Applied Physics · Cross-listed from eess.SY
Multidisciplinary Design Optimization for Wave-Driven Desalination Systems
Nate DeGoede and Maha N. Haji
Wave-driven desalination systems are an innovative solution to the global freshwater crisis, leveraging the complementary characteristics of seawater reverse osmosis and wave energy converters. However, the high costs of this system pose a significant barrier to widespread adoption. Optimization can help these systems reach a more competitive levelized cost of water, but the highly coupled nature of the system necessitates a multidisciplinary design optimization approach. This paper presents a holistic, multidisciplinary design optimization framework for wave-driven desalination system design, integrating models for wave energy converter hydrodynamics, power take-off transmission, seawater reverse osmosis constraints, and economic analysis. This study demonstrates the impact of multidisciplinary design optimization for wave-driven desalination systems, resulting in a 69.5% reduction in levelized cost of water within this modeling framework compared to a nominal design. We demonstrate that multidisciplinary design optimization outperforms two different sequential design approaches, yielding lower levelized costs of water and substantially different optimal designs. The multidisciplinary design optimization results suggest major design changes compared to designs found in the literature. Notably, smaller wave energy converters and larger pistons, along with smaller accumulators and larger seawater reverse osmosis plant installations, are preferred within this modeling framework. These design trends are consistent across a range of sea states, suggesting potential generalizability beyond a single location. This study demonstrates the importance of holistic modeling and co-design for wave-driven desalination systems and establishes an effective optimization framework for future studies to build upon.
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Space Physics
First Observation of Multiple Very-Near-Earth Reconnection Events During a Single Storm Main Phase
Fekireselassie Beyene, Vassilis Angelopoulos, Christine Gabrielse et al. (+11 more)
For the first time, this paper presents three very-near-Earth reconnection (VNERX) events observed within the same 12-hour-long storm main phase. The THEMIS inner probes observed the hallmarks of three episodes of tailward retreating x-lines positioned between
magnetic local time (MLT) 23-24 and radial distance 12-13 Earth radii (RE). The events occurred within a thin current sheet, < 1 RE thick. Simultaneously, dispersionless energetic particle injections above 10s of keV and
magnetic field dipolarizations were observed near and earthward of geosynchronous altitude by the KOMPSAT and Arase satellites. Arase observed earthward flow bursts at or below geosynchronous altitude via ExB enhancements, suggesting VNERX ejecta proceed below geosynchronous orbit. These observations demonstrate that VNERX events, which predominantly occur during the storm main phase, can be frequent and essential for driving injections that can effectively power the ring current. However, they can be observed only at the pre-midnight sector, close to the neutral sheet.
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All authors Fekireselassie Beyene, Vassilis Angelopoulos, Christine Gabrielse, Yoshizumi Miyoshi, Iku Shinohara, Shoichiro Yokota, Satoshi Kasahara, Kunihiro Keika, Tomoaki Hori, Yasumasa Kasaba, Yoshiya Kasahara, Ayako Matsuoka, Mariko Teramoto, Kazuhiro Yamamoto
Chemical Physics
Stepping up enhanced rate calculations with EATR-flooding
Nicodemo Mazzaferro, Willmor J Pena Ccoa, Pilar Cossio et al. (+1 more)
Several recent methods have shown that it is possible to compute rate constants of very slow biomolecular processes using simulations where a time-dependent bias is added along one or several collective variables (CVs). We previously reported the exponential average time-dependent rate (EATR) method, which can improve upon these approaches by accounting for how efficiently the external biasing potential modifies the observed rate using a learned CV-quality factor $\gamma$. This results in more accurate rate estimates using the same data when biasing a suboptimal coordinate. However, as formulated EATR depended on the biasing potential varying over time to properly determine the biasing efficiency, which limits the method's applicability to quasi-static biasing schemes such as “flooding” or on-the-fly probability enhanced sampling (OPES). Here, we present the EATR-flooding approach, which generalizes our method by replacing the need for a time dependent bias by instead varying (stepping up) the strength of the biasing potential across multiple sets of simulations. We implement this approach as an open-source Python library, and demonstrate that this approach is accurate without substantial loss of efficiency compared to standard EATR for a coarse-grained
protein system, and also show good performance on a fully atomistic cavity-ligand model. Two additional appealing features of EATR-flooding are an internal check for over-biasing and the fact that only a single $\gamma$ parameter is predicted for a given choice of CVs, as compared to our earlier results where $\gamma$ empirically depended on biasing rate. Finally, we believe EATR-flooding applies not only to OPES simulations but more generally to CV biasing enhanced sampling approaches, making it broadly useful.
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All authors Nicodemo Mazzaferro, Willmor J Pena Ccoa, Pilar Cossio, Glen M. Hocky
Geophysics · Cross-listed from cs.AI
Unsupervised Electrofacies Classification and Porosity Characterization in the Offshore Keta Basin Using Wireline Logs
Hamdiya Adams, Theophilus Ansah-Narh, Daniel Kwadwo Asiedu et al. (+6 more)
This study presents an unsupervised machine learning workflow for electrofacies analysis in the offshore Keta Basin, Ghana, where core data are scarce. Six standard wireline logs from Well~C were analysed over a depth interval comprising approximately $11{,}195$ samples. K-means clustering was applied in multivariate log space, with the clustering structure evaluated using inertia and silhouette diagnostics. Four clusters were identified, supported by an average silhouette coefficient of approximately $0.50$, indicating moderate but meaningful separation. The resulting electrofacies exhibit systematic, depth-continuous patterns associated with variations in clay content, porosity, and rock framework properties, forming a geological continuum from shale-dominated to cleaner sandstone-dominated units. The results demonstrate that log-only, unsupervised clustering supported by quantitative metrics provides a robust and reproducible framework for subsurface characterisation. The proposed workflow offers a practical tool for early-stage formation evaluation in frontier offshore basins and a foundation for future integrated studies.
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All authors Hamdiya Adams, Theophilus Ansah-Narh, Daniel Kwadwo Asiedu, Bruce Kofi Banoeng-Yakubo, Marcellin Atemkeng, Thomas Armah, Richmond Opoku-Sarkodie, Rebecca Davis, Ezekiel Nii Noye Nortey
Physics Education
Beyond Project-Based Learning: Conference-Style Writing as Authentic Assessment in Interdisciplinary Quantum Engineering Education
Nischal Binod Gautam and Enrique P. Blair
Project-based learning is recognized as an effective approach for improving engagement and applied understanding in STEM education. In
quantum engineering courses, however, the question is no longer only whether students benefit from projects but how those projects should culminate if the goal is authentic disciplinary preparation. This paper examines the educational role of a conference-style paper requirement embedded within a project-based learning implementation for an introductory
quantum mechanics course for engineers. We use post-course survey responses from students in a pilot run of the course. We evaluate perceived effects on conceptual understanding, scientific communication, research readiness, and attitudes toward the writing requirement itself. The results suggest that students viewed the project as beneficial for engagement, confidence, and technical skill development, while the conference-style paper emerged as a demanding but meaningful component of the experience. We argue that once PBL has been established in
quantum mechanics education, conference-style writing can serve as an extension of that model, especially for graduate students. The findings support retaining the conference-paper requirement with improved scaffolding.
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Data Analysis and Statistics · Cross-listed from cs.LG
Anomaly Detection in Soil Heavy Metal Contamination Using Unsupervised Learning for Environmental Risk Assessment
Isaac Tettey Adjokatse, Samuel Senyo Koranteng, George Yamoah Afrifa et al. (+6 more)
Soil contamination by heavy metals poses a persistent environmental and public health concern in rapidly urbanising regions of Ghana, particularly at unregulated waste disposal sites. This study applies an unsupervised machine learning framework to detect and characterise anomalous heavy metal contamination patterns in soils from twelve waste sites and residential controls in the Central Region, of Ghana. Concentrations of eight metals (As, Cd, Cr, Cu, Hg, Ni, Pb, Zn) were analysed alongside standard health risk indices, including the Hazard Index (HI) and Incremental Lifetime Cancer Risk (ILCR). Isolation Forest and PCA reconstruction error each identified $12$ anomalous samples ($15.4\%$ of $78$ samples), while DBSCAN detected no density-isolated noise points. A consensus approach isolated six robust anomalies ($7.7\%)$, all spatially concentrated at a single site (S3). Anomalies exhibited approximately $70$–$80\%$ higher mean HI values than normal samples, with all consensus anomalies exceeding the HI$=1$ threshold. PCA reconstruction error showed a strong positive association with HI ($r \approx 0.8$), indicating consistency between multivariate deviation and health risk. Three distinct anomaly types were identified: extreme Cu enrichment at S3, anomalously low Ni at S4/S5, and moderate multi-metal (Pb–Zn) co-elevation at S9–S12. The results demonstrate that unsupervised machine learning provides granular, objective insight beyond aggregate indices, enabling targeted site prioritisation and risk-informed environmental management.
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All authors Isaac Tettey Adjokatse, Samuel Senyo Koranteng, George Yamoah Afrifa, Theophilus Ansah-Narh, Marcellin Atemkeng, Joseph Bremang Tandoh, Kow Ahor Essel-Yorke, Richmond Opoku-Sarkodie, Rebecca Davis
Optics · Cross-listed from cs.AI
End-to-end autonomous scientific discovery on a real optical platform
Shuxing Yang, Fujia Chen, Rui Zhao et al. (+10 more)
Scientific research has long been human-led, driving new knowledge and transformative technologies through the continual revision of questions, methods and claims as evidence accumulates. Although large
language model (LLM)-based
agents are beginning to move beyond assisting predefined research workflows, none has yet demonstrated end-to-end autonomous discovery in a real physical system that produces a nontrivial result supported by experimental evidence. Here we introduce Qiushi Discovery Engine, an LLM-based agentic system for end-to-end autonomous scientific discovery on a real optical platform. Qiushi Engine combines nonlinear research phases, Meta-Trace
memory and a dual-layer architecture to maintain adaptive and stable research trajectories across long-horizon investigations involving thousands of LLM-mediated
reasoning, measurement and revision actions. It autonomously reproduces a published transmission-
matrix experiment on a non-original platform and converts an abstract coherence-order theory into experimental observables, providing, to our knowledge, the first observation of this class of coherence-order structure. More importantly, in an open-ended study involving 145.9 million
tokens, 3,242 LLM calls, 1,242 tool calls, 163 research notes and 44 scripts, Qiushi Engine proposes and experimentally validates optical bilinear interaction, a physical mechanism structurally analogous to a core operation in
Transformer attention. This AI-discovered mechanism suggests a route towards high-speed, energy-efficient optical hardware for pairwise computation. To our knowledge, this is the first demonstration of an AI agentic system autonomously identifying and experimentally validating a nontrivial, previously unreported physical mechanism, marking a milestone for research-level autonomous
agents.
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All authors Shuxing Yang, Fujia Chen, Rui Zhao, Junyao Wu, Yize Wang, Haiyao Luo, Ning Han, Qiaolu Chen, Yuze Hu, Wenhao Li, Mingzhu Li, Hongsheng Chen, Yihao Yang
Fluid Dynamics
Compressible Navier-Stokes Flow in Schrödinger-Type Variables
James R. Beattie, Max Sokolova, Khush Negandhi et al. (+1 more)
Fluid equations are nonlinear, dissipative, and non-
Hamiltonian, which makes their relation to Schrödinger
evolution and
quantum algorithms nontrivial. We derive an exact Eulerian Cole-
Hopf-type reformulation of isothermal compressible Navier-Stokes (NS) flow in Schrödinger-type amplitude variables. To our knowledge, this gives the first exact Cole-
Hopf-type Schrödinger-variable reformulation of compressible NS flow. In two dimensions, a Helmholtz decomposition separates the velocity into compressive and vortical potentials, whose logarithmic transforms yield two scalar imaginary-time Schrödinger-type equations with nonlinear self-consistent potentials. We show that the mixed density-compressive amplitude $\Psi_\alpha=\rho^\alpha\Theta^{1-2\alpha}$, where $\rho$ is the density, $\Theta$ is the compressive amplitude, and $\alpha\neq 0,\,1/2$, satisfies a nonlinear Schrödinger-type equation with a vector-potential-coupled
Laplacian. The transformed system is exactly equivalent to compressible NS and is nonlocal only through Helmholtz and Poisson projections. In three dimensions, the density-carrying equation retains the same vector-potential-coupled structure, while the solenoidal sector admits a compressible analogue of Ohkitani's incompressible NS Cole-
Hopf formulation. Unlike
unitary hydrodynamic Schrödinger-flow representations, the present equations are imaginary-time heat or drift-
diffusion equations with self-consistent potentials, but they remain an exact change of variables for compressible NS. A two-dimensional Kelvin-Helmholtz unstable shear-layer calculation verifies the transformed equations against a direct compressible NS simulation. The formulation exposes operator structures that may be useful for reduced flow descriptions,
quantum algorithms for operator
evolution, and
quantum partial differential equation solvers.
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All authors James R. Beattie, Max Sokolova, Khush Negandhi, Bart Ripperda
Chemical Physics
Graph Neural Network Predictions of Carbon 1s Binding Energies with Near-Experimental Accuracy
Adam E. A. Fouda, Joshua Zhou, Rodrigo Ferreira et al. (+10 more)
Graph neural networks are promising architectures for fast, accurate and transferable predictions of core-electron binding energies, which depend on the local bond environment. Here we present a graph neural network model for predicting carbon 1s core-electron binding energies in organic molecules. The model is trained with multiconfiguration pair-density functional theory on 8637 carbon atoms in 2116 molecules with 4-16 atoms and evaluated against 570 experimental values in 113 different molecules containing 3-45 atoms. Previous work benchmarked a mean absolute error of 0.27 eV to experiment for the training data level of theory [J. Phys. Chem. A 2025, 129, 36, 8419-8431] and the present model demonstrates an experimental evaluation error of 0.33 eV with good size transferability to larger organic molecules. An equivariant graph neural network is benchmarked against its rotationally invariant analogue and a model comprised of the smooth overlap of atomic positions descriptors and kernel ridge regression for training data efficiency and stability to non-
equilibrium geometries absent from the training data. All models show good training data efficiency and the graph based models have improved transferability to non-
equilibrium geometries. The use of chemically informed, graph-normalized node features reduces the graph neural network's dependence on message passing depth. A case study on the 45 atom avobenzone tautomers demonstrates the model's ability for instant and precise analysis of complex molecules. The software and data are provided by the open-source AugerNet package at https://doi.org/10.5281/zenodo.19689244.
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All authors Adam E. A. Fouda, Joshua Zhou, Rodrigo Ferreira, Patrick Phillips, Valay Agarawal, Bhavnesh Jangid, Jacob J. Wardzala, Rui Ding, Junhong Chen, Nicole Tebaldi, Phay J. Ho, Laura Gagliardi, Linda Young
Geophysics
Adaptive Self-Supervised Surface-Related Multiple Suppression
Huan Song, Shijun Cheng, Huanhuan Tang et al. (+2 more)
Effective suppression of surface-related multiples is essential to prevent imaging artifacts and erroneous structural interpretations. While conventional approaches rely on accurate priors or subsurface model knowledge, and supervised learning methods require labeled data that are impractical to obtain for real seismic data. To overcome these limitations, a recently proposed self-supervised learning (SSL) framework integrates multi-dimensional convolution (MDC) for multiple generation with a two-stage training strategy, eliminating the need for both prior knowledge and labeled data. However, their approach requires manual selection of a scaling factor to match the amplitudes between the MDC-generated multiples and the true multiples, thus introducing subjectivity and limiting its practical applicability. In this study, we propose an adaptive SSL method that treats the scaling factor as a learnable parameter, jointly optimized with the network weights in a unified single-stage training pipeline. This dynamic scaling implicitly introduces amplitude diversity into the training data, acting as an implicit regularizer that improves the network's robustness to amplitude variations of surface-related multiples. We further design a composite loss function with homoscedastic uncertainty-based adaptive weighting, which automatically balances the contributions of multiple loss terms without manual tuning. Synthetic and field data examples demonstrate that our method robustly and effectively suppresses surface-related multiples while preserving primary reflections, with migration results confirming improved subsurface imaging quality.
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All authors Huan Song, Shijun Cheng, Huanhuan Tang, Wei Ouyang, Weijian Mao
Accelerator Physics
Longitudinal beam instability driven by coherent radiation in an SSMB laser modulator
Yingjie Dai, Zhuoyuan Liu, Tong Li et al. (+2 more)
Storage ring-based steady-state microbunching (SSMB) is a promising approach for generating high-average-power coherent radiation, while the instabilities driven by coherent undulator radiation in the
laser modulator (LM) is important for the ring performance. In this paper we investigate the longitudinal single-bunch multi-turn LM instability using cavity mode decomposition techniques. The
evolution of the wakefield in the longitudinal beam dynamics equations are derived, and the instability growth rates are analyzed. Numerical simulations show excellent agreement with the theoretical model, validating the mode decomposition approach. These findings provide critical insights into the design and operation of SSMB storage rings, suggesting effective mitigation strategies to suppress the instability and enhance the overall performance.
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All authors Yingjie Dai, Zhuoyuan Liu, Tong Li, Xiujie Deng, Lixin Yan
Instrumentation and Detectors
Gaseous forms of $^{76}$Ge, $^{82}$Se, $^{96}$Zr, $^{100}$Mo, $^{124}$Sn, and $^{130}$Te: new avenues to future $0\nu\beta\beta$ time projection chambers
Aneesha Avasthi, Benjamin Monreal and Ivana Moya
Searches for neutrinoless double beta decay are growing larger, with tonne-scale targets in several nuclides still far from exhausting the discovery space. What's beyond ton scale? Time projection chambers (TPCs) are one option for building large (100~T or kiloton-scale) instruments, but filling them with the familiar $^{136}$Xe for a $0\nu\beta\beta$ search is a problem: xenon is a scarce element whose atmospheric-extraction supply chain is small and hard to grow. If future $0\nu\beta\beta$ searches wish to exploit TPCs' known hardware scalability, we need to fill them with non-xenon target materials. Of particular value would be a TPC that can drift electrons, rather than ions, letting us use mature readout schemes which require gas gain. In this paper, we identify a set of previously-unappreciated, affordable gases which are likely to be electropositive, allowing electron drift and gain in gas-phase TPCs sensitive to $0\nu\beta\beta$ with the help of track-
topology background rejection. We identify candidate $^{76}$Ge, $^{82}$Se, $^{96}$Zr, $^{100}$Mo, $^{124}$Sn, and $^{130}$Te compounds suitable for gas-phase electron-drift TPCs; some may be suitable for
liquid-phase TPCs as well. Using a figure-of-merit that emphasizes the need for track
topology for background rejection, we argue that 100~T and kiloton-scale gas TPCs are realistic without unprecedented underground infrastructure.
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Optics
Phase-Transition-Driven Hyperbolic Optical Response and Directional Polaritons in Epitaxial VO2 Thin Films
Maria Chiara Paolozzi, Annalisa D Arco, Ilaria Martinelli et al. (+10 more)
Optical anisotropy in crystalline solids enables direction-dependent light-matter interactions and underpins a variety of advanced photonic functionalities. In this context, Vanadium dioxide (VO2) represents a prototypical material that undergoes a reversible MIT near 67{\deg}C, accompanied by pronounced electronic, structural, and optical modifications. The MIT not only dramatically modifies the VO2 electrical conductivity but also reshapes its anisotropic optical response, making VO2 an exceptional platform for dynamically tunable photonic and optoelectronic devices. In this work, we investigate how the intrinsic crystalline anisotropy of VO2 induces a
hyperbolic optical behavior in the metallic rutile phase. We study two epitaxial VO2 thin films of different thicknesses grown on (110) oriented MgF2 substrates. Broadband polarized spectroscopic measurements, spanning the infrared to UV spectral range, are employed to independently investigate the optical response in both the monoclinic and rutile phases. From these measurements, we extract the optical conductivity and the dielectric function, revealing a pronounced anisotropy in the rutile metallic phase, with an enhanced free-carrier response along the rutile c axis. Our data show that, within a narrow near-infrared spectral window, the real parts of the dielectric tensor components along the two principal axes acquire opposite signs, indicating the emergence of a
hyperbolic type-II dispersion. The
hyperbolic response is quantitatively evaluated through the quality factor and the degree of dielectric anisotropy, enabling a systematic assessment of VO2 as a thermally switchable,
hyperbolic optical medium. These findings expand the understanding of anisotropy-driven optical phenomena in phase-change materials and highlight VO2 thin films as a promising platform for tunable and reconfigurable photonic applications.
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All authors Maria Chiara Paolozzi, Annalisa D Arco, Ilaria Martinelli, Lorenzo Mosesso, Jacopo Sera, Alessandro D Elia, Augusto Marcelli, Yingxue Chen, Chongwen Zou, Maria Cristina Larciprete, Marco Centini, Stefano Lupi, Salvatore Macis
Applied Physics · Cross-listed from eess.SP
Optimizing Dynamic Metasurface Antenna Configurations for Direction-of-Arrival and Polarization Estimation Using an Experimentally Calibrated Multiport-Network Model
Jean Tapie and Philipp del Hougne
Sensing the direction of arrival and polarization of impinging signals is a key prerequisite for beamforming and interference mitigation in modern wireless communication systems. Dynamic metasurface
antennas (DMAs) can multiplex direction- and polarization-dependent field information onto a single detector by sequentially switching between programmable configurations. This makes DMAs attractive for joint direction-of-arrival and polarization (DoA-P) estimation with a single
radio-frequency chain. Experimental demonstrations have so far relied on random pre-measured configuration sequences because optimizing the configurations requires an accurate forward model of the fabricated DMA. Here, we use an experimentally calibrated model based on multiport-network theory (MNT) to optimize DMA configuration sequences for DoA-P estimation. Our experimentally calibrated MNT model predicts the dual-polarized far-field response of our 96-element DMA for arbitrary admissible configurations, enabling model-based optimization without additional radiation-pattern measurements. We optimize sequences using effective-rank-based surrogate objectives and compare them with random sequences as a function of the sequence length and the noise level. The optimized sequences yield the largest gains in the intermediate-SNR and intermediate-sequence-length regime, where the inverse problem is neither noise-limited nor already solved by random diversity. We also tackle a dual-source scenario involving a jammer and a desired transmitter. Our results illustrate some of the potential in the context of jamming-resilient communications that is unlocked by experimentally calibrated MNT models for fabricated DMAs.
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Geophysics
Meta-learning-enhanced implicit full waveform inversion
Zefeng Wang, Shijun Cheng, Weijian Mao et al. (+2 more)
Implicit full waveform inversion (IFWI) introduces implicit neural representations to parameterize the subsurface velocity model as a continuous function of spatial coordinates, which alleviates the dependence on the initial model and improves inversion flexibility. However, IFWI still requires a large number of iterative updates for each new exploration area, leading to slow convergence, high computational cost, and a lack of mechanisms to share prior knowledge across different geological settings, thereby limiting its efficiency and generalization capability. To further accelerate convergence and enhance cross-area generalization, we propose a meta-learning-based implicit full waveform inversion method, referred to as Meta-learning-enhanced implicit full waveform inversion (Meta-IFWI). In this framework, the subsurface velocity model is represented using an implicit neural network with periodic activation functions (SIREN), while a meta-learning strategy is employed to pretrain a single network on multiple velocity inversion tasks. Through this process, the network learns shared inversion priors and rapid adaptation strategies across different geological scenarios. For a new inversion task, the pretrained Meta-IFWI model can be efficiently adapted to the observed seismic data with only a few gradient updates, significantly reducing the number of iterations required for inversion. Numerical experiments conducted on in-distribution models, including layered synthetic models and the Overthrust model, as well as out-of-distribution complex models such as Marmousi 2, demonstrate that, compared with conventional IFWI, the proposed Meta-IFWI achieves improved inversion accuracy while substantially accelerating convergence and reducing computational cost. Moreover, Meta-IFWI exhibits enhanced robustness and stronger cross-area generalization capability.
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All authors Zefeng Wang, Shijun Cheng, Weijian Mao, Wei Ouyang, Huanhuan Tang
Geophysics
Designing Solutions to Geophysical Inverse Problems by Changing Variables
Xuebin Zhao, Andrew Curtis and Klaus Mosegaard
Geoscientists often solve inverse problems to estimate values of parameters of interest given relevant data sets.
Bayesian inference solves these problems by combining probability distributions that describe uncertainties in both observations and unknown parameters, and we require that the solution provides unbiased uncertainty estimates in order to inform risk-based decisions. It has been known for over a century that employing different, but equivalent parametrisations of the same information can yield conditional probabilities that are mathematically inconsistent, a property referred to as the BK-inconsistency. Recently this inconsistency was shown to invalidate the solutions to physical problems found using several well-established methods of
Bayesian inference. In this study, we explore the extent to which this inconsistency affects solutions to common geophysical problems. We demonstrate that changes in parametrisations result in inconsistent conditional probability densities, even though they represent exactly the same information. We show that this can affect
Bayesian posterior solutions dramatically across various geoscientific problems using real and synthetic data. Given that deterministic inversion is often equivalent to finding the maximum a posteriori solution to specific
Bayesian problems (the mathematical equations to be solved are identical), the BK-inconsistency also results in inconsistent solutions to deterministic inverse problems. Indeed, we show that solutions can potentially be designed, simply by changing the parametrisation. This study highlights that a careful rethinking of
Bayesian inference and deterministic inversion may be required in physical problems: the effects that we demonstrate are likely to affect past and present inverse problem solutions in a variety of different fields of application.
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Biological Physics · Cross-listed from q-bio.TO
Theory of adhesion-driven self-organisation in growing tissues
Carles Falcó, Samuel W. S. Johnson, Mohit P. Dalwadi et al. (+1 more)
Cell invasion and spatial pattern formation are two distinct manifestations of cellular self-organisation in development, regeneration, and disease. Here, we develop and analyse a unified theoretical framework that links these two seemingly different behaviours within a single mechanistic model for adhesion-mediated self-organisation in growing
cell populations. Using a multiscale analysis, we show that the balance between
cell-
cell adhesion, self-
diffusion, and proliferation controls the emergence of distinct collective dynamics. We find that for weak adhesion, tissues invade through stable monotone fronts. As adhesion increases, invasion slows, fronts become unstable, leading to aggregates and spatial patterns emerging behind the advancing edge. In two spatial dimensions, these instabilities generate fingering morphologies reminiscent of dysregulated invasion in cancer. Crucially, we show that density-dependent regulation of adhesion suppresses these instabilities and restores cohesive tissue expansion. Together, our results identify adhesion strength and its regulation as key determinants of whether tissues invade cohesively or fragment into patterns, and provide a unified framework for understanding collective migration, morphogenesis, and dysregulated growth.
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All authors Carles Falcó, Samuel W. S. Johnson, Mohit P. Dalwadi, Philip K. Maini
Optics
Time-to-space ghost imaging with classical light
Nikita Solonovich, Chaoliang Ding, Polina P. Kuzhir et al. (+3 more)
Ghost imaging uses two light beams correlated in the transverse position, time, or frequency to create an image of a spatial, temporal, or spectral object. We propose a scheme of time-to-space ghost imaging for creating a spatial image of a temporal object, enabled by two spatio-temporally correlated light beams. Assuming a spatio-temporal Gaussian Schell model for the description of the source, we obtain analytical expressions for the point-spread function of the system and its temporal resolution. We show how the required source of partially coherent light can be realized by a combination of a diffraction grating and a spatial light modulator. As follows from our analysis, the temporal resolution of a time-to-space imaging system is determined by the duration of the
laser pulses used and the transverse coherence length imposed by the spatial light modulator, does not depend on the resolution time of the photodetectors, and can reach the sub-picosecond range.
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All authors Nikita Solonovich, Chaoliang Ding, Polina P. Kuzhir, Tero Setälä, Ari T. Friberg, Dmitri B. Horoshko
Fluid Dynamics · Cross-listed from math.NA
Data assimilation for slightly compressible flow
Aytekin Ç{\i}b{\i}k and Rui Fang
Continuous data assimilation (CDA) nudges observational data into governing equations to recover the underlying flow and improve predictions. Existing rigorous CDA analyses focus primarily on incompressible flows, yet no physical flow is perfectly incompressible. Approximating a slightly compressible flow with an incompressible model introduces non-negligible model errors. Data assimilation for compressible flows remains challenging due to strong nonlinearities and the presence of shocks. We design an
algorithm that addresses the limitations of velocity-only nudging for slightly compressible flow. This work incorporates both velocity and pressure data from the slightly compressible flow and nudges both quantities into the incompressible Navier–Stokes equations. Our analysis shows that the model error decays exponentially in the initial error, with an asymptotic residual of order $\mathcal{O}(H)$, where H denotes the observation resolution. The analysis also identifies a scaling for the pressure nudging parameter $\mu_1 = O(1/H^2)$ that ensures effective assimilation. We validate the theoretical results through a suite of numerical experiments: a convergence study confirming optimal rates, a modified Taylor–Green
vortex benchmark demonstrating synchronization of energy, enstrophy, and pressure, and an acoustic wave propagation test that isolates the role of pressure nudging and achieves a $97.9\%$ reduction in pressure error relative to velocity-only assimilation. Together, these results provide a foundation for discrete error estimates and realistic compressible applications.
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Atmospheric and Oceanic Physics
Dynamics of East Atlantic seed vortex populations in global km-scale models
Ben Maybee, Francesca Morris, Juliane Schwendike et al. (+5 more)
Africa is the primary source of cyclonic
vortices over the tropical Atlantic. Over both land and sea, these
vortices are entwined with deep convective activity, with the majority being African Easterly Wave troughs. Their convective interactions have downstream impacts, since the same
vortices provide the seed population for Atlantic basin tropical cyclone (TC) genesis. Understanding the dynamics of East Atlantic seed populations, particularly the processes that distinguish
vortices which undergo cyclogenesis, is crucial for understanding the formation of Atlantic hurricanes and model representations of their populations. Here we investigate these questions in three one-year, atmosphere-land global km-scale Met Office Unified Model simulations. We use objective tracking
algorithms to independently identify seed
vortices, easterly waves, TCs, and Mesoscale Convective Systems (MCSs), benchmarking against reanalysis and satellite observations. The simulations display equivalent continental and offshore seed
vortex frequencies, however the high-resolution, explicit convection simulation produces fewer, weaker hurricanes. Parameterised convection counterparts show stronger
vortex amplification of seeds crossing the West African coastline, and more hurricanes form offshore. In the explicit simulation, we identify a failure to maintain strong vertical mass flux profiles experienced by seeds as the primary cause of weak
vortex development, finding no low-level circulation development offshore about seed
vortices. Using MCS tracks, we show that systematic differences in convective organisation between the simulations can explain the differences in mass flux profiles, and thus
vortex evolution. Deficiencies in the explicit simulation stem primarily from underestimation of MCS anvils and stratiform rainfall, a long-standing systematic bias common amongst explicit convection models.
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All authors Ben Maybee, Francesca Morris, Juliane Schwendike, Ashar Aslam, Calum Scullion, Richard W. Jones, Dasha Shchepanovska, Kevin I Hodges
Biological Physics
Can we teach generative artificial intelligence the design language of engineered living materials?
Andrés Díaz Lantada, José A. Yáñez, William Solórzano-Requejo et al. (+1 more)
This study presents a versatile ontology and a useful codification scheme for describing all kinds of engineered living materials (ELMs). The different components of the ontology, namely: families according to the taxonomy for ELMs, industrial applications and synthesis or processing methods, are systematically organized, enumerated, classified, codified and explained. The methodic application of the ontology to a set of 100 relevant examples of ELMs helps to demonstrate its utility and adaptability to many different types of ELMs with a wide range of industrial applications and obtained through numerous synthesis and processing methods. This proves that the developed ontology and codification schemes, with the glossary provided to support its implementation and application, can serve as a comprehensive classification tool for the emergent field of ELMs. Furthermore, the usability of the ELMs ontology and codification by a generative artificial intelligence (AI) is explored and validated by different means, checking that both natural
language and the codification are understandable for describing ELMs, verifying that the generative AI adequately codifies examples of ELMs according to the ontology, and validating the synergic applicability of the ontology and codification with generative AI tools for illustrating novel ELMs and supporting their conceptual design. This study is expected to provide a universal
language to facilitate communication in the ELMs field and to foster the discovery of new ELMs and related innovations, hoping it may accelerate scientific and technological discoveries.
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All authors Andrés Díaz Lantada, José A. Yáñez, William Solórzano-Requejo, Monsur Islam
Physics and Society
A well-motivated model of pedestrian dynamics
Ezel Üsten, Anna Sieben, Mohcine Chraibi et al. (+1 more)
In pedestrian dynamics, the internal drive that propels individuals toward their goals is typically captured by a single, fixed parameter, the desired walking speed. This simplification overlooks that motivation fluctuates in response to changing spatial and social conditions within a crowd. This paper proposes a dynamic motivation model grounded in expectancy-value theory from psychology, in which each
agent's motivation evolves over time depending on proximity to the goal, relative position among other pedestrians, and individual goal importance. The resulting motivation modulates multiple movement parameters simultaneously, including walking speed, gap-closing behavior, and interpersonal spacing. The model is evaluated in simulated pre-bottleneck waiting scenarios using paired statistical comparisons across multiple random seeds and population sizes, and compared with trajectory data from the CROMA concert-entry bottleneck experiments under low- and high-motivation framings. Simulations show that the dynamic model produces structured heterogeneity in the crowd:
agents self-organize into differentiated positions near the bottleneck, with those closer to the front occupying less space, a pattern absent in the static baseline but clearly present in the experimental data. These findings suggest that motivation in crowds should be understood not as a uniform increase in urgency, but as a mechanism that reorganizes competitive positioning along spatial and social axes. Future work should extend the framework to open-door throughput scenarios, larger populations, and richer social interactions such as group cohesion and cooperative strategies.
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All authors Ezel Üsten, Anna Sieben, Mohcine Chraibi, Armin Seyfried
Fluid Dynamics
Constitutive Modelling of Korteweg Fluids Using Liu's Method
Zagorka Matić, Srboljub Simić and Peter Ván
The paper studies constitutive modelling of Korteweg fluids. Thermodynamic consistency, i.e. compatibility with
entropy balance law, is achieved using Liu's method of multipliers. Appropriate constitutive assumptions facilitated inclusion of the capillary effects in the specific
entropy. Korteweg stresses are derived from the
equilibrium conditions – vanishing of the
entropy production and its minimization in
equilibrium. Material parameter in Korteweg stresses is allowed to depend on temperature, which turns out to be consistent with kinetic-theory results and leads to cross-coupling of mechanical and thermal effects. The generalized Gibbs' relation, which inherits the capillary effects, is derived as consequence, which is a peculiar feature of the Liu's method.
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Biological Physics · Cross-listed from cond-mat.soft
Programmable Persistent Random Walks in Active Brownian Particles Govern Emergent Dynamics
Tarun Sunkesula Raghavendra, Yogesh Shelke, Stijn van der Ham et al. (+2 more)
Self-propelled particles serve as minimal models for emulating the dynamic self-organization of microorganisms, yet most synthetic systems remain limited to a single mode of motion, namely active Brownian particles (ABPs). Here, we present an experimental strategy to encode various persistent random walks in ABPs by combining light-modulated propulsion strength with
magnetic control of propulsion direction. Our system enables programmable Levy walks with tunable step-length distributions, run-and-tumble dynamics, self-avoiding random walks, and Gaussian walks, with on-demand switching between motion modes within a single experiment. In addition, particles are steered along complex trajectories such as Fibonacci spirals and nested polygons. Beyond single-particle behavior, we show that propulsion modes influence clustering dynamics by comparing ABPs with
chiral active particles undergoing circular motion. These results establish a versatile platform for investigating how encoded motion at the level of individual particles governs transport, search strategies, and emergent organization in active matter systems.
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All authors Tarun Sunkesula Raghavendra, Yogesh Shelke, Stijn van der Ham, Anpuj Nair S, Hanumantha Rao Vutukuri
Fluid Dynamics
Conditional diffusion denoising probabilistic model for super-resolution of atmospheric boundary layer large eddy simulation
Omar Sallam and Mirjam Fürth
Climate change necessitates rapid expansion of renewable energy, with wind energy offering a scalable and low-impact solution. However, accurate prediction of wind loads and power generation remains challenging due to uncertainties in wind shear and
turbulence stresses under atmospheric boundary layer (ABL) conditions. High-fidelity Large Eddy Simulations (LES) are typically used to reduce these uncertainties but are computationally expensive and impractical for large-scale or real-time applications. This work addresses this limitation using generative AI, specifically Conditional Denoising
Diffusion Probabilistic Models, to reconstruct high-resolution turbulent flow fields from coarse inputs. A high-fidelity
dataset is generated using a parallel high-order finite-difference solver across varying geostrophic wind speeds, surface roughness conditions aligned with IEC wind classes, and multiple grid resolutions. The
diffusion model is trained for super-resolution across different scale factors and evaluated under interpolation and extrapolation scenarios. Results show accurate recovery of fine-scale turbulent structures, Reynolds stresses, and statistical properties in interpolation cases, indicating strong physical consistency within the training domain. However, extrapolation to higher wind speeds leads to increased noise and overprediction of turbulent stresses, highlighting limitations in generalization. Overall, the study demonstrates that physics-informed generative models can significantly reduce computational cost while maintaining acceptable accuracy, enabling faster and more reliable turbulent inflow characterization for wind energy applications.
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Plasma Physics
Geodesic torsion of the magnetic field line and other predictors of coil non-planarity in QI stellarators
Andrea Pavone, Sehyun Kwak and Felix Warmer
Stellarator
magnetic fields are often realized with non-planar modular coils with their out-of-plane bending being one of the main engineering challenges. We ask whether the non planarity of the fialment can be predicted from the
equilibrium alone. We look at a
dataset of coils for stellarator equilibira and find that the geodesic torsion of the
magnetic field line is a strong and robust predictor in a wide range of coil feasibility requirements. We compare the performance of the geodesic torsion against other
equilibrium and surface geometry properties and find it to be a superior predictorv hardly replaceable by other quantities we considered in this
dataset. The ranking it induces also reproduces the ordering of four published configurations — precise QA, precise QI at nfp 2 and 3, and W7-X. Our conclusions suggest that geodesic torsion of the
magnetic field line may be considered as a cheap and robust candidate for a stage-one non-planarity proxy.
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Applied Physics
Unveiling the key role of Interfaces in the Design of finite-sized Metamaterial Structures
Svenja Hermann, Kévin Billon, Manuel Collet et al. (+1 more)
This paper investigates the influence of interfaces on the performance of finite-sized mechanical metamaterial structures for vibration damping applications. The metamaterial structures are designed in a sandwich configuration in which two homogeneous plates are connected to a metamaterial array. We test four different arrays that are obtained from the same metamaterial by differently cutting the metamaterial's unit
cell at the metamaterial/plate interface. When the four unit
cells are periodically repeated in space, they create the same infinitely large metamaterial with an identical mechanical response. In finite-sized structures, however, the different interfaces between the metamaterial array and the plates~–~called “material interfaces”~–~and between the metamaterial and the air~–~called “free interfaces”~–~strongly affect the specimen's vibration transmission characteristics. Using experimental measurements and validated finite-element (FE) models, we demonstrate a significant influence of the different types of interfaces on the global responses and local displacement fields of the structures. We also demonstrate the presence of a vibroacoustic coupling in the structures which also depends on the type of metamaterial/plate interfaces. Furthermore, we explore optimization strategies for enhancing the vibration damping performance of the metamaterial structures considering not only the metamaterial array but also the adjacent structures (the homogeneous plates). A comparison with
benchmark cases illustrates the optimization potential that the interfaces' design offers for the vibration damping capability of finite-sized metamaterial structures. We show that optimizing the type of targeted interfaces can shift a metamaterial's response from underperforming to significantly outperforming compared to classical solutions for noise and vibration damping in civil engineering.
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All authors Svenja Hermann, Kévin Billon, Manuel Collet, Angela Madeo
Physics and Society · Cross-listed from cs.CL
Swap distance minimization shapes the order of subject, object and verb in languages of the world
Jairo Rios-El-Yazidi and Ramon Ferrer-i-Cancho
Languages of the world vary concerning the order of subject (S), object (O) and verb (V). The most frequent dominant orders are SOV and SVO, and researchers have tailored models to this fact. However, there are still
languages whose dominant order does not conform to these expectations or even lack a dominant order. Here we approach word order variation through swap distance, namely the distance between orders in the permutohedron, a graph where the vertices are the six possible orders of S, O and V, and two orders are connected if they differ by a swap of adjacent constituents. Then word order variation becomes a problem of assigning probabilities to word orders so as to minimize the average swap distance. Although previous work suggests that swap distance minimization may influence synchronic and diachronic variation as well as word order acceptability, here we provide large-scale cross-linguistic evidence across families, macroareas and sources that word order variation within
languages is shaped by the principle of swap distance minimization, even when the dominant order is not SOV/SVO and even when a dominant order is lacking.
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Atomic Physics
Fast, powerful, low-noise optical pumping of an atomic vapor with semiconductor optical amplifiers
Diana Méndez-Avalos, Théo Louzada Meireles, Morgan W. Mitchell et al. (+1 more)
We use a $^{87}\text{Rb}$ atomic vapor, suitable for an optically-pumped magnetometer (OPM) in Earth-field conditions, to study the noise properties of three strategies for generating pulsed optical pumping. We compare a frequency-modulated (FM)
laser, amplitude modulation (AM) via an acousto-optic modulator (AOM), and amplitude modulation via a
semiconductor optical amplifier (SOA). Pumping the ensemble to operate as a Bell-Bloom OPM, and with an equal degree of
spin polarization, the three methods give nearly identical sensitivity, showing that the SOA, despite being an active device, can introduce negligible additional noise. Pumping the ensemble to operate as a free-induction-decay OPM, we observe longer unpumped coherence times with the SOA-AM method than with the FM method. Finally, using the higher power available from the SOA, we demonstrate an environment-limited sensitivity of $80\text{fT}/\sqrt{\text{Hz}}$ at $600\text{Hz}$ and 200fT$200\text{fT}/\sqrt{\text{Hz}}$ at $4\text{kHz}$, one to two orders of magnitude beyond what was achievable with the other pumping methods.
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All authors Diana Méndez-Avalos, Théo Louzada Meireles, Morgan W. Mitchell, Aleksandra Sierant
Applied Physics
Dynamic disentanglement of photoflexoelectricity and flexophotovoltage
Zhiguo Wang, Yuanyang Guo, Zhenggang Rao et al. (+4 more)
The coupling between light and strain gradients shows two kinds of effects: light enhanced flexoelectricity (photoflexoelectricity) and gradient enhanced photovoltage (flexophotovoltage). Although these effects originate from fundamentally different physical mechanisms (one is light enhanced electromechanical coupling, the other is a bulk photovoltaic effect), in this article we show that dynamic flexoelectric measurements of
semiconductors under illumination intrinsically contain contributions from both. To allow disentangling them, we have developed a general theoretical framework for their combined response in oscillating systems, demonstrating that the two contributions can be unambiguously separated through their distinct frequency and phase dependencies. We have validated these predictions using oscillating cantilever measurements on centrosymmetric perovskite
semiconductors (SrTiO3 and methylammonium lead bromide, MAPbBr3), obtaining selfconsistent values for the coefficients both effects which are in excellent agreement with independent static measurements. Our results establish a general protocol for disentangling both light strain gradient couplings using only oscillatory measurements, and clarify the interpretation of flexoelectric measurements under illumination.
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All authors Zhiguo Wang, Yuanyang Guo, Zhenggang Rao, Zhibin Wen, Massimiliano Stengel, Longlong Shu, Gustau Catalan