface discontinuities and non-physical artifacts in the local flow field, particularly at large MRF fractions, at essentially the same computational cost.
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All authors Denis Andreev, Georgios Bletsos, Antonios Kritikos, Niklas Kühl, Thomas Rung
Biological Physics · Cross-listed from cond-mat.stat-mech
Control of morphology and topology in a lattice model of branching morphogenesis
Christian Hanauer, Frank Jülicher and Efe Ilker
We present a
lattice model for morphogen-controlled branching morphogenesis which combines ideas and concepts from non-
equilibrium physics and developmental biology. In this model, the
stochastic occupation dynamics of
cells is coupled with signaling molecules (morphogens) produced by the
cells. We investigate growth patterns governed by morphogen concentration gradients, spanning regimes ranging from the
diffusion-limited aggregation limit to the
stochastic surface growth (Eden model) limit. Moreover, we introduce control over
topology by a local operator and study growth, degrowth, and steady-state dynamics of branched patterns. The
topology-preserving steady-state clusters exhibit a power-law scaling of radius of gyration with cluster size, yielding the exponents $0.68\pm0.01$ in square
lattice and $0.67\pm 0.01$ in hexagonal
lattice.
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Physics and Society
Extreme outbreaks in non-Markovian epidemics on complex networks
Ami Taitelbaum and Michael Assaf
Extreme epidemic risk is controlled by the right tail of the outbreak-size distribution, but this distribution is generally unknown for non-Markovian spreading on networks. Here we determine this distribution by mapping non-Markovian SIR dynamics to an effective Markovian description. We show that arbitrary infection and recovery time statistics can be incorporated through a single edge transmissibility, yielding an effective Markovian process that reproduces the full outbreak-size statistics. For weakly heterogeneous networks, the reduction yields a universal well-mixed semiclassical theory governed by the bond-percolation reproductive number. Outbreak statistics across diverse waiting-time distributions and
topologies collapse onto one predictive curve. For highly heterogeneous and empirical networks, the corresponding effective Markovian dynamics on the network captures the complete distribution. Our results provide a direct route from measured waiting-time distributions to quantitative predictions of network-level extreme-outbreak risk.
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Plasma Physics
Implementation and verification of the avalanche source in a 3D full-f particle-in-cell model of relativistic electrons for studies of tokamak disruptions
Fiona Wouters, Hannes Bergström, Matthias Hoelzl et al. (+3 more)
Tokamak disruptions may lead to the acceleration of some electrons to relativistic energies. These so-called runaway electrons (REs) can multiply exponentially via knock-on collisions with thermal electrons. As the resulting RE avalanche is exponentially sensitive to the pre-disruption
plasma current, multi-MA RE beams may form in large future devices, risking severe localized wall damage. In this work, an energy and momentum conserving knock-on collision operator is implemented in the 3D nonlinear MHD code JOREK for the full-f relativistic hybrid fluid-kinetic model that describes the REs using the particle-in-
cell (PiC) approach both for full-orbit and drift-kinetic markers, which will enable accurate modeling of the RE phase-space dynamics in realistic 3D electromagnetic fields. Such a self-consistent treatment of the RE avalanche and competing losses in the
stochastic fields of MHD-active
plasmas is required to further the understanding of RE transport and phase-space dynamics in self-consistent interaction with the 3D
plasma evolution, which is needed for developing reliable predictions as well as reliable mitigation methods. To make such novel high-fidelity simulations computationally viable, a resampling technique was also implemented to restrict the number of markers. The avalanche model is verified using analytical expressions from literature and applied to a JET-like termination scenario, demonstrating its applicability to realistic 3D MHD active scenarios. Future work on porting to accelerated high-performance computing systems will be needed to cross the long time scales involved, e.g., in periodic termination and re-avalanching that could occur in large devices like ITER.
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All authors Fiona Wouters, Hannes Bergström, Matthias Hoelzl, Guido T. A. Huijsmans, Jan van Dijk, the JOREK team
Plasma Physics
Solution to an unsolved problem in diodes: Limiting current for small emission area and low emission energy
Y. Y. Lau, David P. Chernin, Yves Heri et al. (+1 more)
The maximum current that can be extracted from a diode is a central question in electronic devices, especially for the generation of radiation from microwaves to x-rays. The challenge in its prediction increases significantly when the electron emission is restricted to a small area for which the classical one-dimensional Child-Langmuir law is no longer applicable. We address this unsolved problem using a model that simultaneously includes a small emission area and a small electron emission velocity. New scaling laws are presented for this difficult regime. These results are obtained from three vastly different approaches: a differential equation formulation which provides extremely high resolution, an integral equation formulation which leads to the scaling laws, and a particle-in-
cell simulation which shows the temporal-spatial
evolution. Comparisons of the predicted maximum current among these three approaches are performed over a large range of parameters, paying special
attention to the resolution of the potential minimum in the immediate vicinity of the cathode surface. Corroborations of the scaling laws with experiments on thermionic cathodes and on photoinjectors are indicated. The model consists of a periodic array of electron sheets of finite width in a planar diode, all emitted from a cathode with the same energy. Electron motion is restricted to the direction normal to the cathode. This problem is of interest to the electron device, accelerator, pulsed power, aerospace, and applied mathematics communities.
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All authors Y. Y. Lau, David P. Chernin, Yves Heri, Peng Zhang
Computational Physics
Reframing the coupling force of adaptive resolution simulation in terms of the Liouville-type hierarchy for open systems
Julian F. Hille and Rupert Klein
In this work, we present a novel perspective on the coupling force employed to compensate the interface artifacts prevalent in adaptive resolution simulations (AdResS) of open many-particle systems. We show that a substantial part of this "thermodynamic force" can be framed in terms of the theoretical model of the Liouville-type hierarchy for open systems. The correspondence is made explicit for the case of a simple atomistic fluid, for which a one-dimensional integral expression is derived. This enables the analysis for dependencies of the thermodynamic force on important simulation parameters, which is taken to inspire both simplifications for the numerical calculation of the thermodynamic force and new criteria for its validation that are adequate to the interfacial nature of the problem. The theoretical claims are then verified in a simulation study of the atomistic supercritical Lennard-Jones fluid at different thermodynamic states.
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Fluid Dynamics
The balance between compactness and forecast accuracy of data-driven latent-space reduced-order models in controlled wake flows
Alberto Solera-Rico, Patricia García-Caspueñas, Carlos Sanmiguel Vila et al. (+1 more)
Model-based active flow control requires predictive models that are accurate, stable, and fast enough for real-time optimisation. In controlled wake flows, this is often achieved through Reduced-Order Models (ROMs) that first compress high-dimensional velocity snapshots into a latent space and then learn a time- stepping predictor for the dynamics in the latent space. Here, we study how the choice of the spatial encoder affects the predictability of the resulting latent coordinates for wake flows under control inputs. Using two actuated 2D wake configurations, a simplified truck wake and the fluidic pinball, we compare Proper Orthogonal Decomposition (POD) against nonlinear Convolutional Autoencoders (CAEs) and two types of variational autoencoders for compression, and evaluate several temporal predictors based on Long Short-Term
Memory networks. CAEs achieve higher compression efficiency and sharper short-term reconstructions, but they produce latent dynamics that are more irregular and with broadband spectral content. As a consequence, long-horizon forecasts degrade faster and show a higher probability of catastrophic divergence than POD-based models. POD yields smoother latent trajectories that are easier to learn and extrapolate, leading to more reliable predictions beyond the short- term regime. These results reveal a clear trade-off between compactness and forecast accuracy, and suggest that the stability of the latent dynamics prediction can outweigh maximal compression. This is particularly relevant for control strategies rooted in forecasts of the dynamics, such as model predictive control and reinforcement learning. The findings provide practical guidance for designing actuation-aware, hardware-feasible predictive ROMs for real-time flow control.
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All authors Alberto Solera-Rico, Patricia García-Caspueñas, Carlos Sanmiguel Vila, Stefano Discetti
Biological Physics
Computational modelling reveals advantages of ventricular over atrial cardiomyocytes to power self-guided biorobots
Nathan Lilly, Guy S. Bewick, Claudiu Vasile Giuraniuc et al. (+1 more)
Solid tumours and other hypoxic regions are difficult to treat with conventional systemic drug delivery, which distributes therapeutics non-specifically and increases toxicity in off-target sites. Biohybrid microrobots driven by living
cells offer a self-powered, bio-compatible solution to autonomous targeted delivery, however, existing platforms often require external stimuli to move and locate towards target zones. We present a computational model of an H-shaped biohybrid microrobot, actuated by neonatal rat cardiomyocytes coupled to the body through an anisotropic friction-ratchet generating a net forward locomotion. The model is simulated under normoxic straight-line conditions and hypoxic gradient-steering conditions for two distinct phenotypes, atrial and ventricular. Ventricular
cells produce a $4.35\times$ increase in locomotion speed over atrial
cells at an equivalent cost of transport and a $2.91\times$ greater heading response under an applied gradient, confirming that phenotype is an underexploited design variable. The computational model in this study shows autonomous, gradient-based steering in a cardiomyocyte-based platform and the effectiveness of treating cardiomyocyte phenotype as an explicit design parameter. These findings support the long-term feasibility of cardiomyocyte-driven microrobots for autonomous, self-targeted therapeutic delivery in hypoxic regions and as such offer a route to reducing systemic drug exposure relative to conventional treatment.
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All authors Nathan Lilly, Guy S. Bewick, Claudiu Vasile Giuraniuc, Maria Elena Giannaccini
Medical Physics
Optically Encoded Suspension Microarrays: Materials, Design Strategies, and Future Directions for Multiplexed Bioanalysis
Mikhail Sokolov, Irina S. Kriukova, Alyona Sukhanova et al. (+1 more)
Suspension microarrays based on optically encoded microbeads have become one of the most versatile platforms for multiplexed bioanalysis because they combine solution-phase reaction kinetics, flexible assay design, and high-throughput detection. However, despite more than two decades of intense research, no consensus has emerged regarding the optimal strategies for particle encoding, surface functionalization, and signal decoding. Progress has mainly been driven by incremental improvements in individual materials rather than by systematic comparison of competing technological concepts. This review critically evaluates the main approaches to the fabrication of optically encoded microbeads, including post-synthetic (swelling and layer-by-layer assembly) and in situ encoding strategies, and proposes a mechanistic classification of in situ methods of particle formation into polymerization-driven and confinement-controlled ones. Instead of comparing the fabrication methods solely in terms of encoding capacity, we assess their relative merits in terms of structural control, code stability, scalability, compatibility with biofunctionalization, and suitability for clinical implementation. We further examine the strengths and limitations of organic fluorophores, aggregation-induced emission luminogens,
semiconductor quantum dots, and upconversion nanoparticles and show that no encoding material is universally optimal and that performance is determined by trade-offs between optical properties, manufacturing complexity, and stability in biological media. We argue that future progress will depend not as much on increasing the theoretical number of optical codes as on improving the code reproducibility, minimizing spectral crosstalk and nonspecific interactions, and employing microfluidic fabrication, antifouling surface chemistry, automated spectral decoding, and artificial intelligence-assisted data analysis. These developments are expected to transform suspension microarrays from multiplexed analytical tools into standardized lab-on-a-microbead platforms suitable for next-generation clinical diagnostics.
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All authors Mikhail Sokolov, Irina S. Kriukova, Alyona Sukhanova, Igor Nabiev
Atomic Physics
Serrodyne Matterwave Optics
Jack Roth, Madeline Bernstein, Andrew Christensen et al. (+4 more)
Bragg diffraction for atom interferometry conventionally requires two
laser frequencies whose difference makes the two-
photon transition resonant. We show that this frequency difference can instead be generated by serrodyne modulation, allowing Bragg pulses to be driven with a single-frequency
laser. The serrodyne modulation is performed using an acousto-optic modulator driven with a phase-modulated RF drive, and generated kilohertz-scale frequency shifts on top of the AOM carrier frequency. While in general serrodyne modulation generates strong unwanted frequency components, we find conditions so that the Bragg diffraction pulses, and atom interferometers constructed from them, are robust to these imperfections, opening the door to the development of ultra compact, single frequency Bragg diffraction based atom interferometers with serrodyne modulation implemented by a position modulated retroreflection mirror.
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All authors Jack Roth, Madeline Bernstein, Andrew Christensen, Yuno Iwasaki, Holger Mueller, Zaire Sprowal, Robert Compton
Plasma Physics
Impact of ion-electron collisions on nonlocal ion heat conduction, viscous stress, and diffusion
Nicholas Mitchell, David Chapman and Grigory Kagan
By applying a first-principles reduced kinetic method, this work demonstrates the impact of ion-electron collisions on ion transport for strongly inhomogeneous
plasmas in the nonlocal regime, where collisionality is insufficient to enforce local thermal
equilibrium due to sharp gradients. Ion heat conduction and viscous stress in both unmagnetized and magnetized
plasmas are considered, as well as inter-species
diffusion in multi-species
plasmas. Most notably, even for equal ion and electron temperatures, ion-electron collisions are found to substantially modify the peak nonlocal heat flow, whereas nonlocal preheats are strongly suppressed since streaming suprathermal particles are further out in the tail of the cold ion distribution where ion-electron collisions become dominant.
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Medical Physics
Sleep-related forcing of cerebrospinal fluid streaming transport through the third ventricle
Pengcheng Zhao, Luke McTavish and Christoph Bruecker
Quasi-periodic pressure pulses associated with cardiac and respiratory activity generate reciprocating cerebrospinal fluid (CSF) motion in the human ventricles. While weak and more regular during wakefulness, oscillatory fluid motions have recently been found to be much more exaggerated during NREM sleep, appearing as large flushes in successive pulse trains. The present study investigates whether this forcing can induce greater residual streaming through the human ventricles. Transient CFD simulations were performed in the third ventricle under awake and sleep-related flow pulses. Sleep-related forcing produced clear caudal and rostral preferential pathways and less overall recirculation in the cavity. The streaming flux relative to the stroke volume increased from a low value of about \(0.540\%\) to \(28.4\%\) during sleep. Thus, sleep-related forcing changes not only the magnitude of reciprocating fluid displacement but also the relative contribution of residual streaming. These findings demonstrate differences in hydrodynamic organisation in the third ventricle and highlight the importance of this organisation for intraventricular fluid exchange.
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