Computational Physics
Omnisolver: An extensible interface to Ising spin-glass and QUBO solvers: adding a distributed GPU brute-force plugin
Konrad Jałowiecki, Jakub Pawłowski, Bartłomiej Gardas et al. (+1 more)
This software update extends Omnisolver with omnisolver-bruteforce, a first-class plugin for exact exhaustive search of QUBO and Ising instances on CUDA-enabled GPUs. The update contributes three components: packaging of the single-GPU brute-force kernel of [Computer Physics Communications 260, 107728, 2021] as a first-class Omnisolver plugin with a uniform Python and CLI interface, a Ray-based distributed framework that splits the search into $2^{k}$ fixed-variable subproblems and dispatches them across multiple GPUs and hosts, with a controller that gathers and merges partial results, and numerical stabilization of the fast float32 ground-state path through compensated incremental updates, periodic exact energy re-anchoring, and best-buffer refresh. Stabilization activates automatically for $N \geq 40$ leaving the public sampler API is unchanged. On dense random Ising instances we measure exhaustive solve times up to $N=60$ on $8\times$ NVIDIA H100 (96 GB) GPUs, reaching $\approx\!3.15$ days at $N=60$ in close agreement with the empirical $t_{N+1}=2\,t_N$ doubling rule, with the distributed sampler reaching the full $\approx\!8\times$ speedup over a single H100 from $N\!\gtrsim\!44$ onwards. The plugin thereby serves as a practical ground-truth oracle for state-of-the-art classical heuristics such as the Simulated Bifurcation Machine.
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All authors Konrad Jałowiecki, Jakub Pawłowski, Bartłomiej Gardas, Łukasz Pawela
Computational Physics
Machine Learning-Driven Chemical Reactor Network Modeling of the Sandia-D Flame
Nicolas J. Tricard, Benjamin C. Koenig and Sili Deng
Turbulent combustion simulations are crucial for many scientific and engineering systems. However, the high cost to fully resolve the complex multiscale and multiphysics behavior makes direct simulation typically infeasible. The equivalent reactor network (ERN) approach attempts to improve computational efficiency by replacing a multidimensional turbulent simulation with a series of much cheaper 0-D and 1-D chemical reactors, providing a surrogate model that retains detailed chemistry at the cost of simplified flow physics. However, their development remains a challenge, often requiring either expert analysis, or automated approaches that sacrifice accuracy. In this work, we develop an automated machine-learning-assisted framework for constructing ERNs of the Sandia-D turbulent methane/air flame. Principal component analysis is first used to reduce high-dimensional thermochemical computational fluid dynamics (CFD) data to a low-dimensional latent space, where k-means clustering identifies physically interpretable flame regions used to initialize a reactor-network graph. This initialization is then refined using finite-difference gradient descent wrapped around non-differentiable Cantera reactor simulations. Across 30 RANS simulations spanning a range of pilot temperatures and inlet methane compositions, the optimized 7-reactor ERN achieves a maximum-temperature $R^2$ score of 0.7945 while preserving a $\sim6000\times$ speedup over the CFD solver. Outlet CO prediction remains more challenging, with a final $R^2$ score of $-0.4183$, but improves substantially from the unoptimized clustering initialization. These results show that unsupervised thermochemical feature extraction can provide effective physics-informed initializations for ERN construction, while gradient-based refinement can significantly improve predictive accuracy without manual reactor-network design.
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Optics
Ultrafast Terahertz and Optical Spectroscopy under Synergetic Extreme Conditions
Xinbo Wang, Tao Dong, Jianlin Luo et al. (+1 more)
Elucidating and manipulating emergent phases in complex materials requires direct access to their low-energy collective modes. Terahertz (THz) time-domain and ultrafast optical spectroscopies have emerged as indispensable experimental tools, enabling the probing of intrinsic electrodynamics and the coherent control of non-
equilibrium states. At the Synergetic Extreme Condition User Facility (SECUF), we have developed a suite of intense ultrashort light sources covering the near-infrared, mid-infrared, and THz spectral ranges. By integrating these strong-field pulses with extreme sample environments, such as low temperatures, strong
magnetic fields, and high pressures, we have established several state-of-the-art spectroscopy platforms. In this article, we outline the technical specifications of each setup and highlight representative user experiments. The presented results underscore the exceptional capability of the THz experimental unit (A4-2) to explore ultrafast dynamics across a multi-parameter thermodynamic phase space.
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All authors Xinbo Wang, Tao Dong, Jianlin Luo, Nanlin Wang
Applied Physics
AlN Gate Interlayer for UWBG AlGaN Transistors with Breakdown Field >6.9 MV/cm and PFOM >1.8 GW/cm2
Seungheon Shin, Jonathan Pratt, Joe McGlone et al. (+5 more)
We report the demonstration of regrown epitaxial AlN gate interlayers with ultra-wide bandgap (UWBG) AlGaN polarization-graded field effect transistors (PolFETs). The introduction of the epitaxial AlN gate interlayer enables significant improvement in breakdown strength, with average breakdown field exceeding 6.94 MV/cm, which represents state-of-the-art for lateral field effect transistors, while maintaining excellent on-state current density exceeding 1 A/mm. The integration of epitaxial AlN enables state-of-the-art power-switching figure of merit exceeding 1.87 GW/cm2 at a breakdown voltage exceeding 1.45 kV. This work shows the potential of UWBG AlGaN for next-generation high-power switching and RF applications with enhanced device performance established by a high-quality epitaxially regrown AlN gate interlayer.
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All authors Seungheon Shin, Jonathan Pratt, Joe McGlone, Yinxuan Zhu, Brianna A. Klein, Andrew Armstrong, Andrew A. Allerman, Siddharth Rajan
Fluid Dynamics
Scale-invariance and characteristic length scale for the large-scale vortices in geostrophic convective turbulence with friction
Guang-Yu Ding, Tian-Yi Pei, Hang-Yu Zhu et al. (+1 more)
In geostrophic convective
turbulence, large-scale
vortices (LSVs) emerge through upscale energy transfer and are commonly regulated by large-scale friction. Yet the role of friction in setting the LSV size remains poorly understood. Here we perform direct numerical simulations of rotating Rayleigh-Benard convection with a linear friction term $\alpha\mathbf{u}$. Contrary to the classical prediction $L_\alpha\sim\alpha^{-3/2}$ obtained from the Kraichnan-Leith-Batchelor (KLB) theory, we find that the LSV radius follows $R_{LSV}\sim\alpha^{-1/2}$. This discrepancy originates from the energy spectrum of the barotropic (2D)
manifold, which exhibits $E_{2D}(k)\sim k^{-3}$ over the range of upscale energy transfer, rather than the canonical $k^{-5/3}$ scaling. To explain this behavior, we analyze the energy pathways of the barotropic
manifold and show that the inverse transfer is strongly nonlocal, coupling a broad range of intermediate scales directly to the cutoff scale. We propose that this coupling leads to a balance between the local and large-scale shear strain rates, resulting in a scale-invariant coarse-grained vorticity. The resulting prediction $E_{2D}(k)\sim k^{-3}$ is supported by circulation statistics exhibiting $\langle|\Gamma(r)|\rangle\sim r^2$. The observed $k^{-3}$ spectrum naturally yields the scaling $R_{LSV}\sim\alpha^{-1/2}$. These results provide a physical interpretation for the widely observed $k^{-3}$ spectrum in condensation-dominated
turbulence and suggest that LSV-size estimates based on the classical $k^{-5/3}$ spectrum may be significantly biased in geophysical and astrophysical flows.
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All authors Guang-Yu Ding, Tian-Yi Pei, Hang-Yu Zhu, Ke-Qing Xia
Chemical Physics
Vibrations Drive Ultrafast Intersystem Crossing of a Photoexcited Cr(III) Complex
Ying You, James K. McCusker, Arshad Mehmood et al. (+1 more)
The Cr(III) coordination complex serves as an archetypical 3d transition metal system for probing ultrafast excited-state dynamics with
spin conversion due to its intrinsic intersystem crossing (ISC) pathway, $^{4}T_{2g} \rightarrow ^{2}E_{g}$, upon photoexcitation. Here we conduct ab initio molecular dynamics simulations in the $^{4}T_{2g}$ state of a model Cr(III) coordination complex, followed by analyses of multireference electronic structure properties. Across 50 trajectories, the compound retains a persistent Jahn-Teller distortion in the excited state, while exhibiting prominent symmetric metal-ligand bond stretching vibrations with frequencies of 219 cm$^{-1}$ and 465 cm$^{-1}$. State-averaged complete active space self-consistent field (SA-CASSCF) calculations obtain two corresponding normal modes at 225 cm$^{-1}$ and 487 cm$^{-1}$ with symmetric stretching character. The lower-frequency twisting/scissoring mode strongly modulates the $^{4}T_{2g}/^{2}E_{g}$ energy gap, periodically zeroing the energy gap, whereas
spin-orbit coupling is essentially invariant to vibrational motion ($\approx$ 60-80 cm$^{-1}$). Furthermore, calculations of single-point excited-state absorption from $^{2}E_{g}$ to a higher ligand-to-metal charge-transfer (LMCT) state indicate that the coherences previously observed in transient absorption spectra arise from
nuclear motion on the $^{2}E_{g}$ surface. These results provide insights into the correlation between vibrational motion and electronic transitions, which can facilitate rational molecular design of transition metal complexes with desired excited-state properties by leveraging ligand versatility.
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All authors Ying You, James K. McCusker, Arshad Mehmood, Benjamin G. Levine
Geophysics · Cross-listed from astro-ph.IM
Data-Driven Forecasting of three-Component Seismograms Using Transformer Architectures
Waleed Esmail, Stuart Russell, Jana Klinge et al. (+2 more)
Forecasting seismic waveforms beyond observed data remains challenging due to the nonlinear, dispersive, and multi-scale nature of seismic wave propagation. In this work, we introduce SeismoGPT, a
transformer-based autoregressive model designed to forecast three-component seismic waveforms directly in the time domain.
Forecasting is formulated as a physically constrained continuation problem in which the model receives waveform context beginning at the P-wave arrival and extending a defined time beyond the S-wave arrival, after which future motion is generated recursively without access to ground-truth samples. Evaluation is performed on synthetic seismograms spanning source depths of 5–100\,km, epicentral distances of 10–90$^\circ$, and magnitudes $3 \leq M_w \leq 7$. To disentangle the effects of context length and prediction horizon, we define three evaluation configurations using a distance-normalized context ratio and fixed prediction horizons of 120 and 240\,s. Across all configurations, the model achieves a median normalized cross correlation of 0.93 or higher. Analysis of representative forecasts shows that successful predictions preserve both phase coherence and spectral energy distribution. Where failure cases arise, this is primarily due to gradual phase drift during autoregressive rollout rather than unphysical signal generation. These results demonstrate that
transformer-based sequence models can learn stable dynamical continuation of seismic wavefields, highlighting the potential of foundation-model approaches for physics-driven time-series
forecasting. There are potential applications of this methodology in seismic warning and hazard mitigation, particularly for next-generation gravitational-wave observatories, such as the Einstein
Telescope.
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All authors Waleed Esmail, Stuart Russell, Jana Klinge, Alexander Kappes, Christine Thomas
Plasma Physics
Coordinate-invariant flux-surface Fourier analysis in tokamaks
Matthew Pharr, Evan Bursch, Nikolas Logan et al. (+3 more)
The
Fourier spectra of resonant quantities in tokamaks depend on the choice of
magnetic coordinates, and an area weighting of the
Fourier integrand preserves the resonant coefficients on rational surfaces. That result constrains only the resonant interior; the coordinate dependence of the external
Fourier spectrum, which determines the coupling to Resonant
Magnetic Perturbation (RMP) coils and error-field penetration, was left untreated. This paper shows that pairing a square-root-area weighted
vacuum field perturbation with a full-area-weighted resonant field yields a coupling
matrix C whose singular values are invariant under coordinate transformations and whose right singular vectors reconstruct to a consistent real-space field pattern across coordinate systems, completing the coordinate-invariance picture for the
plasma-3D-field coupling paradigm. GPEC calculations confirm the analytic result and show that improperly weighted coupling
matrices can produce dominant modes whose overlap with the
vacuum field perturbation differs by a factor of $2--3$ between coordinate systems for strongly shaped, low aspect ratio
equilibria, with the discrepancy growing with inverse aspect ratio. The same coordinate dependence afflicts alternative formulations such as the three-mode metric or zeroing the $q=2$ resonant field without proper weighting. The result applies to any tool computing
Fourier spectra of resonant or external quantities on flux surfaces.
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All authors Matthew Pharr, Evan Bursch, Nikolas Logan, Priyansh Lunia, Jong-Kyu Park, Carlos Paz-Soldan
Fluid Dynamics
Energy Transfer Mechanisms in Wake-Modulated Transonic Flutter
Vedasri Godavarthi, Jacob Turner, Jung-Hee Seo et al. (+1 more)
Transonic flutter is a detrimental aeroelastic instability that can generate large-amplitude structural oscillations, leading to severe vibration, fatigue damage, reduced operational limits, and potentially catastrophic structural failure. Incoming wake disturbances can further amplify this instability, making it critical to identify the underlying aerodynamic mechanisms responsible for predicting and controlling flutter onset. The underlying flow physics is complex with nonlinear interactions between the wake and the wing, shock motion, shock-induced flow separation,
vortex shedding and the wing motion. In this study, we perform high-fidelity direct numerical simulations of a sinusoidally pitching NACA0012 airfoil with an underwing cylinder at various transonic Mach numbers and a Reynolds number of 10,000. Through energy maps, we identify that the addition of the cylinder significantly expands flutter boundaries compared to an airfoil-only system. We extend the force partitioning method to partition the power transferred between the flow and the airfoil for compressible flows. Application of this approach to distinct regions of the flow domain indicates that the gap flow between the wing and the cylinder is the dominant contributor to the energy transfer from flow to the wing. The blockage effects due to the cylinder cause flow acceleration on the wing which further enhances the tendency for flutter. We investigate cylinder placement relative to the airfoil to reveal that flutter is enhanced only when the cylinder is placed upstream of the pivot point on the airfoil. The current study highlights how such partitioning methods can parse force and energy transfer mechanisms in complex, unsteady high-speed flows.
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All authors Vedasri Godavarthi, Jacob Turner, Jung-Hee Seo, Rajat Mittal
Fluid Dynamics
Symmetry Breaking and Restoration in Turbulent Thermal Convection Arises from the Competition Between Advection and Buoyancy
Guang-Yu Ding, Fang Xu and Ke-Qing Xia
Spontaneous
symmetry breaking (SSB) remains poorly understood in thermal convection, but hints may be found from its restoration. We hereby compare the two convection systems: experiments with
polymer additives, and simulations with linear friction. We observe the restoration of similar symmetric flows in both these systems. Additionally, restoration coincides with enhanced, time-symmetric velocity-buoyancy correlation, and a sharp drop in the normalized buoyancy-response time. These results indicate buoyancy predominance: velocity is statistically slaved to buoyancy and preferentially remains vertical. The predominance of buoyancy provides a local orientation mechanism, which is necessary for restoring the
symmetry of the system. Conversely, this orientation mechanism is lost locally in canonical convective flows, thus SSB naturally occurs in Rayleigh-Bénard convection. Our results suggest that the breaking and restoration of
symmetry in thermal convection are both attributable to the competition between advection and buoyancy.
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Plasma Physics
Kinetic Theory for Electronic Transport Properties of Warm Dense Matter: Chapman-Enskog Solution of the Uehling-Uhlenbeck Equation
Lucas J. Babati, Nathaniel R. Shaffer, Louis Jose et al. (+1 more)
A kinetic theory is developed to describe the electrical conductivity, thermal conductivity, and electrothermal coefficients in warm dense
plasmas. It models electron degeneracy using the Uehling-Uhlenbeck equation, diffraction by computing scattering cross sections
quantum mechanically, and strong coupling by treating the scattering events using the potential of mean force. A key advancement detailed here is the development of a Chapman-Enskog solution of the Uehling-Uhlenbeck equation for hydrodynamic transport coefficients. The result is a model which accurately predicts transport coefficients spanning from warm dense matter conditions through hot dilute
plasmas, including the influence of electron-electron interactions. Results are compared with
quantum molecular dynamics simulations, experiments, and other models. The present method is able to capture the ”Spitzer” terms in the classical
plasma limit, while also capturing the correct degenerate limit. The transition between these limits in the warm dense matter regime is explained in terms of the availability of states for electron scattering.
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All authors Lucas J. Babati, Nathaniel R. Shaffer, Louis Jose, Scott D. Baalrud
Atmospheric and Oceanic Physics · Cross-listed from cs.LG
Scalable Uncertainty Quantification for Extreme Weather Forecasting via Empirical Neural Tangent Kernels
Jose Marie Antonio Miñoza, Rex Gregor Laylo and Sebastian C. Ibañez
Deep learning weather models now match numerical weather prediction accuracy while running orders of magnitude faster, but produce deterministic forecasts without uncertainty estimates, a critical gap for high-stakes decisions during extreme weather events. This paper proposes Neural Tangent Kernel-based uncertainty quantification (NTK-UQ) using last-layer empirical features. Theoretical analysis predicts that UQ quality is architecture-dependent through two mechanisms. First, a variance collapse mechanism explains when UQ fails: when the
eigenvalue truncation rank approaches the effective rank of the feature space, the GP correction term consumes nearly all prior variance, destroying discrimination between tropical cyclones and routine conditions; architectures with concentrated spectra (spectral operators) require aggressive truncation ($k \leq 10$), while
attention-based models tolerate full-rank computation. Second, decomposition performance depends on the non-Gaussian, heavy-tailed structure of extreme weather: Independent Component Analysis exploits higher-order statistics (kurtosis, negentropy) to isolate heavy-tailed extreme-event features, achieving higher discrimination than singular value decomposition, which captures only second-order variance. A data-driven selection rule chooses ICA or SVD from the feature eigenspectrum concentration ratio, correctly prescribing the superior decomposition for all four evaluated architectures. Compared to split conformal prediction (the natural post-hoc baseline), NTK-UQ achieves 31–37% sharper prediction intervals at 90% coverage, and uniquely produces adaptive intervals that scale with extreme event severity, which conformal prediction cannot achieve by construction. The framework requires no retraining; inference-time uncertainty requires only a single
matrix-vector product per sample.
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Plasma Physics
Plasma Conductivity from Warm Dense Matter to the Spitzer Limit Using Mean-Force Kinetic Theory
Lucas J. Babati, Nathaniel R. Shaffer, Louis Jose et al. (+1 more)
A theoretical model is developed to compute electronic transport coefficients extending from warm and dense to hot and dilute
plasma conditions. This kinetic theory-based approach models strong Coulomb correlations by treating interactions using the potential of mean force, electron degeneracy using the Uehling-Uhlenbeck equation, and diffraction by computing cross sections
quantum mechanically. The result provides a fast and accurate means to compute electrical conductivity,thermal conductivity and electrothermal coefficients, including contributions from electron-electron interactions. The model enables accurate calculation of materials properties in many warm dense matter systems, including inertial confinement fusion,
stellar evolution, and high energy density
plasma experiments.
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All authors Lucas J. Babati, Nathaniel R. Shaffer, Louis Jose, Scott D. Baalrud
Physics and Society
Using large scale GPS data to reveal EV driver activity patterns beyond charging sessions
Callie Clark, Anne Driscoll, Xiyuan Ren et al. (+4 more)
Accurate insights into electric vehicle (EV) driver behavior are essential for long-term infrastructure planning, grid management, and understanding downstream economic impacts, yet individual level data on EV mobility remains limited. Here, we develop a scalable framework to infer EV ownership and charging behavior from passively collected, high-resolution mobility traces covering over 760,000 drivers across four major U.S. metropolitan areas. We identify likely EV drivers based on distinctive visitation patterns to charging stations and gas stations, frequency of visits, and daily travel behavior, and calibrate cohort size using aggregate EV registration statistics. The resulting EV cohort closely matches official registration data at the zip code level and exhibits charging patterns consistent with independent, charger level
benchmark datasets, providing external validation of the inferred population. Leveraging this inferred cohort, we reconstruct charging events and associated activity patterns to examine how EV drivers interact with surrounding urban amenities. Compared to non-EV drivers, EV drivers exhibit systematically higher visitation rates to nearby cafes and restaurants during charging sessions, revealing significant economic spillover effects. Furthermore, we find EV drivers exhibit trip bundling behavior, visiting more POIs over less time and distance on days where they charge versus all other days. These patterns are not observable in conventional charging session data, which lack behavioral context beyond the charging event itself. Our results demonstrate the potential of using mobility data to enable a richer, behaviorally grounded understanding of the off-plug needs of EV drivers, providing a foundation for optimizing charging infrastructure deployment and co-locating complementary urban amenities in an increasingly electrified transportation landscape.
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All authors Callie Clark, Anne Driscoll, Xiyuan Ren, Salsabil Salah, Marta C. Gonzalez, Joseph Y. J. Chow, Takahiro Yabe
Chemical Physics
Controlling $\langle \hat{S}^2 \rangle$ in Broken-symmetry Density Functional Theory Calculations via Constrained Optimization
Jeronimo Lira and Juan E. Peralta
Accurate determination of
magnetic exchange coupling constants ($J$) from density functional theory (DFT) remains challenging, particularly for open-shell systems where broken-
symmetry (BS) solutions suffer from spurious
spin contamination that systematically exaggerates $J$ values. Several methods have been proposed to address this problem by adjusting the mapping scheme from the DFT energies to the Heisenberg-Dirac-van Vleck effective
spin Hamiltonian energies. In this work, we explore a different route by imposing a constraint to the DFT energy that enforces a target value of the
spin-squared expectation, $\langle \hat{S}^2 \rangle$, using a Lagrange multiplier approach. By explicitly controlling the
spin character of the electronic state, the method attempts to overcome limitations of standard BS calculations to describe
magnetic interactions. As part of the theoretical formulation, we derive analytical expressions for the gradient of the
spin-squared expectation value with respect to the
spin-resolved density
matrices, which are required for the practical implementation of the constraint within a generalized Kohn-Sham scheme. These expressions are general to any single-determinant method and remain valid for arbitrary
spin states. We apply the
spin-constrained approach to the calculation of $J$ couplings and compare with three energy-difference-based schemes for a set of representative systems, including H$_2$He, H$_3$He$_3$ arranged in an equilateral triangle, and a bis($\mu$-hydroxo) Cu(II) complex. Across all cases, the constrained formulation yields systematically lower and more consistent exchange couplings across different density functional approximations. This work establishes a robust and general route for incorporating
spin-state constraints into DFT-based studies of
magnetic exchange interactions.
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Plasma Physics
Experimental Tests of Radio-Frequency Heating Saturation in Ultracold Neutral Plasmas
Bridget O'Mara, Ryan C. Baker and Jacob L. Roberts
For non-resonant
radio-frequency (RF) fields, electron heating in sufficiently collisional
plasmas can be driven primarily by inverse bremsstrahlung absorption. When the quiver velocity v_osc approaches the electron thermal velocity v_th, theory often predicts sublinear scaling of the heating rate with RF power, indicating saturation. We experimentally test this prediction in ultracold neutral
plasmas by finding RF pulses of different amplitude and duration that produce the same electron heating. Despite v_osc being comparable to v_th, we measured no observable saturation. We compare our results to linear response theory (LRT) and a binary collision theory (BCT). The predicted saturation in both theories is sensitive to how common assumptions about cutoff parameters are applied, and agreement with experimental results is much better if quiver-velocity-dependent cutoffs in LRT and BCT are used. Additionally, under our conditions of moderate coupling and magnetization, we find no evidence that RF heating distorts the electron velocity distribution from Maxwell-Boltzmann, indicating saturation from the Langdon effect is suppressed.
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Physics and Society · Cross-listed from cs.GT
Democracy on Rugged Landscapes: Phase Transitions in Optimal Voting Rules
Joshua Nunley
Laws and institutions shape individual outcomes through complex interactions with citizens' diverse circumstances, yet how different voting methods navigate this coupled landscape remains poorly understood. We model collective governance as optimization on NK fitness landscapes, where shared bits (laws) are updated by voting while individual bits (personal traits) remain fixed. A cross-dependency parameter $\alpha$ controls how legislation's effects depend on individual circumstances. We compare eight standard voting methods and a generalized scoring family across landscape ruggedness $K \in \{1,\ldots,20\}$ and $\alpha \in [0,1]$ with 1000 runs per configuration. Under direct democracy, the optimal voting method undergoes sharp phase transitions as a function of landscape complexity: cardinal score voting dominates on smooth landscapes, ordinal scoring with $p=0.35$ at low-to-moderate ruggedness, Borda count across a wide middle range, and
STAR voting at the highest complexity. A two-parameter empirical formula reduces the $(K, \alpha)$ plane to a single complexity axis for visualization. Borda count achieves the highest mean fitness and lowest variance across most of the parameter space. We further introduce a representative democracy model parameterized by identity weight $\beta$ and candidate self-interest $p_{\mathrm{self}}$. Representation reshapes the complexity-dependent structure even under favorable conditions: cardinal score voting dominates across most regimes, with plurality emerging as the top method at high $\beta$ and low-to-moderate $p_{\mathrm{self}}$.
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Biological Physics
Classical Coherence Distinguishes Organisms from Colonies
Yehuda Roth
What distinguishes a multicellular organism from a colony? In the first scenario, individual
cells belong to the whole; their function is defined only within the organismal context. In a bacterial colony, each
cell retains autonomy; the collective is merely a sum of separable parts. This distinction parts that belong to a unified whole versus parts that remain independent is precisely the definition of coherence in physics: a system described by a single state vector. We introduce a framework for classical coherence in biological systems. Unlike
quantum coherence, which is fragile and decoheres on picosecond timescales in warm In environments, classical coherence is actively sustained by metabolic work. We construct this framework by analogy to the center of mass coordinate of a many body system: a collective mode that encodes the state of the whole. Translating this to DNA sequence space, we define a Lagrangian formalism where genetic sequences play the role of coordinates and mutation rates represent velocities. The resulting Euler-Lagrange equations yield a collective coordinate representing organismal coherence. A key prediction of our model is that coherent organisms exist in a superposition of cellular configurations that collapse upon measurement. This produces broad variance in infection outcomes across identically prepared samples, whereas incoherent colonies yield consistent, repeatable responses. To verify this prediction, we propose an experimental test using Dictyostelium discoideum, whose
cells can exist either as unicellular amoebae or as multicellular slugs. Infecting both states with the same virus and measuring the distribution of Infected
cells will directly validate or falsify our coherence hypothesis.
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Physics and Society
Disaster-induced behavioral change restructures social networks toward bonding ties
Vaidehi Raipat, Daniel Aldrich and Takahiro Yabe
Population displacement following environmental shocks reshapes the spatial organization of social interactions, often fragmenting existing ties and weakening community cohesion. Although social capital is widely recognized as a key determinant of resilience, its dynamic restructuring after disruption remains poorly quantified. Here, we develop a spatially embedded, dynamic network framework that operationalizes social capital as a network of repeated encounter opportunities inferred from large-scale mobility data. We construct temporal co-presence networks at third places to track how socio-spatial networks reorganize under disruption. We apply this framework to communities affected by the 2021 Marshall Fire in Colorado. We find that disaster-induced displacement leads to substantial contraction of socio-spatial networks, with mean weighted degree decreasing by 48%. To isolate underlying mechanisms, we develop two counterfactual models: a random node removal model and a behaviour-informed model in which individuals are removed based on their estimated propensity to evacuate. Both counterfactuals predict substantially lower connectivity than observed, indicating that post-disaster connectivity remains systematically higher than expected based on displacement behavior alone. Structural analysis of the network reveals that this residual connectivity is disproportionately concentrated among bonding ties between sociodemographically similar individuals, while bridging ties are comparatively fragile. Furthermore, interaction becomes increasingly located around third places, suggesting that these places act as spatial anchors for the persistence of social ties under disruption. Together, these findings provide a first empirical view of how behavioral responses to disruption shape community resilience through the reorganization of social networks.
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