Chemical Physics · Cross-listed from quant-ph
Pauli propagation enables fast classical simulation of strongly correlated quantum systems
Chinmay Shrikhande, Arnab Bachhar, Aaron Rodriguez Jimenez et al. (+2 more)
Ground state energy estimation for strongly correlated
quantum systems remains a central challenge in computational physics and chemistry. While tensor network methods like DMRG provide efficient solutions for one-dimensional systems, higher-dimensional problems remain difficult. Here we present a variational double bracket flow (vDBF)
algorithm that leverages Pauli Propagation, a technique originally developed for classical simulation of
quantum circuits, to efficiently approximate ground state energies. By combining greedy operator selection with coefficient-based fluctuation truncation and energy-variance extrapolation, we obtain sub-1% relative errors for Heisenberg and Hubbard
lattices of up to 128
qubits. For a 10$\times$10 Heisenberg
lattice (100
qubits), vDBF obtains accurate results in approximately 2 minutes on a single CPU thread, compared to over 50 hours on 64 threads for DMRG. Accurate results are also obtained for systems that are difficult for
quantum Monte Carlo: on the maximally frustrated $8\times8$ $J_1$-$J_2$
lattice and the hole-doped $8\times8$ Hubbard model, the tightest thresholds extrapolate to within a few tenths of a percent of the best published variational estimates. We further test vDBF on the 84-
qubit $\pi$-valence active space of hexabenzocoronene, where the most reliable extrapolated correlation energies agree with DMRG to within 1%. These results demonstrate that classical simulation techniques developed in the context of
quantum advantage benchmarking can provide practical tools for many-body physics.
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All authors Chinmay Shrikhande, Arnab Bachhar, Aaron Rodriguez Jimenez, Edward F. Valeev, Nicholas J. Mayhall
Medical Physics
Fast 3D Ultrasound Localization Microscopy via Projection-based Processing Framework
Jingke Zhang, Jingyi Yin, U-Wai Lok et al. (+10 more)
Three-dimensional ultrasound localization microscopy (ULM) enables comprehensive visualization of the vasculature, thereby improving diagnostic reliability. Nevertheless, its clinical translation remains challenging, as the exponential growth in voxel count for full 3D reconstruction imposes heavy computational demands and extensive post-processing time. In this row-column array (RCA)-based 3D in vivo pig kidney ULM study, we reformulate each step of the full 3D ULM pipeline, including beamforming, clutter filtering, motion estimation, microbubble separation and localization into a series of computational-efficient 2D operations, substantially reducing the number of voxels to be processed while maintaining comparable accuracy. The proposed framework reconstructs each 0.75-s ensemble acquired at frame rate of 400 Hz, covering a 25*27.4*27.4 mm3 volume, in 0.52 s (70% of the acquisition time) on a single RTX A6000 Ada GPU, while maintaining ULM image quality comparable to conventional 3D processing. Quantitatively, it achieves a structural similarity index (SSIM) of 0.93 between density maps and a voxel-wise velocity agreement with slope of 0.93 and R2 = 0.88, closely matching conventional 3D results, and for the first time, demonstrating potential for real-time feedback during scanning, which could improve robustness, reduce operator dependence and accelerate clinical workflows.
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All authors Jingke Zhang, Jingyi Yin, U-Wai Lok, Lijie Huang, Ryan M. DeRuiter, Tao Wu, Kaipeng Ji, Yanzhe Zhao, James D. Krier, Xiang-yang Zhu, Lilach O. Lerman, Chengwu Huang, Shigao Chen
Chemical Physics
A Low Cost Relativistic Algebraic Diagrammatic Construction Method Based on Cholesky Decomposition and Frozen Natural Spinors for Electronic Ionization, Attachment and Excitation Energy Problem
Sudipta Chakraborty, Kamal Majee and Achintya Kumar Dutta
We present an efficient relativistic implementation of algebraic diagrammatic construction (ADC) theory up to third order for the treatment of electronic ionization potentials (IP), electron affinities (EA), and excitation energies (EE) in heavy-element systems using an exact two-component atomic mean-field (X2CAMF)
Hamiltonian. The approach combines Cholesky decomposition (CD) of two-electron integrals with frozen natural spinors (FNS) to significantly reduce the computational cost without compromising accuracy. To improve the description of excited states, we have implemented a state-specific frozen natural spinor (SS-FNS) framework and applied it to both electron affinity and excitation energy calculations. In addition to the standard relativistic ADC(3) method, we investigate a semi-empirically scaled variant in which the third-order contribution to the ADC secular
matrix is multiplied by a scaling factor (x), denoted as FNS/SS-FNS-[ADC(2)+(x)(3)]. This [ADC(2)+(x)(3)] approach shows systematic improvements over conventional ADC(3) in a variety of cases. Substantial computational savings are achieved through the use of FNS and SS-FNS schemes when compared to canonical calculations, resulting in significant speedups for ionization, attachment, and excitation energy computations. The current implementation accurately reproduces the canonical four-component ADC(3) results while significantly reducing computational cost. The efficiency and robustness of the method are demonstrated through applications to medium and large-sized molecular systems, including systems with 70 atoms and over 2600 basis functions.
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Optics · Cross-listed from quant-ph
Tunable WS$_2$ Micro-Dome Open Cavity Single Photon Source
Jens-Christian Drawer, Salvatore Cianci, Vita Solovyeva et al. (+13 more)
Versatile, tunable, and potentially scalable single-
photon sources are a key asset in emergent photonic
quantum technologies. In this work, a single-
photon source based on WS$_2$ micro-domes, created via hydrogen ion irradiation, is realized and integrated into an open, tunable optical microcavity. Single-
photon emission from the coupled emitter-cavity system is verified via the second-order correlation measurement, revealing a $g^{(2)}(\tau=0)$ value of 0.3. A detailed analysis of the spectrally selective, cavity enhanced emission features shows the impact of a pronounced acoustic
phonon emission sideband, which contributes specifically to the non-resonant emitter-cavity coupling in this system. The achieved level of cavity-emitter control highlights the potential of open-cavity systems to tailor the emission properties of atomically thin
quantum emitters, advancing their suitability for real-world
quantum technology applications.
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All authors Jens-Christian Drawer, Salvatore Cianci, Vita Solovyeva, Alexander Steinhoff, Christopher Gies, Falk Eilenberger, Kenji Watanabe, Takashi Taniguchi, Ivan Solovev, Giorgio Pettinari, Federico Tuzi, Elena Blundo, Marco Felici, Antonio Polimeni, Martin Esmann, Christian Schneider
Optics · Cross-listed from cond-mat.mes-hall
Phonon-tunable THz magnonic emission in multiferroic heterostructures
Sylvain Massabeau, Amr Abdelsamie, Florian Godel et al. (+13 more)
Collective excitations such as
magnons and polar
phonons provide natural access to the terahertz (THz) regime, but efficient generation and tunability remain elusive. Multiferroic BiFeO3 combines both orders at room temperature, offering a unique platform for narrowband THz emission. Here, we achieve efficient sub-bandgap optical rectification of coupled
phonon-polaritons near 2 THz in bare epitaxial thin films. In Pt/BiFeO3
bilayers, we demonstrate that coupling the electromagnon branch with ultrafast strain waves, optically generated in Pt layers with various thicknesses, can produce tunable and narrowband emission between 0.4-0.8 THz. These results uncover the intertwined role of
phonons,
magnons, and magneto-acoustic dynamics in
antiferromagnetic multiferroics, and establish these hybrid platforms as versatile engineered narrowband THz sources.
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All authors Sylvain Massabeau, Amr Abdelsamie, Florian Godel, Filip Miljevic, Noela Rezi, Pascale Gemeiner, Karim Bouzehouane, Thomas Buttiens, Sukhdeep Dhillon, Thomas Maroutian, Jean-Marie George, Henri Jaffres, Brahim Dkhil, Stephane Fusil, Vincent Garcia, Romain Lebrun
Computational Physics · Cross-listed from q-bio.PE
Approximate Bayesian Computation Made Easy: A Practical Guide to ABC-SMC for Dynamical Systems with pymc
Mario Castro
Mechanistic models are essential tools across ecology, epidemiology, and the life sciences, but parameter inference remains challenging when likelihood functions are intractable. Approximate
Bayesian Computation with Sequential Monte Carlo (ABC-SMC) offers a powerful likelihood-free alternative that requires only the ability to simulate data from mechanistic models. Despite its potential, many researchers remain hesitant to adopt these methods due to perceived complexity. This tutorial bridges that gap by providing a practical, example-driven introduction to ABC-SMC using Python. From predator-prey dynamics to hierarchical epidemic models, we illustrate by example how to implement, diagnose, and interpret ABC-SMC analyses. Each example builds intuition about when and why ABC-SMC works, how partial observability affects parameter identifiability, and how hierarchical structures naturally emerge in
Bayesian frameworks. All code leverages PyMC's modern probabilistic programming interface, ensuring reproducibility and easy adaptation to new problems. The code its fully available for download at \href{https://github.com/mariocastro73/ABCSMC_pymc_by_example}{mariocastro73/ABCSMC\_pymc\_by\_example}
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Applied Physics
Thermomechanical model of solar cells
Tom Markvart
The paper considers a model for the
solar cell as a mechanical open-cycle thermodynamic engine where the chemical potential is produced in an isochoric process corresponding to the thermalization of electron-hole pairs. Expansion of the beam under one-sun illumination and current generation are described as isothermal lost work. More generally, voltage produced in an open cycle process corresponds to availability, leading to a correction to the Shockley-Queisser detailed balance limit.
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Optics
High resolution 3D imaging of diamonds with multiphoton microscopy
Elana G. Alevy, Samuel D. Crossley, Lam T. Nguyen et al. (+2 more)
Diamonds offer unique benefits for optical technology development due to their optical, chemical, electrical, mechanical, and thermal properties. These attributes also contribute to their aesthetic appeal, high commercial value, and utility in geological studies. Thus, there is high demand for nondestructive techniques that enable rapid analysis of natural and synthetic diamonds as well as diamond-like simulants. Here, we demonstrate sub-micrometer, nondestructive, three-dimensional imaging and spectral analysis of diamonds using multiphoton microscopy (MPM). This approach stimulates nonlinear optical emissions to provide unique insights into the interior structure, fluorescent defects, and formational conditions of diamonds. As a result, MPM can be used to investigate gemstone quality, vacancy centers used in
quantum technologies, and the various inclusions and fluorescent emitters that may trace gemstone provenance and treatment history.
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All authors Elana G. Alevy, Samuel D. Crossley, Lam T. Nguyen, Vu D. Phai, Khanh Q. Kieu
Optics
Integrated emitters with CMOS-compatible tuning for large scale quantum SiN photonic circuits
Jasper De Witte, Atefeh Shadmani, Zhe Liu et al. (+10 more)
Next-generation scalable
quantum photonic technologies operating at the single
photon level rely on bringing together optimized
quantum building blocks with minimal optical coupling losses. Achieving this necessitates the heterogeneous integration of different elements onto a single interposer chip. Integrated
quantum emitters are key enablers for generating single
photons, inducing
quantum nonlinearities, and producing
entanglement. In this work, we demonstrate the scalable integration of mature InGaAs
quantum dots embedded in GaAs waveguides onto a low-loss SiN photonic platform, as evidenced by a high processing yield of 94.7% using a commercially available micro-transfer printing tool. These integrated emitters are embedded within a p-i-n
heterostructure that allows for noise suppression, near-blinking-free operation and wavelength tunability upon CMOS-level electrical biasing. With this, we pave the way for scalable integration of diverse
quantum photonic devices on a single chip.
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All authors Jasper De Witte, Atefeh Shadmani, Zhe Liu, Andraz Debevc, Tom Vandekerckhove, Marcus Albrechtsen, Rüdiger Schott, Arne Ludwig, Janez Krč, Gunther Roelkens, Leonardo Midolo, Bart Kuyken, Dries Van Thourhout
Applied Physics
Vibrational Transportation of Deformable Axisymmetric Particles
Marina E. Terzi, Vladislav V. Aleshin, Jules Ghesqui`ere et al. (+1 more)
A particle on a substrate supporting a surface acoustic wave can experience horizontal drift excited by the dry friction force. The effect is referred to as vibrational transportation, or as a surface acoustic wave motor. A traditional theory of vibrational transportation considers a particle as a material point moving on a rigid substrate. A more realistic representation is a contact model based on Cattaneo-Mindlin (also called Hertz-Mindlin) mechanics applicable to an axisymmetric deformable particle. A recent semi-analytical extension of the Cattaneo-Mindlin solution called the Method of
Memory Diagrams allows one to compute the hysteretic friction force for an arbitrary loading history in terms of contact displacements, and, subsequently, to numerically solve the equations of motion. Depending on the materials' and excitation parameters, the particle can stay in permanent contact with the substrate or experience multiple jumps. In the former case, the particle can slide along the surface, during each wave period, advancing and receding with different efficiencies, which finally results in a drift. The drift can occur both in the wave propagation direction and against it. In the regime of multiple jumps, directed horizontal motion is also possible. It is based on synchronization between the wave period and rebounding events. A rebound occurs once per period and consistently at the same phase. At the beginning of the process, the particle moves with an acceleration that decreases and finally disappears. Exactly the same type of motion against the wave has been observed in our preliminary experiments. We demonstrate that a point mass behaves differently: in a regime of permanent contact, negative and positive sliding are equilibrated, which produces no drift, whereas multiple rebounds of a point mass are always chaotic, at least for fully conservative collisions.
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All authors Marina E. Terzi, Vladislav V. Aleshin, Jules Ghesqui`ere, Vincent Tournat
Physics Education
Striving for Equity in Canadian Physics
Svetlana Barkanova, Gwen Grinyer, Juliette Mammei et al. (+2 more)
We discuss a number of new initiatives and events since 2020 which we hope will contribute to advancement of equity issues within the physics community in Canada. A recent analysis of high-school data shows that men are still over-represented in high-school physics courses, and the fraction has not changed in over a decade. Results from a national survey show that despite improvements over the years, the percentage of women and gender diverse physicists drops by around 35% between undergraduate students to those in a physics career. This decline is even more notable among Black, Indigenous, and people of colour (BIPOC) women and gender diverse physicists, whose representation drops by almost 60%. Several programs from the National Sciences and Engineering Research Council (NSERC) have been implemented in order to improve equity, diversity, and accessibility in STEM on a national level, most notably the Chairs for Women in Sciences and Engineering (CSWE) and Chairs for Inclusion in Sciences and Engineering (CISE) initiatives. It is crucial to maintain data collection and support existing as well as new EDI projects in future years as we work to build a more inclusive community of physicists in Canada.
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All authors Svetlana Barkanova, Gwen Grinyer, Juliette Mammei, Carolyn Sealfon, Anastasia Smolina
Physics and Society · Cross-listed from cs.DL
The Intertwined Rise of Collaboration Scale, Reference Diversity, and Breakthrough Potential in Modern Science: A 40-Year Cross-Disciplinary Study
Sarah J. James, Marcus A. Rodriguez and David P. Miller
Over the last four decades, the way knowledge is created in academia has transformed dramatically: research teams have grown larger, scholars draw from ever-wider pools of prior work, and the most influential discoveries increasingly emerge from complex collaborative efforts. Using a massive
dataset of over 15 million publications spanning 1970-2010 and covering six major domains (Humanities, Social Sciences, Agricultural Sciences, Medical and Health Sciences, Engineering and Technology, and Natural Sciences), this study tracks how three core features of scientific papers - authorship team size, the breadth and variety of cited sources, and eventual citation impact - have co-evolved over time. We uncover striking differences across disciplines. In every field, papers that build on a broader and more diverse knowledge base consistently attract more citations later on, lending large-scale empirical support to theories that view scientific breakthroughs as outcomes of novel recombination across distant ideas. Bigger teams, on average, generate work with greater ultimate influence, but the gains taper off after a certain scale; very large consortia seldom produce the absolute highest-impact papers. While the Humanities and Social Sciences remain anchored in solo or small-group authorship traditions, the Natural Sciences, Medicine, and Engineering have moved decisively toward big-team mega-science. These patterns illuminate the underlying production technology of discovery, reveal discipline-specific barriers to collaboration and idea integration, and offer evidence-based guidance for research funding agencies, universities, and policymakers seeking to organize scientific work for maximum breakthrough potential.
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Atomic Physics · Cross-listed from quant-ph
Quantum theory of electrically levitated nanoparticle-ion systems: Motional dynamics and sympathetic cooling
Saurabh Gupta, Bernard Faulend, Dmitry S. Bykov et al. (+2 more)
We develop the theory describing the
quantum coupled dynamics of the center-of-mass motion of a nanoparticle and an ensemble of ions co-trapped in a dual-frequency linear Paul trap. We first derive analytical expressions for the motional frequencies and classical trajectories of both nanoparticle and ions. We then derive a
quantum master equation for the ion-nanoparticle system and quantify the sympathetic cooling of the nanoparticle motion enabled by its Coulomb coupling to a continuously Doppler-cooled ion. We predict that motional cooling down to sub-kelvin temperatures is achievable in state-of-the-art experiments even in the absence of motional feedback and in the presence of micromotion. We then extend our analysis to an ensemble of $N$ ions, predicting a linear increase of the cooling rate as a function of $N$ and motional cooling of the nanoparticle down to tenths of millikelvin in current experimental platforms. Our work establishes the theoretical toolbox needed to explore the ion-assisted preparation of non-Gaussian motional states of levitated nanoparticles.
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All authors Saurabh Gupta, Bernard Faulend, Dmitry S. Bykov, Tracy E. Northup, Carlos Gonzalez-Ballestero
Computational Physics · Cross-listed from cond-mat.mtrl-sci
Unified interface dipole theory for Fermi level pinning effect at metal-semiconductor contacts
Ziying Xiang, Jun-Wei Luo and Shu-Shen Li
We present a unified bond dipole theory for metal-
semiconductor interfaces to explain the microscopic origin of interface dipoles and
Fermi level pinning (FLP) in terms of Harrison's bond-orbital model. By combining first-principles calculations with tight-binding analysis, we show that localized bonding between
semiconductor surface dangling bonds and metal orbitals is sufficient to generate a large interface dipole and induce strong FLP, even when only a single metal monolayer is present. Within this framework, metal-induced gap states (MIGS), dangling-bond-induced surface states (DBSS), and bonding states embedded in the valence band are all understood as different outcomes of the same underlying interface bonding mechanism, rather than as independent causes of FLP. We further establish that the key parameter governing FLP strength is the density of surface dangling bonds that can form new chemical bonds with the metal, which directly controls the magnitude of the bond-induced interface dipole. This picture naturally explains the weaker pinning observed in more ionic
semiconductors than in covalent ones and provides practical guidance for engineering metal-
semiconductor interfaces and tuning Schottky barrier heights.
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Medical Physics
A Dynamic Anti-Equinus Orthosis with Electromyography Sensor for Neuromuscular Rehabilitation
Manuel Terradillos Perea, Olga Alonso Gonzalez, Cristina Soguero Ruiz et al. (+1 more)
The equinus foot is a neuromuscular condition that affects ankle dorsiflexion, impairing gait and reducing quality of life. This study presents EquiSay, a dynamic anti-equinus orthosis equipped with an anterior elastic tension system and an electromyography (EMG) sensor to quantify muscle activation, particularly of the tibialis anterior. EquiSay provides dynamic support that improves foot posture and natural movement while enabling real-time neuromuscular monitoring. To address the limited availability of EMG data, the system incorporates a U-Net based model for generating synthetic EMG signals and a predictive framework for automatic calibration of minimum activation thresholds. Experimental results show improved dorsiflexion, increased patient satisfaction, and valuable clinical insights for rehabilitation planning. These findings highlight the potential of EquiSay as an assistive tool and as a platform for future AI-enhanced developments.
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All authors Manuel Terradillos Perea, Olga Alonso Gonzalez, Cristina Soguero Ruiz, David Gutierrez
Atmospheric and Oceanic Physics · Cross-listed from cs.AI
EWE: An Agentic Framework for Extreme Weather Analysis
Zhe Jiang, Jiong Wang, Xiaoyu Yue et al. (+5 more)
Extreme weather events pose escalating risks to global society, underscoring the urgent need to unravel their underlying physical mechanisms. Yet the prevailing expert-driven, labor-intensive diagnostic paradigm has created a critical analytical bottleneck, stalling scientific progress. While AI for Earth Science has achieved notable advances in prediction, the equally essential challenge of automated diagnostic
reasoning remains largely unexplored. We present the Extreme Weather Expert (EWE), the first intelligent
agent framework dedicated to this task. EWE emulates expert workflows through knowledge-guided planning, closed-loop
reasoning, and a domain-tailored meteorological toolkit. It autonomously produces and interprets
multimodal visualizations from raw meteorological data, enabling comprehensive diagnostic analyses. To catalyze progress, we introduce the first
benchmark for this emerging field, comprising a curated
dataset of 103 high-impact events and a novel step-wise evaluation metric. EWE marks a step toward automated scientific discovery and offers the potential to democratize expertise and intellectual resources, particularly for developing nations vulnerable to extreme weather.
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All authors Zhe Jiang, Jiong Wang, Xiaoyu Yue, Zijie Guo, Wenlong Zhang, Fenghua Ling, Wanli Ouyang, Lei Bai
Physics Education
Making sense of quantum teleportation: An intervention study on students' conceptions using a diagrammatic approach
Sebastian Kilde-Westberg, Andreas Johansson, Anna Pearson et al. (+1 more)
Quantum physics education at the upper-secondary level traditionally follows a historical approach, rarely extending beyond early 20th-century ideas, leaving students unprepared for comprehending modern
quantum technologies central to everyday life and many facets of modern industry. To address this gap, we investigated how upper-secondary students and pre-service teachers understand
quantum teleportation when taught with a simplified diagrammatic formalism based on the ZX-calculus, which represents
quantum processes as diagrams of wires and boxes. Through phenomenographic analysis of
video-recorded group work sessions, written responses to exercises, and a group interview, with a total of n=21 participants, we identified an outcome space consisting of four qualitatively different, hierarchically ordered categories of description encapsulating the different ways of experiencing
quantum teleportation. The categories revealed that a conceptual progression depends on how one understands the temporality in
quantum processes, the role of
entanglement in
quantum teleportation, the active nature of
quantum measurements, and interpretations of mathematical operations in the diagrams. Our findings demonstrate that while a simplified diagrammatic formalism for teaching
quantum physics provides an accessible entry point at the upper-secondary level, it does not automatically resolve fundamental conceptual challenges, and requires careful consideration in terms of developing teaching and learning sequences. Finally, these results provide educators with a deeper understanding of conceptual affordances and challenges for designing and improving instruction, whilst also highlighting the need for further exploring how students and teachers alike understand
quantum phenomena.
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All authors Sebastian Kilde-Westberg, Andreas Johansson, Anna Pearson, Jonas Enger
Fluid Dynamics
$\texttt{CRLS}$: Convolutional Regularized Least Squares Framework for Reduced Order Modeling of Transonic Flows
Muhammad Bilal and Ashwin Renganathan
We develop a convolutional regularized least squares ($\texttt{CRLS}$) framework for reduced-order modeling of transonic flows with shocks. Conventional proper orthogonal decomposition (POD) based reduced models are attractive because of their optimality and low online cost; however, but they perform poorly when snapshots contain parameter-dependent discontinuities, leading to smeared shocks, stair-stepping, or non-physical oscillations. In $\texttt{CRLS}$, we first map each full-order snapshot to a smoother representation by applying a one-dimensional Gaussian convolution with reflect padding along the flow field coordinates. The convolution hyperparameters (kernel width and support) are selected automatically by
Bayesian optimization on a held-out set of snapshots. POD bases are then extracted from the smoothed data, and the parametric dependence of the POD coefficients is learned via radial basis function interpolation. To recover sharp shock structures, we introduce an efficient deconvolution step formulated as a regularized least squares problem, where the regularization centers the reconstruction around a nearest-neighbor reference snapshot in parameter space. The resulting $\texttt{CRLS}$ surrogate is evaluated on inviscid transonic flow over the RAE2822 airfoil, modeled by the steady compressible Euler equations solved with SU2 over a Latin hypercube sample of Mach number and angle of attack. Compared with standard POD and smoothed-POD baselines, $\texttt{CRLS}$ yields markedly improved shock location and strength, lower surface-pressure and field-level errors, and a $42$% reduction in the number of POD modes required to capture a fixed fraction of snapshot energy. These results demonstrate that $\texttt{CRLS}$ provides an accurate, data-efficient, and largely automated route to shock-aware reduced order models for high-speed aerodynamic design.
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Optics
White light interferometry analysis for measuring thin film thickness down to few nanometers
Victor Ziapkoff, François Boulogne, Anniina Salonen et al. (+1 more)
We present a practical white-light interferometric method, supported by an open-source Python library optifik for automated spectrum-to-thickness deduction, enabling foam film measurements down to a few nanometers. We describe three typical spectral scenarii encountered in this method: spectra exhibiting numerous interference fringes, spectra with a moderate number of peaks, and spectra with only a few identifiable features, providing illustrative examples for each case. We also discuss the main limitations of the technique, including spectral range constraints, the necessity of knowing the refractive index, and the influence of spectral resolution and signal quality. Finally, we demonstrate the application of the method in a time-resolved study of a TTAB (tetradecyltrimethylammonium bromide) foam film undergoing elongation and thinning. This method can be adapted to measure any thin non-opaque layer.
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All authors Victor Ziapkoff, François Boulogne, Anniina Salonen, Emmanuelle Rio
Computational Physics
Differentiable Physics-Neural Models enable Learning of Non-Markovian Closures for Accelerated Coarse-Grained Physics Simulations
Tingkai Xue, Chin Chun Ooi, Zhengwei Ge et al. (+3 more)
Numerical simulations provide key insights into many physical, real-world problems. However, while these simulations are solved on a full 3D domain, most analysis only require a reduced set of metrics (e.g. plane-level concentrations). This work presents a hybrid physics-neural model that predicts scalar transport in a complex domain orders of magnitude faster than the 3D simulation (from hours to less than 1 min). This end-to-end differentiable framework jointly learns the physical model parameterization (i.e. orthotropic diffusivity) and a non-Markovian neural closure model to capture unresolved, 'coarse-grained' effects, thereby enabling stable, long time horizon rollouts. This proposed model is data-efficient (learning with 26 training data), and can be flexibly extended to an out-of-distribution scenario (with a moving source), achieving a Spearman correlation coefficient of 0.96 at the final simulation time. Overall results show that this differentiable physics-neural framework enables fast, accurate, and generalizable coarse-grained surrogates for physical phenomena.
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All authors Tingkai Xue, Chin Chun Ooi, Zhengwei Ge, Fong Yew Leong, Hongying Li, Chang Wei Kang
Biological Physics · Cross-listed from cond-mat.soft
Spatiotemporal Control of Charge +1 Topological Defects in Polar Active Matter
Birte C. Geerds, Abhinav Singh, Mathieu Dedenon et al. (+4 more)
Topological defects are a conspicuous feature of active
liquid crystals that have been associated with important morphogenetic transitions in organismal development. Robust development thus requires a tight control of the motion and placement of topological defects. In this manuscript, we study a mechanism to control +1 topological defects in an active polar fluid confined to a disk. If activity is localized in an annulus within the disk, the defect moves on a circular trajectory around the center of the disk. Using an ansatz for the polar field, we determine the dependence of the angular speed and the circle radius on the boundary orientation of the polar field and the active annulus. Using a proportional integral controller, we guide the defect along complex trajectories by changing the active annulus size and the boundary orientation.
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All authors Birte C. Geerds, Abhinav Singh, Mathieu Dedenon, Daniel J. G. Pearce, Frank Jülicher, Ivo F. Sbalzarini, Karsten Kruse
Chemical Physics
Diffusion-controlled reaction rate to an active site in a spherical cavity: Extension of Berg's theory
Sergey D. Traytak and Georgiy A. Babushkin
This study is due to various applications in physics, chemistry and especially in biology, where both bounded configuration domain and chemical anisotropy could play a great part. In fact we generalize the well-known Berg theory, which describes
diffusion-controlled reactions occurring within a spherically symmetric absorber-cavity system. The trapping probability and the reaction rate at which a small diffusing particle is captured by an axially symmetric one reactive patch absorber inside a spherical cavity were found semi-analytically and numerically by means of the dual series relations method. This approach leads to such incredibly fast convergence, that it may rightly be referred to as exact one. The results obtained can be used to test numerical programmes that describe
diffusion-controlled reactions in real physical systems for reactants with arbitrary anisotropic reactivity, which are located inside of various cavities as well as in the unbounded domains. Moreover, we managed to find a close connection between the dual series relations method and the generalized method of separation of variables.
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Fluid Dynamics
An octree-based sampling algorithm for analyzing big simulation data
Janis Geise, Sebastian Spinner, Richard Semaan et al. (+1 more)
As computational resources continue to increase, the storage and analysis of vast amounts of data will inevitably become a bottleneck in computational fluid dynamics (CFD) and related fields. Although compression
algorithms and efficient data formats can mitigate this issue, they are often insufficient when post-processing large amounts of volume data. Processing such data may require additional high-performance software and resources, or it may restrict the analysis to shorter time series or smaller regions of interest. The present work proposes an improved version of the existing Sparse Spatial Sampling
algorithm ($S^3$) to reduce the data from time-dependent flow simulations. The $S^3$
algorithm iteratively generates a time-invariant octree grid based on a user-defined metric, efficiently down-sampling the data while aiming to preserve as much of the metric as possible. Using the sampled grid allows for more efficient post-processing and enables
memory-intensive tasks, such as computing the modal decomposition of flow snapshots. The enhanced version of $S^3$ is tested and evaluated on the scale-resolving simulations of the flow past a tandem configuration of airfoils in the transonic regime, the incompressible turbulent flow past a circular cylinder, and the flow around an aircraft half-model at high Reynolds and Mach numbers. $S^3$ significantly reduces the number of mesh
cells by $35 \%$ to $95\%$ for all test cases while accurately preserving the dominant flow dynamics, enabling post-processing of CFD data on a local workstation rather than HPC resources for many cases.
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All authors Janis Geise, Sebastian Spinner, Richard Semaan, Andre Weiner
Chemical Physics
Design of a Plastic Inorganic Semiconductor GaPS4-Based Gas Sensor for Conformal Monitoring of Gas Lines
Qiao Wang
This paper reports the first gas sensor based on the plastic inorganic
semiconductor GaPS4, pioneering the application of plastic inorganic
semiconductors in the field of gas sensing. Unlike traditional rigid sensors, this device leverages the unique layered structure and ultra-wide bandgap of GaPS4 to achieve high sensitivity and selectivity in detecting NO2. The intrinsic plastic deformability of the material enables it to conform tightly to complex curved pipelines like an "electronic bandage," completely eliminating monitoring blind spots. Nanoindentation tests reveal that its extremely low hardness (0.20 GPa) confers exceptional flexibility while maintaining stable electrical characteristics even under bent states. The device exhibits a linear response to NO2 concentrations ranging from 1 to 10 ppm at room temperature. Although the limited defects in the single-
crystal material result in pA-level response currents, defect engineering offers a viable pathway for performance enhancement. This study breaks through the conventional boundaries of plastic inorganic
semiconductors confined to photoelectric and thermoelectric applications, opening new avenues for their use in gas sensing and advancing gas monitoring technology toward "conformal integration."
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Medical Physics
Stopping power monitoring during proton therapy by means of prompt gamma timing: first experimental results with a homogeneous phantom
Julius Werner, Francesco Pennazio, Piergiorgio Cerello et al. (+11 more)
Proton therapy's full potential is limited by uncertainties that prevent optimal dose distribution. Monitoring techniques can reduce these uncertainties and enable adaptive treatment planning. Spatiotemporal Emission Reconstruction from
Prompt-Gamma Timing (SER-PGT) is a promising method that provides insights into both particle range and stopping power, whose calculation would normally require knowledge about patient tissue properties that cannot be directly measured. We present the first experimental results using a 226.9 MeV synchrotron-
proton beam impinging on a homogeneous phantom at a sub-clinical intensity (2 - 4 x 10^7 pps). SER-PGT uses data from a multi-detector setup: a thin and segmented Low Gain Avalanche Diode for
proton detection and Lanthanum Bromide-based
crystals for
photon detection. The estimated stopping power profile showed an 8% +- 3% average error compared to NIST PSTAR values, and 2% +- 2% deviation relative to water at 100 MeV. Range assessment in a phantom with a 4 cm air-gap successfully identified the range shift with a 3 mm standard deviation. These results demonstrate the feasibility of using SER-PGT to recover both range and stopping power information through particle kinematics and PGT measurements.
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All authors Julius Werner, Francesco Pennazio, Piergiorgio Cerello, Elisa Fiorina, Simona Giordanengo, Felix Mas Milian, Alessio Mereghetti, Franco Mostardi, Marco Pullia, Sahar Ranjbar, Roberto Sacchi, Anna Vignati, Magdalena Rafecas, Veronica Ferrero
Optics
Design and Fabrication of Microfluidic Micro-Membranes by means of Two-Photon Polymerization
Vladimir Osipov, Alexander Dulebo and David J. Webb
We show that with the use of a short wavelength (520nm) femtosecond
laser source, Two-
Photon Polymerization (2PP) fabrication of micro-membrane designs with channel sizes down to 1 {\mu}m is possible, using commercial photopolymers OrmoComp and FemtoBond. This approach, involving the flexible design and direct manufacturing of micro-filters and micro-membranes, can be applied for novel microfluidic devices.
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Applied Physics · Cross-listed from cs.SD
Acoustic neural networks: Identifying design principles and exploring physical feasibility
Ivan Kalthoff, Marcel Rey and Raphael Wittkowski
Wave-guide-based physical systems provide a promising route toward energy-efficient analog computing beyond traditional electronics. Within this landscape, acoustic neural networks represent a promising approach for achieving low-power computation in environments where electronics are inefficient or limited, yet their systematic design has remained largely unexplored. Here we introduce a framework for designing and simulating acoustic neural networks, which perform computation through the propagation of sound waves. Using a digital-twin approach, we train conventional neural network architectures under physically motivated constraints including non-negative signals and weights, the absence of bias terms, and nonlinearities compatible with intensity-based, non-negative acoustic signals. Our work provides a general framework for acoustic neural networks that connects learnable network components directly to physically measurable acoustic properties, enabling the systematic design of realizable acoustic computing systems. We demonstrate that constrained recurrent and hierarchical architectures can perform accurate
speech classification, and we propose the SincHSRNN, a hybrid model that combines learnable acoustic bandpass filters with hierarchical temporal processing. The SincHSRNN achieves up to 95% accuracy on the AudioMNIST
dataset while remaining compatible with passive acoustic components. Beyond computational performance, the learned parameters correspond to measurable material and
geometric properties such as attenuation and transmission. Our results establish general design principles for physically realizable acoustic neural networks and outline a pathway toward low-power, wave-based neural computing.
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Optics
Low-dose Chemically Specific Bioimaging via Deep-UV Lensless Holographic Microscopy on a Standard Camera
Piotr Arcab, Mikolaj Rogalski, Karolina Niedziela et al. (+11 more)
Deep-ultraviolet (DUV) microscopy can provide label-free biochemical contrast by exploiting the intrinsic absorption of nucleic acids,
proteins and lipids, offering chemically specific morphological information that complements structural optical thickness contrast from phase-sensitive imaging. However, existing DUV microscopes typically rely on specialized optics and DUV-sensitive cameras, which restrict field of view, increase system complexity and cost, and often require high illumination doses that risk photodamage. Here, we report a low-dose deep-UV lensless
holographic microscopy platform that uses standard board-level CMOS sensors designed for visible light, eliminating all imaging optics and dedicated DUV detectors. Our system achieves large field-of-view (up to 116 mm2) DUV imaging with low illumination and label-free phase and chemically specific amplitude contrast. A specialized defocus/wavelength diverse pixel super-resolution reconstruction with total-variation regularization and robust autofocusing halves the effective sensor pixel pitch and yields down to 870 nm lateral resolution. We demonstrate chemically specific, label-free bioimaging on challenging specimens, including Saccharomyces cerevisiae, extracellular vesicles and unstained mouse liver tissue. In liver sections, imaging at {\lambda} = 330 nm reveals lipid- and retinoid-rich accumulations that co-localize with Oil Red O staining, enabling label-free identification of hepatic stellate (Ito)
cells. This combination of low-dose operation, chemically specific contrast and standard CMOS hardware establishes DUV lensless
holographic microscopy as a practical and scalable route to high-content submicron-resolution whole-slide preparation-free bioimaging without exogenous labels.
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All authors Piotr Arcab, Mikolaj Rogalski, Karolina Niedziela, Anna Chwastowicz, Emilia Wdowiak, Julia Dudek, Julianna Winnik, Pawel Matryba, Jolanta Mierzejewska, Malgorzata Lenarcik, Ewa Stepien, Piotr Zdankowski, Grzegorz Szewczyk, Maciej Trusiak
Applied Physics
Fly-by transit: A novel door-to-door shared mobility with minimal stops
Wenbo Fan and Weihua Gu
This paper introduces fly-by transit (FBT), a novel mobility system that employs modular mini-electric vehicles (mini-EVs) to provide door-to-door shared mobility with minimal stops. Unlike existing modular minibus concepts that rely on in-motion coupling and passenger transfers – technologies unlikely to mature soon – FBT lowers the technological barriers by building upon near-term feasible solutions. The system comprises two complementary mini-EV modules: low-cost trailers for on-demand feeder trips and high-performance leaders that guide coupled trailers in high-speed platoons along trunk lines. Trailers operate independently for detour-free feeder services, while stationary coupling at designated hubs enables platoons to achieve economies of scale (EoS). In-motion decoupling of the tail trailer allows stop-less operation without delaying the main convoy. As a proof of concept, a stylized corridor model is developed to analyze optimal FBT design. Results indicate that FBT can substantially reduce travel times relative to conventional buses and lower operating costs compared with e-hailing taxis. Numerical analyses further demonstrate that FBT achieves stronger EoS than both buses and taxis, yielding more than 13% savings in generalized system costs. By addressing key limitations of existing transit systems, this study establishes FBT as a practical and scalable pathway toward transformative urban mobility and outlines directions for future research.
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Optics
Monitoring and Regulation of Micro-Displacement Deviation in Few-Mode Beam Alignment through Mode Decomposition
Lin Xu, Li Pei, Jianshuai Wang et al. (+2 more)
Beam
alignment enables efficient, stable transmission and control of optical energy and information, which critically depend on precise monitoring and regulation of the three-dimensional (3D) relative positioning between fibers. This study introduces an approach to achieve more accurate 3D measurement of the spatial displacement between two optical fibers in a few-mode configuration, by integrating mode decomposition with a straightforward machine learning
algorithm. This method leverages inherent information from the optical field, enabling precise beam
alignment with a simple structure and minimal computational effort. In the 3D measurement experiment, the proposed method achieves a coefficient of determination of 0.99 for transverse offsets in the x- and y-directions, and 0.98 for air gap in the z-direction. The RMSE in x-direction, y-direction and z-direction is respectively 0.135 {\mu}m, 0.128 {\mu}m and 2.42 {\mu}m. The time for a single 3D displacement calculation is 4.037e-4 seconds. Furthermore, it facilitates single-step displacement regulation with a deviation tolerance within 0.15 {\mu}m and modal content regulation with an accuracy of 4.67%. These results establish a theoretical framework for addressing key challenges in optical path
alignment, crosstalk compensation, precision instrument manufacturing, and fiber optic sensing.
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All authors Lin Xu, Li Pei, Jianshuai Wang, Zhouyi Hu, Tigang Ning