Tootfinder

U-generating system dynamics with a product state using local operations
Daniel Dilley, Alvin Gonzales, Jeffrey Larson, Mark Byrd
https://arxiv.org/abs/2509.23083 https://

U-generating system dynamics with a product state using local operations
A quantum system may undergo undesired dynamics if it is already correlated with its environment before global unitary evolution takes place. To mitigate these effects, we apply local operations on the system of interest prior to the global evolution. This ensures that a specific dynamics-matching condition is satisfied - namely, that the system's dynamics can be U-generated starting from an initial product state. We review two strategies for achieving this: local measurements and local unitari…

Dark Signals in the Brain: Augment Brain Network Dynamics to the Complex-valued Field
Jiangnan Zhang, Chengyuan Qian, Wenlian Lu, Gustavo Deco, Weiyang Ding, Jianfeng Feng
https://arxiv.org/abs/2509.24715

Dark Signals in the Brain: Augment Brain Network Dynamics to the Complex-valued Field
Recordings of brain activity, such as functional MRI (fMRI), provide low-dimensional, indirect observations of neural dynamics evolving in high-dimensional, unobservable spaces. Embedding observed brain dynamics into a higher-dimensional representation may help reveal functional organization, but precisely how remains unclear. Hamiltonian mechanics suggests that, by introducing an additional dimension of conjugate momenta, the dynamical behaviour of a conservative system can be formulated in a …

Triangular neural synchronization patterns in visual impairment: A comprehensive case series exploring multi-node network dynamics and the Neural Triangle Index (NTI) https://www.

Triangular Neural Synchronization Patterns in Visual Impairment: A Comprehensive Case Series Exploring Multi-node Network Dynamics and the Neural Triangle Index
Background: Compensatory neural plasticity in visual impairment involves complex reorganization of multi-node networks that goes beyond conventional bilateral connection analysis. Triangular synchronization patterns, representing coordinated activity among three cortical sites simultaneously, remain unexplored despite their potential to reveal fundamental mechanisms underlying compensatory neuroplasticity. Objective: This study aimed to conduct a case series examining triangular neural synchro…

Learning Hybrid Dynamics via Convex Optimizations
Kaito Iwasaki, Sangli Teng, Anthony Bloch, Maani Ghaffari
https://arxiv.org/abs/2509.24157 https://arxiv.…

Learning Hybrid Dynamics via Convex Optimizations
This paper investigates the problem of identifying state-dependent switching systems, a class of hybrid dynamical systems that combine multiple linear or nonlinear modes. We propose two broad classes of switching systems: switching linear systems (SLSs) and switching polynomial systems (SPSs). We first formulate the joint estimation of the mode dynamics and switching rules as a mixed integer program. To solve its inherent scalability issue, we develop a hierarchy of convex relaxations and estab…

Small-Covariance Noise-to-State Stability of Stochastic Systems and Its Applications to Stochastic Gradient Dynamics
Leilei Cui, Zhong-Ping Jiang, Eduardo D. Sontag
https://arxiv.org/abs/2509.24277

Small-Covariance Noise-to-State Stability of Stochastic Systems and Its Applications to Stochastic Gradient Dynamics
This paper studies gradient dynamics subject to additive random noise, which may arise from sources such as stochastic gradient estimation, measurement noise, or stochastic sampling errors. To analyze the robustness of such stochastic gradient systems, the concept of small-covariance noise-to-state stability (NSS) is introduced, along with a Lyapunov-based characterization. Furthermore, the classical Polyak-Lojasiewicz (PL) condition on the objective function is generalized to the $\mathcal{K}$…

Synchronization transitions and spike dynamics in a higher-order Kuramoto model with L\'{e}vy noise
Dan Zhao, J\"urgen Kurths, Norbert Marwan, Yong Xu
https://arxiv.org/abs/2509.24554

Synchronization transitions and spike dynamics in a higher-order Kuramoto model with Lévy noise
Synchronization in various complex networks is significantly influenced by higher-order interactions combined with non-Gaussian stochastic perturbations, yet their mechanisms remain mainly unclear. In this paper, we systematically investigate the synchronization and spike dynamics in a higher-order Kuramoto model subjected to non-Gaussian Lévy noise. Using the mean order parameter, mean first-passage time, and basin stability, we identify clear boundaries distingusihing synchronization and inc…

Graph-Based Learning of Free Surface Dynamics in Generalized Newtonian Fluids using Smoothed Particle Hydrodynamics
Hyo-Jin Kim, Jaekwang Kim, Hyung-Jun Park
https://arxiv.org/abs/2509.24264

Graph-Based Learning of Free Surface Dynamics in Generalized Newtonian Fluids using Smoothed Particle Hydrodynamics
In this study, we propose a graph neural network (GNN) model for efficiently predicting the flow behavior of non-Newtonian fluids with free surface dynamics. The numerical analysis of non-Newtonian fluids presents significant challenges, as traditional algorithms designed for Newtonian fluids with constant viscosity often struggle to converge when applied to non-Newtonian cases, where rheological properties vary dynamically with flow conditions. Among these, power-law fluids exhibit viscosity t…

A multiscale analysis of mean-field transformers in the moderate interaction regime
Giuseppe Bruno, Federico Pasqualotto, Andrea Agazzi
https://arxiv.org/abs/2509.25040 https://…

A multiscale analysis of mean-field transformers in the moderate interaction regime
In this paper, we study the evolution of tokens through the depth of encoder-only transformer models at inference time by modeling them as a system of particles interacting in a mean-field way and studying the corresponding dynamics. More specifically, we consider this problem in the moderate interaction regime, where the number $N$ of tokens is large and the inverse temperature parameter $β$ of the model scales together with $N$. In this regime, the dynamics of the system displays a multiscal…

Quantum Dynamics with Time-Dependent Neural Quantum States
Alejandro Romero-Ros, Javier Rozal\'en Sarmiento, Arnau Rios
https://arxiv.org/abs/2509.24865 https://

Quantum Dynamics with Time-Dependent Neural Quantum States
We present proof-of-principle time-dependent neural quantum state (NQS) simulations to illustrate the ability of this approach to effectively capture key aspects of quantum dynamics in the continuum. NQS leverage the parameterization of the wave function with neural-network architectures. Here, we put NQS to the test by solving the quantum harmonic oscillator. We obtain the ground state and perform coherent state and breathing mode dynamics. Our results are benchmarked against analytical soluti…

Thermally Activated Plasticity in Single-Crystal Titanium: A Molecular Dynamics Study of Nanoscale Deformation
G. Markovic, F. J. Dominguez-Gutierrez
https://arxiv.org/abs/2509.23193

Thermally Activated Plasticity in Single-Crystal Titanium: A Molecular Dynamics Study of Nanoscale Deformation
Hexagonal close-packed (hcp) titanium exhibits a complex temperature-dependent mechanical response that is central to its use in structural applications. We employ large-scale molecular dynamics simulations to investigate the nanoindentation behavior of single-crystalline a-Ti along the [0001], [10-10], and [2-110] orientations at 10, 300, and 600 K. The simulations reveal how temperature modifies the onset of plasticity and the subsequent evolution of dislocation activity, including nucleation…

In the field of dynamical glaciology, this would be DeConto and Pollard, 2016.
I think it's dead wrong, but it's certainly led to some very interesting studies.and some real advanced in the otherwise moribund calving dynamics sub-field.
Would love to hear other suggestions from #climate (or adjacent) fields..
#science

Hierarchical Control Design for Space Robots with Application to In-Orbit Servicing Missions
Pietro Bruschi
https://arxiv.org/abs/2509.22955 https://arxiv.…

Hierarchical Control Design for Space Robots with Application to In-Orbit Servicing Missions
In-Orbit Servicing and Active Debris Removal require advanced robotic capabilities for capturing and detumbling uncooperative targets. This work presents a hierarchical control framework for autonomous robotic capture of tumbling objects in space. A simulation environment is developed, incorporating sloshing dynamics of the chaser, a rarely studied effect in space robotics. The proposed controller combines an inner Lyapunov-based robust control loop for multi-body dynamics with an outer loop ad…

Killer new self-titled EP by Sydney's BIG MODERN. Great guitar tone & lyrics, dynamics, it's tight, minimal sometimes.. there's some cool surprises on this one and I'm really digging it. And it's on Bandcamp AND Qobuz, which is cool - links below for both.
https://bigmodern.bandcamp.com/album/b

Evolutionary hypergame dynamics: Introspection reasoning and social learning
Feipeng Zhang, Te Wu, Guofeng Zhang, Long Wang
https://arxiv.org/abs/2509.24398 https://

Evolutionary hypergame dynamics: Introspection reasoning and social learning
In the realm of evolutionary game theory, standard frameworks typically presuppose that every player possesses comprehensive knowledge and unrestricted access to the entire strategy space. However, real-world human society inherently harbors diverse levels of knowledge, experience, and background among individuals. Hypergames incorporate this heterogeneity by permitting individuals to differ in their access to the full strategy set, reflecting cognitive or informational constraints and giving r…

Rolling with modular symmetry: quintessence and de Sitter in heterotic orbifolds
Hansel Gordillo-Ruiz, Miguel Hernandez-Segura, Ignacio Portillo-Castillo, Saul Ramos-Sanchez, Ivonne Zavala
https://arxiv.org/abs/2509.22781

Rolling with modular symmetry: quintessence and de Sitter in heterotic orbifolds
Modular invariance is a fundamental symmetry in string compactifications, constraining both the structure of the effective theory and the dynamics of moduli and matter fields. It has also gained renewed importance in the context of swampland conjectures and, independently, flavour physics. We investigate a modular-invariant scalar potential arising from heterotic orbifolds, where the flavour structure and moduli dynamics are jointly shaped by the underlying geometry. Focusing on a string-inspir…

Mathematical and numerical modeling of coupled oxygen dynamics and neuronal electrophysiology
Francesco Daniele, Caterina B. Leimer Saglio, Stefano Pagani, Paola F. Antonietti
https://arxiv.org/abs/2509.22863

Mathematical and numerical modeling of coupled oxygen dynamics and neuronal electrophysiology
Understanding how oxygen supply shapes neuronal excitability is crucial for explaining brain function in pathological scenarios, such as ischemia. This condition is caused by a reduced blood supply, leading to oxygen and other metabolites deprivation; this energy deficit impairs ionic pumps and causes cellular swelling. In this work, this phenomenon is modeled through a volumetric variation law that links cell swelling to local oxygen concentration and the percentage of blood flow reduction. Th…

Global dynamics of damped Euler systems with exterior potentials
Young-Pil Choi, Houzhi Tang, Weiyuan Zou
https://arxiv.org/abs/2509.23538 https://arxiv.or…

Global dynamics of damped Euler systems with exterior potentials
We study the three-dimensional isothermal Euler equations with linear damping and an exterior potential. For sufficiently large damping, we prove global well-posedness for arbitrarily large initial data by combining a parabolic comparison principle with scaled high-order energy estimates ensuring uniform density bounds. In the small-data regime with arbitrary damping, we establish global classical solutions and derive sharp algebraic decay rates via spectral analysis and frequency decomposition…

Polymer Bianchi-I with polymer matter
Aleena Zulfiqar, Syed Moeez Hassan
https://arxiv.org/abs/2509.24586 https://arxiv.org/pdf/2509.24586

Polymer Bianchi-I with polymer matter
We analyze the effective dynamics of a polymer quantized homogeneous and anisotropic Bianchi-I universe coupled to a polymer quantized scalar field. We use a pressureless dust field as an internal clock, and work with the gravitational Misner variables. We find that for a consistent polymer quantization of the anisotropies, the volume variable has to be polymerized first, and that a choice of different polymer scales for the two leads to substantially different dynamics. We derive an effective …

Collective transport efficiency of microswimmer swarms optimized by tactic run-tumble dynamics
Maggie Liu, Arnold J. T. M. Mathijssen
https://arxiv.org/abs/2509.25068 https://…

Collective transport efficiency of microswimmer swarms optimized by tactic run-tumble dynamics
The collective motion of microorganisms and microrobots can be used for particle delivery, especially when guided by external magnetic fields, phototaxis, or chemotaxis. This cargo transport is enhanced significantly by hydrodynamic entrainment, where the surrounding fluid and any dissolved molecules or suspended cargo particles are dragged along with a collectively moving swarm. However, it remains unclear how this directed entrainment is affected by stochastic run-tumble motion, and how such …

Sometimes I think people’s opposition (well justified!!) to war and the military-industrial complex makes them lose sight of the class dynamics in this situation:
Military service, National Guard and otherwise, is a pathway to steady pay, to health care, to something resembling the fabled middle class. The Trump crowd can treat the National Guard members as if they are disposable political props in large part because of class dynamics.

Multifractal features of multimodal cardiac signals: Nonlinear dynamics of exercise recovery
A. Maluckov, D. Stojanovic, M. Miletic, Lj. Hadzievski, J. Petrovic
https://arxiv.org/abs/2509.23317

Multifractal features of multimodal cardiac signals: Nonlinear dynamics of exercise recovery
We investigate the recovery dynamics of healthy cardiac activity after physical exertion using multimodal biosignals recorded with a polycardiograph. Multifractal features derived from the singularity spectrum capture the scale-invariant properties of cardiovascular regulation. Five supervised classification algorithms - Logistic Regression (LogReg), Suport Vector Machine with RBF kernel (SVM-RBF), k-Nearest Neighbors (kNN), Decision Tree (DT), and Random Forest (RF) - were evaluated to disting…

Magnetic-field dependent vortex dynamics and critical currents in superconducting microwires with regular large-area perforation by pinholes
Dong Zhu, Ilya Charaev, Konstantin Ilin, Andreas Schilling
https://arxiv.org/abs/2509.23404

Magnetic-field dependent vortex dynamics and critical currents in superconducting microwires with regular large-area perforation by pinholes
We report on results of simulations and experiments of vortex states in superconducting micro-wires with periodic rectangular pinhole structures. The simulations have been performed by means of numerically solving the time-dependent Ginzburg-Landau (TDGL) equations. With increasing bias current and for different values of the external magnetic field applied normal to the structure plane, we observe at first a vortex free Meissner state, followed by a resistive vortex-flow mixed state and a stat…

Momentum-resolved two-dimensional spectroscopy as a probe of nonlinear quantum field dynamics
Duilio De Santis, Alex G\'omez Salvador, Nataliia Bazhan, Sebastian Erne, Maximilian Pr\"ufer, Claudio Guarcello, Davide Valenti, J\"org Schmiedmayer, Eugene Demler
https://arxiv.org/abs/2509.25147

Momentum-resolved two-dimensional spectroscopy as a probe of nonlinear quantum field dynamics
Emergent collective excitations constitute a hallmark of interacting quantum many-body systems, yet in solid-state platforms their study has been largely limited by the constraints of linear-response probes and by finite momentum resolution. We propose to overcome these limitations by combining the spatial resolution of ultracold atomic systems with the nonlinear probing capabilities of two-dimensional spectroscopy (2DS). As a concrete illustration, we analyze momentum-resolved 2DS of the quant…

Dyadic Neural Dynamics: Extending Representation Learning to Social Neuroscience
Maria Glushanina, Jeffrey Huang, Michelle McCleod, Brendan Ames, Evie Malaia
https://arxiv.org/abs/2509.23479

Dyadic Neural Dynamics: Extending Representation Learning to Social Neuroscience
Social communication fundamentally involves at least two interacting brains, creating a unique modeling problem. We present the first application of Contrastive Embedding for Behavioral and Neural Analysis (CEBRA) to dyadic EEG hyperscanning data, extending modeling paradigms to interpersonal neural dynamics. Using structured social interactions between participants, we demonstrate that CEBRA can learn meaningful representations of joint neural activity that captures individual roles (speaker-l…

Nonrelativistic quantum dynamics in a twisted screw spacetime
Faizuddin Ahmed, Edilberto O. Silva
https://arxiv.org/abs/2509.24714 https://arxiv.org/pdf/25…

Nonrelativistic quantum dynamics in a twisted screw spacetime
We investigate the nonrelativistic quantum dynamics of a spinless particle in a screw-type spacetime endowed with two independent twist controls that interpolate between a pure screw dislocation and a homogeneous twist. From the induced spatial metric we build the covariant Schrödinger operator, separate variables to obtain a single radial eigenproblem, and include a uniform magnetic field and an Aharonov-Bohm (AB) flux by minimal coupling. Analytically, we identify a clean separation between …

Google DeepMind hires former Boston Dynamics CTO Aaron Saunders as VP of hardware engineering, as Demis Hassabis envisions Gemini to become a sort of robot OS (Will Knight/Wired)
https://www.wired.com/story/google-hires-cto-boston-dynamics-demis-hassabis-…

Google DeepMind Hires Former CTO of Boston Dynamics as the Company Pushes Deeper Into Robotics
DeepMind's chief says he envisions Gemini as an operating system for physical robots. The company has hired Aaron Saunders to help make that a reality.

wiki_link_dyn: Wikipedia link dynamics (2011)
Six networks of the evolving hyperlink structure among wikipedia articles, for simple English (en), German (de), Dutch (nl), Polish (pl), Italian (it), French (fr), taken in August 2011. Each edge is timestamped, and an edge (i,j) indicates that article i hyperlinks to j. The edge sign indicates the addition ( 1) or deletion (-1) of that link at the specified time.
This network has 100312 nodes and 1627472 edges.
Tags: Informati…

In-situ X-ray imaging of reduction-nitridation in ferric oxide under high Pressure
Yu Tao, Depu Liu, Chunyin Zhou, Xv Jia, Jingyi Liu, Xue Chang, Yangbin Wang, Li Lei
https://arxiv.org/abs/2509.24289

In-situ X-ray imaging of reduction-nitridation in ferric oxide under high Pressure
The investigation of high-pressure chemical reaction dynamics has long been constrained by the absence of effective in-situ characterization methods. Here we performed the state-of-the-art synchrotron radiation in-situ X-ray imaging based on large-volume press (LVP) technology to uncover the crucial information on the dynamics of the underlying phenomena of the formation of iron-based spherical product in the high-pressure solid-state metathesis reaction (HSM). We successfully give access to th…

Rethinking Portfolio Risk: Forecasting Volatility Through Cointegrated Asset Dynamics
Gabriele Casto
https://arxiv.org/abs/2509.23533 https://arxiv.org/pdf…

Rethinking Portfolio Risk: Forecasting Volatility Through Cointegrated Asset Dynamics
We introduce the Historical and Dynamic Volatility Ratios (HVR/DVR) and show that equity and index volatilities are cointegrated at intraday and daily horizons. This allows us to construct a VECM to forecast portfolio volatility by exploiting volatility cointegration. On S&P 500 data, HVR is generally stationary and cointegration with the index is frequent; the VECM implementation yields substantially lower mean absolute percentage error (MAPE) than covariance-based forecasts at short- to mediu…

Active Authentication via Korean Keystrokes Under Varying LLM Assistance and Cognitive Contexts
Dong Hyun Roh, Rajesh Kumar
https://arxiv.org/abs/2509.24807 https://

Active Authentication via Korean Keystrokes Under Varying LLM Assistance and Cognitive Contexts
Keystroke dynamics is a promising modality for active user authentication, but its effectiveness under varying LLM-assisted typing and cognitive conditions remains understudied. Using data from 50 users and cognitive labels from Bloom's Taxonomy, we evaluate keystroke-based authentication in Korean across three realistic typing scenarios: bona fide composition, LLM content paraphrasing, and transcription. Our pipeline incorporates continuity-aware segmentation, feature extraction, and classific…

A Spectral-Grassmann Wasserstein metric for operator representations of dynamical systems
Thibaut Germain, R\'emi Flamary, Vladimir R. Kostic, Karim Lounici
https://arxiv.org/abs/2509.24920

A Spectral-Grassmann Wasserstein metric for operator representations of dynamical systems
The geometry of dynamical systems estimated from trajectory data is a major challenge for machine learning applications. Koopman and transfer operators provide a linear representation of nonlinear dynamics through their spectral decomposition, offering a natural framework for comparison. We propose a novel approach representing each system as a distribution of its joint operator eigenvalues and spectral projectors and defining a metric between systems leveraging optimal transport. The proposed …

Designing AI-Infused Interactive Systems for Online Communities: A Systematic Literature Review
Yuanhao Zhang, Xiaoyu Wang, Jiaxiong Hu, Ziqi Pan, Zhenhui Peng, Xiaojuan Ma
https://arxiv.org/abs/2509.23309

Designing AI-Infused Interactive Systems for Online Communities: A Systematic Literature Review
AI-infused systems have demonstrated remarkable capabilities in addressing diverse human needs within online communities. Their widespread adoption has shaped user experiences and community dynamics at scale. However, designing such systems requires a clear understanding of user needs, careful design decisions, and robust evaluation. While research on AI-infused systems for online communities has flourished in recent years, a comprehensive synthesis of this space remains absent. In this work, w…

The role of viral dynamics and infectivity in models of oncolytic virotherapy for tumours with different motility
David Morselli, Federico Frascoli, Marcello Edoardo Delitala
https://arxiv.org/abs/2509.24522

The role of viral dynamics and infectivity in models of oncolytic virotherapy for tumours with different motility
The use of ad-hoc engineered viruses in the fight against tumours is one of the greatest ideas in cancer therapeutics within the last three decades. Together with other strategies such as immunotherapies, nanoparticles and adjunct therapies, the use of viral vectors in clinical trials and in the clinics has been and is still widely studied and pursued. The ability of those vectors to infiltrate and infect tumours represents one of the key attributes that regulates the success of such a strategy…

Exponentially slow Mixing arising from Entropic Repulsion in $p$-SOS model
Seokun Choi
https://arxiv.org/abs/2509.23120 https://arxiv.org/pdf/2509.23120

Exponentially slow Mixing arising from Entropic Repulsion in $p$-SOS model
We investigate the Glauber dynamics of the generalized (2+1)-dimensional $p$-SOS model under a hard floor constraint. This setting induces entropic repulsion: the integer-valued interface height is forced to remain above the wall and consequently rises to a typical height $H(p,L)$ that depends on both the parameter $p$ and the system size $L$. In the classical SOS model ($p=1$), \cite{caputo2016scaling, caputo2014dynamics} derived an exponential lower bound for the mixing time, demonstrating th…

A Hybrid Optimal Velocity Drag Model for Traffic Flow Dynamics
Nizhum Rahman, Trachette L. Jackson
https://arxiv.org/abs/2509.22671 https://arxiv.org/pdf/2…

A Hybrid Optimal Velocity Drag Model for Traffic Flow Dynamics
We develop a Hybrid Optimal Velocity-Drag (OVD) model that combines the behavioral structure of the classical Optimal Velocity Model (OVM) with a drag-based saturation law motivated by Newtonian mechanics. The model retains the OVM principle that desired speed depends on headway, but replaces the linear relaxation law with a formulation that enforces bounded accelerations and smooth convergence to equilibrium. After reviewing the OVM framework and its stability properties, we introduce the OVD …

Crosslisted article(s) found for physics.flu-dyn. https://arxiv.org/list/physics.flu-dyn/new
[1/1]:
- A Hybrid Optimal Velocity Drag Model for Traffic Flow Dynamics
Nizhum Rahman, Trachette L. Jackson

Supervised Machine Learning for Predicting Open Quantum System Dynamics and Detecting Non-Markovian Memory Effects
Ali Abu-Nada, Subhashish Banerjee
https://arxiv.org/abs/2509.22758

Supervised Machine Learning for Predicting Open Quantum System Dynamics and Detecting Non-Markovian Memory Effects
We present a \emph{novel} and scalable supervised machine learning framework to predict open-quantum system dynamics and detect non-Markovian memory using only local ancilla measurements. A system qubit is coherently coupled to an ancilla via a symmetric XY Hamiltonian; the ancilla interacts with a noisy environment and is the only qubit we measure. A feedforward neural network, trained on short sliding windows of supplementary data from the past, forecasts the observable system $\langle Z_{(S)…

Exploring parameter dependence of heavy-flavor dynamics in small collision systems
Grace Pang
https://arxiv.org/abs/2509.23081 https://arxiv.org/pdf/2509.2…

Exploring parameter dependence of heavy-flavor dynamics in small collision systems
Observations from high-multiplicity proton-lead ($p$-Pb) collisions indicate that small systems may exhibit collective behavior in both heavy and light hadrons. This work investigates the roles of initial- and final-state interactions in shaping the nuclear modification factor and elliptic flow of $D$ mesons measured in $p$-Pb collisions. Initial-state effects, including the Cronin and shadowing effects, are considered in the heavy-quark initial conditions, while final-state interactions are si…

Field-theoretical form of the molecular dynamics method in condensed matter physics: relativistic quantum statistical thermodynamics
A. Yu. Zakharov
https://arxiv.org/abs/2509.24082

Field-theoretical form of the molecular dynamics method in condensed matter physics: relativistic quantum statistical thermodynamics
The principles of microscopic foundation of thermodynamics within the framework of relativistic quantum theory have been formulated

Witnessing Magnetic Reconnection in Tangled Superpenumbral Fibrils Around a Sunspot
Hechao Chen, Hui Tian, Eric R. Priest, Christopher B. Prior, Chun Xia, Anthony R. Yeates, Xiaoli Yan, Yadan Duan, Zhenyong Hou, Zhenghua Huang, Oliver E. K. Rice
https://arxiv.org/abs/2509.23636

Witnessing Magnetic Reconnection in Tangled Superpenumbral Fibrils Around a Sunspot
Three-dimensional magnetic reconnection is a fundamental plasma process crucial for heating the solar corona and generating the solar wind, but resolving and characterizing it on the Sun remains challenging. Using high-quality data from the Chinese New Vacuum Solar Telescope, the Solar Dynamics Observatory, and the Interface Region Imaging Spectrograph, this work presents highly suggestive direct imaging evidence of magnetic reconnection during the untangling of braided magnetic structures abov…

BladderFormer: A Streaming Transformer for Real-Time Urological State Monitoring
Chengwei Zhou, Steve Majerus, Gourav Datta
https://arxiv.org/abs/2509.24178 https://

BladderFormer: A Streaming Transformer for Real-Time Urological State Monitoring
Bladder pressure monitoring systems are increasingly vital in diagnosing and managing urinary tract dysfunction. Existing solutions rely heavily on hand-crafted features and shallow classifiers, limiting their adaptability to complex signal dynamics. We propose a one-layer streaming transformer model for real-time classification of bladder pressure states, operating on wavelet-transformed representations of raw time-series data. Our model incorporates temporal multi-head self-attention and stat…

Griffiths phase emerging from strong mutualistic disorder in high-dimensional interacting systems
Tommaso Jack Leonardi, Amos Maritan, Sandro Azaele
https://arxiv.org/abs/2509.24692

Griffiths phase emerging from strong mutualistic disorder in high-dimensional interacting systems
The majority of analysis of interacting systems is done for weak and well-balanced interactions, when in fact topology and rare event factors often result in strong and sign-biased interactions when considering real systems. We analyse the impact of strong mutualistic interactions in a uniformly weighted Erdős-Rényi network under generalised Lotka-Volterra dynamics. In difference to the typical case we show the interaction topology and system dynamics combine to produce power law abundance di…

On the rotating nonlinear Klein-Gordon equation with multiscale effects: structure-preserving methods and applications to vortex dynamics
Meng Li, Chunyan Niu, Huifei Wang, Junjun Wang
https://arxiv.org/abs/2509.23191

On the rotating nonlinear Klein-Gordon equation with multiscale effects: structure-preserving methods and applications to vortex dynamics
We study numerical methods for the rotating nonlinear Klein-Gordon (RKG) equation, a fundamental model in relativistic quantum physics, which exhibits highly oscillatory multiscale behavior due to the presence of a small parameter ε. The RKG equation models rotating galaxies under the Minkowski metric and also provides an effective description of phenomena such as cosmic superfluids. This work focuses on the development and rigorous analysis of structure-preserving Galerkin finite element meth…

Correlative 3D Mapping of Structure, Composition, and Valence State Dynamics in Battery Cathodes via Simultaneous ADF-EDS-EELS Tomography
Jaewhan Oh, Sunggu Kim, Chaehwa Jeong, Jason Manassa, Jonathan Schwartz, Sangmoon Yoon, Robert Hovden, Hye Ryung Byon, Yongsoo Yang
https://arxiv.org/abs/2509.23034

Correlative 3D Mapping of Structure, Composition, and Valence State Dynamics in Battery Cathodes via Simultaneous ADF-EDS-EELS Tomography
Understanding degradation in battery cathodes and other functional materials requires simultaneous knowledge of structural, chemical, and electronic changes in three dimensions (3D). Here, we present a simultaneous ADF-EDS-EELS tomography method that enables 3D mapping of atomic structure, composition, valence states, and transition metal inhomogeneity within a single, low-dose STEM tilt series acquisition. Applied to LiNi1/3Co1/3Mn1/3O2 (NCM111) particles at different electrochemical cycling s…

Coupled Planetary Interior and Tidal Evolution
Tim Hallatt, Sarah Millholland
https://arxiv.org/abs/2509.22923 https://arxiv.org/pdf/2509.22923

Coupled Planetary Interior and Tidal Evolution
We present a new planetary structure/thermal evolution model, designed for use in problems that couple orbital dynamics with planetary structure. We first benchmark our structural/thermal evolution calculations against the \texttt{MESA} stellar evolution code, finding excellent agreement across a wide range of planet mass, equilibrium temperature, entropy, and extra heating deposited at various depths in the planet. We then apply our method to study the tidal migration histories of Neptunes in …

PredNext: Explicit Cross-View Temporal Prediction for Unsupervised Learning in Spiking Neural Networks
Yiting Dong, Jianhao Ding, Zijie Xu, Tong Bu, Zhaofei Yu, Tiejun Huang
https://arxiv.org/abs/2509.24844

PredNext: Explicit Cross-View Temporal Prediction for Unsupervised Learning in Spiking Neural Networks
Spiking Neural Networks (SNNs), with their temporal processing capabilities and biologically plausible dynamics, offer a natural platform for unsupervised representation learning. However, current unsupervised SNNs predominantly employ shallow architectures or localized plasticity rules, limiting their ability to model long-range temporal dependencies and maintain temporal feature consistency. This results in semantically unstable representations, thereby impeding the development of deep unsupe…

Particle Acceleration along Magnetic Fields as the Origin of Ear-like Structures in Supernova Remnants
Huan Yu, Jun Fang
https://arxiv.org/abs/2509.23547 https://

Particle Acceleration along Magnetic Fields as the Origin of Ear-like Structures in Supernova Remnants
The origin of ear-like structures with two opposite lobes extending from the shell of supernova remnants (SNRs) remains a subject of active debate, with proposed mechanisms ranging from jet-driven explosions to interactions with bipolar circumstellar media. We present a novel investigation using Particle-in-Cell (PIC) simulations to examine the dynamics of a spherical ejecta in magnetized media, revealing a different formation mechanism for these structures. Our results demonstrate that particl…

DRIFT-Net: A Spectral--Coupled Neural Operator for PDEs Learning
Jiayi Li, Flora D. Salim
https://arxiv.org/abs/2509.24868 https://arxiv.org/pdf/2509.24868…

DRIFT-Net: A Spectral--Coupled Neural Operator for PDEs Learning
Learning PDE dynamics with neural solvers can significantly improve wall-clock efficiency and accuracy compared with classical numerical solvers. In recent years, foundation models for PDEs have largely adopted multi-scale windowed self-attention, with the scOT backbone in \textsc{Poseidon} serving as a representative example. However, because of their locality, truly globally consistent spectral coupling can only be propagated gradually through deep stacking and window shifting. This weakens…

wiki_link_dyn: Wikipedia link dynamics (2011)
Six networks of the evolving hyperlink structure among wikipedia articles, for simple English (en), German (de), Dutch (nl), Polish (pl), Italian (it), French (fr), taken in August 2011. Each edge is timestamped, and an edge (i,j) indicates that article i hyperlinks to j. The edge sign indicates the addition ( 1) or deletion (-1) of that link at the specified time.
This network has 100312 nodes and 1627472 edges.
Tags: Informati…

Predictive Criteria for Electrospray-Assisted Droplet Dynamics in Aerodynamic Flow Fields
Ethan Kahn (Unaffiliated, Z\"urich, Switzerland)
https://arxiv.org/abs/2509.22676 …

Predictive Criteria for Electrospray-Assisted Droplet Dynamics in Aerodynamic Flow Fields
Electrospray technology enables external electric fields to steer charged droplets, with potential applications in fuel-air mixing and aerodynamic flow control. This study develops a computational framework that couples a steady OpenFOAM RANS solution of a NACA 1912 airfoil channel flow with a reduced-order Lagrangian particle model to examine droplet trajectories under viscous drag and electrostatic forces. Baseline uniform-flow tests confirmed that electrostatic deflection weakens as inertial…

Multi-Robot Allocation for Information Gathering in Non-Uniform Spatiotemporal Environments
Kaleb Ben Naveed, Haejoon Lee, Dimitra Panagou
https://arxiv.org/abs/2509.22883 https…

Multi-Robot Allocation for Information Gathering in Non-Uniform Spatiotemporal Environments
Autonomous robots are increasingly deployed to estimate spatiotemporal fields (e.g., wind, temperature, gas concentration) that vary across space and time. We consider environments divided into non-overlapping regions with distinct spatial and temporal dynamics, termed non-uniform spatiotemporal environments. Gaussian Processes (GPs) can be used to estimate these fields. The GP model depends on a kernel that encodes how the field co-varies in space and time, with its spatial and temporal length…

Noncommutative Landau problem in graphene: a gauge-invariant analysis with the Seiberg-Witten map
Aslam Halder
https://arxiv.org/abs/2509.23763 https://arx…

Noncommutative Landau problem in graphene: a gauge-invariant analysis with the Seiberg-Witten map
We investigate the relativistic quantum dynamics of amassless electron in graphene in a two-dimensional noncommutative (NC) plane under a constant background magnetic field. To address the issue of gauge invariance, we employ an effective massless NC Dirac field theory, incorporating the Seiberg-Witten (SW) map alongside the Moyal star product. Using this framework, we derive a manifestly gauge-invariant Hamiltonian for a massless Dirac particle, which serves as the basis for studying the relat…

Quasi-Ergodic Control of Multi-Periodic Autoregressive Processes: Formulation and Examples
Vyacheslav Kungurtsev
https://arxiv.org/abs/2509.24729 https://a…

Quasi-Ergodic Control of Multi-Periodic Autoregressive Processes: Formulation and Examples
This work considers state dynamics driven by Periodic Autoregressive Moving Average noise, and control of the system over time. Such processes appear frequently in applications involving the environment, such as energy and agriculture. Managing these systems applying forecasts to make decisions that exhibit foresight and risk aversion while maximizing profits is a challenging control problem that can be computationally difficult for standard scenario- based methods. This paper presents a formul…

DraftMarks: Enhancing Transparency in Human-AI Co-Writing Through Interactive Skeuomorphic Process Traces
Momin N. Siddiqui, Nikki Nasseri, Adam Coscia, Roy Pea, Hari Subramonyam
https://arxiv.org/abs/2509.23505

DraftMarks: Enhancing Transparency in Human-AI Co-Writing Through Interactive Skeuomorphic Process Traces
As generative AI becomes part of everyday writing, questions of transparency and productive human effort are increasingly important. Educators, reviewers, and readers want to understand how AI shaped the process. Where was human effort focused? What role did AI play in the creation of the work? How did the interaction unfold? Existing approaches often reduce these dynamics to summary metrics or simplified provenance. We introduce DraftMarks, an augmented reading tool that surfaces the human-AI …

Reconstructing Quantum States and Expectations via Dynamical Tomography
Marco Peruzzo, Tommaso Grigoletto, Francesco Ticozzi
https://arxiv.org/abs/2509.24636 https://

Reconstructing Quantum States and Expectations via Dynamical Tomography
When the dynamics of a quantum system of interest is known, an informationally-complete set of observables is not needed for state reconstruction via tomographic techniques: letting the system evolve before performing the measurement allows one to effectively extend the available ways to probe the system. This idea leads to dynamical quantum tomography, whose feasibility we characterize for general quantum dynamics using Krylov-based methods. Specializing to Markovian ones, we also provide dete…

Effective Free Energy Landscapes and Hawking-Page Transitions
Choon-Lin Ho
https://arxiv.org/abs/2509.25039 https://arxiv.org/pdf/2509.25039

Effective Free Energy Landscapes and Hawking-Page Transitions
A recent interesting development on the dynamics of black hole phase transitions, the Hawking-Page transition, has been the so-called Gibbs free energy landscape approach. In this formalism, it is assumed that there exists a canonical ensemble of a series of black hole spacetimes with arbitrary horizon radius at a given ensemble temperature. An off-shell Gibbs free energy is defined for every spacetime state in the ensemble, with the horizon radius treated as the order parameter. The minima (ma…

Towards a Certificate of Trust: Task-Aware OOD Detection for Scientific AI
Bogdan Raoni\'c, Siddhartha Mishra, Samuel Lanthaler
https://arxiv.org/abs/2509.25080 https://

Towards a Certificate of Trust: Task-Aware OOD Detection for Scientific AI
Data-driven models are increasingly adopted in critical scientific fields like weather forecasting and fluid dynamics. These methods can fail on out-of-distribution (OOD) data, but detecting such failures in regression tasks is an open challenge. We propose a new OOD detection method based on estimating joint likelihoods using a score-based diffusion model. This approach considers not just the input but also the regression model's prediction, providing a task-aware reliability score. Across num…

Probing the large-scale magnetic field inside the Sun from three decades of observed surface magnetograms
Soumyadeep Chatterjee, Gopal Hazra
https://arxiv.org/abs/2509.23959 htt…

Probing the large-scale magnetic field inside the Sun from three decades of observed surface magnetograms
Space-weather and disturbances in the heliosphere are manifestations of the solar magnetic field, which is solely driven by the interior dynamo, and constraining the solar interior magnetic field and its oscillatory behavior is one of the major challenges in solar physics. Observationally, none of the techniques, including helioseismology, are able to provide an estimation of the interior magnetic field. We reconstruct, for the first time, the dynamics of the interior large-scale magnetic field…

CaRe-BN: Precise Moving Statistics for Stabilizing Spiking Neural Networks in Reinforcement Learning
Zijie Xu, Xinyu Shi, Yiting Dong, Zihan Huang, Zhaofei Yu
https://arxiv.org/abs/2509.23791

CaRe-BN: Precise Moving Statistics for Stabilizing Spiking Neural Networks in Reinforcement Learning
Spiking Neural Networks (SNNs) offer low-latency and energy-efficient decision-making on neuromorphic hardware by mimicking the event-driven dynamics of biological neurons. However, due to the discrete and non-differentiable nature of spikes, directly trained SNNs rely heavily on Batch Normalization (BN) to stabilize gradient updates. In online Reinforcement Learning (RL), imprecise BN statistics hinder exploitation, resulting in slower convergence and suboptimal policies. This challenge limits…

The Orbital Eccentricities of Planets in the Kinematic Thin and Thick Galactic Disks
Sheila Sagear, Sarah Ballard, Kathryne J. Daniel, Adrian M. Price-Whelan, S\'oley \'O. Hyman, Gregory J. Gilbert, Christopher Lam
https://arxiv.org/abs/2509.23973

The Orbital Eccentricities of Planets in the Kinematic Thin and Thick Galactic Disks
The orbital eccentricity distribution of exoplanets is shaped by a combination of dynamical processes, reflecting both formation conditions and long-term evolution. Probing the orbital dynamics of planets in the kinematic thin and thick Galactic disks provides insight into the degree to which stellar and Galactic environmental factors affect planet formation and evolution pathways. The classification of host stars in Galactic kinematic terms constitutes a potentially useful axis for the interpr…

Anti-hyperuniform Critical States of Active Topological Defects
Simon Guldager Andersen, Tianxiang Ma, Makito F. Katsume, Kexin Li, Xiao Liu, Martin Cramer Pedersen, Amin Doostmohammadi
https://arxiv.org/abs/2509.22911

Anti-hyperuniform Critical States of Active Topological Defects
Topological defects are fundamental to the collective dynamics of non-equilibrium systems and in active matter, mediating spontaneous flows, dynamic self-organization, and emergent pattern formation. Here, we reveal critical states in active nematics, marked by slowed defect density relaxation, amplified fluctuations, and heightened sensitivity to activity. Near criticality, defect interactions become long-ranged, scaling with system size, and the system enters an anti-hyperuniform regime with …

Tidal disruption of a magnetized star
Pavel Abolmasov, Omer Bromberg, Amir Levinson, Ehud Nakar
https://arxiv.org/abs/2509.23894 https://arxiv.org/pdf/2509…

Tidal disruption of a magnetized star
Tidal disruptions of stars by supermassive black holes in galactic centers (TDEs) are now being actively studied both theoretically and observationally. They are observed throughout the electromagnetic spectrum, from radio to gamma-rays. It is still unclear how the emission is produced and, in particular, what is the role of the magnetic field of the disrupted star. There are many ways how magnetic fields might affect the dynamics of a TDE. They are likely responsible for the angular momentum t…

Measurement-induced phase transition in interacting bosons from most likely quantum trajectory
Anna Delmonte, Zejian Li, Rosario Fazio, Alessandro Romito
https://arxiv.org/abs/2509.24520

Measurement-induced phase transition in interacting bosons from most likely quantum trajectory
We propose a new theoretical method to describe the monitored dynamics of bosonic many-body systems based on the concept of the most likely trajectory. We show how such trajectory can be identified from the probability distribution of quantum trajectories, i.e. measurement readouts, and how it successfully captures the monitored dynamics beyond the average state. We prove the method to be exact in the case of Gaussian theories and then extend it to the interacting Sine-Gordon model. Although no…

Strong stability and the Schiffer Conjecture for the fluid-elastic semigroup
Karoline Disser
https://arxiv.org/abs/2509.23989 https://arxiv.org/pdf/2509.23…

Strong stability and the Schiffer Conjecture for the fluid-elastic semigroup
In a series of papers, Avalos and Triggiani established the fluid-elastic semigroup for the coupled Stokes-Lamé system modelling the coupled dynamics of a linearly elastic structure immersed in a viscous Newtonian fluid. They analyzed the spectrum of its generator and proved that the semigroup is strongly stable, if the domain of the structure satisfies a geometric condition, i.e. it is not a bad domain. We extend these results in two directions: first, for bad domains, we prove a decompositio…

Very High Frequency Interpolation for Direct Torque Control
Rafael Kourdis, Maciej St\k{e}pie\'n, J\'er\^ome Manhes, Nicolas Mansard, Steve Tonneau, Philippe Sou\`eres, Thomas Flayols
https://arxiv.org/abs/2509.24175

Very High Frequency Interpolation for Direct Torque Control
Torque control enables agile and robust robot motion, but deployment is often hindered by instability and hardware limits. Here, we present a novel solution to execute whole-body linear feedback at up to 40 kHz on open-source hardware. We use this to interpolate non-linear schemes during real-world execution, such as inverse dynamics and learned torque policies. Our results show that by stabilizing torque controllers, high-frequency linear feedback could be an effective route towards unlocking …

Black Hole Entropy and Complexity Growth in Horndeski Gravity within the AdS/BCFT Framework
Fabiano F. Santos, Behnam Pourhassan, Emmanuel N. Saridakis
https://arxiv.org/abs/2509.23430

Black Hole Entropy and Complexity Growth in Horndeski Gravity within the AdS/BCFT Framework
This work investigates the connection between quantum complexity and gravitational dynamics within the framework of Horndeski gravity, extending the AdS/BCFT correspondence to include scalar-tensor interactions. By refining the ``\(complexity = action\)'' conjecture we investigate how Horndeski gravity modifies the Wheeler-DeWitt patch and the causal structure of the black hole. Our analysis reveals that the linear growth of complexity, proportional to the product of black hole entropy and temp…

DyMoDreamer: World Modeling with Dynamic Modulation
Boxuan Zhang, Runqing Wang, Wei Xiao, Weipu Zhang, Jian Sun, Gao Huang, Jie Chen, Gang Wang
https://arxiv.org/abs/2509.24804 …

DyMoDreamer: World Modeling with Dynamic Modulation
A critical bottleneck in deep reinforcement learning (DRL) is sample inefficiency, as training high-performance agents often demands extensive environmental interactions. Model-based reinforcement learning (MBRL) mitigates this by building world models that simulate environmental dynamics and generate synthetic experience, improving sample efficiency. However, conventional world models process observations holistically, failing to decouple dynamic objects and temporal features from static backg…

wiki_link_dyn: Wikipedia link dynamics (2011)
Six networks of the evolving hyperlink structure among wikipedia articles, for simple English (en), German (de), Dutch (nl), Polish (pl), Italian (it), French (fr), taken in August 2011. Each edge is timestamped, and an edge (i,j) indicates that article i hyperlinks to j. The edge sign indicates the addition ( 1) or deletion (-1) of that link at the specified time.
This network has 100312 nodes and 1627472 edges.
Tags: Informati…

Computing Invariant Zeros of a MIMO Linear System Using State-Space Realization
Jhon Manuel Portella Delgado, Ankit Goel
https://arxiv.org/abs/2509.24105 https://

Computing Invariant Zeros of a MIMO Linear System Using State-Space Realization
Poles of a multi-input multi-output (MIMO) linear system can be computed by solving an eigenvalue problem; however, the problem of computing its invariant zeros is equivalent to a generalized eigenvalue problem. This paper revisits the problem of computing the invariant zeros by solving an eigenvalue problem. We introduce a realization called the invariant zero form in which the system's invariant zeros are isolated in a partition of the transformed dynamics matrix. It is shown that the invaria…

Hierarchical Analysis and Control of Epidemic Spreading over Networks using Dissipativity and Mesh Stability
Shirantha Welikala, Hai Lin, Panos J. Antsaklis
https://arxiv.org/abs/2509.24665

Hierarchical Analysis and Control of Epidemic Spreading over Networks using Dissipativity and Mesh Stability
Analyzing and controlling spreading processes are challenging problems due to the involved non-linear node (subsystem) dynamics, unknown disturbances, complex interconnections, and the large-scale and multi-level nature of the problems. The dissipativity concept provides a practical framework for addressing such concerns, thanks to the energy-based representation it offers for subsystems and the compositional properties it provides for the analysis and control of interconnected (networked) syst…

Teleoperator-Aware and Safety-Critical Adaptive Nonlinear MPC for Shared Autonomy in Obstacle Avoidance of Legged Robots
Ruturaj Sambhus, Muneeb Ahmad, Basit Muhammad Imran, Sujith Vijayan, Dylan P. Losey, Kaveh Akbari Hamed
https://arxiv.org/abs/2509.22815

Teleoperator-Aware and Safety-Critical Adaptive Nonlinear MPC for Shared Autonomy in Obstacle Avoidance of Legged Robots
Ensuring safe and effective collaboration between humans and autonomous legged robots is a fundamental challenge in shared autonomy, particularly for teleoperated systems navigating cluttered environments. Conventional shared-control approaches often rely on fixed blending strategies that fail to capture the dynamics of legged locomotion and may compromise safety. This paper presents a teleoperator-aware, safety-critical, adaptive nonlinear model predictive control (ANMPC) framework for shared …

Brain Harmony: A Multimodal Foundation Model Unifying Morphology and Function into 1D Tokens
Zijian Dong, Ruilin Li, Joanna Su Xian Chong, Niousha Dehestani, Yinghui Teng, Yi Lin, Zhizhou Li, Yichi Zhang, Yapei Xie, Leon Qi Rong Ooi, B. T. Thomas Yeo, Juan Helen Zhou
https://arxiv.org/abs/2509.24693

Brain Harmony: A Multimodal Foundation Model Unifying Morphology and Function into 1D Tokens
We present Brain Harmony (BrainHarmonix), the first multimodal brain foundation model that unifies structural morphology and functional dynamics into compact 1D token representations. The model was pretrained on two of the largest neuroimaging datasets to date, encompassing 64,594 T1-weighted structural MRI 3D volumes (~ 14 million images) and 70,933 functional MRI (fMRI) time series. BrainHarmonix is grounded in two foundational neuroscience principles: structure complements function - structu…

Beyond the Hook: Predicting Billboard Hot 100 Chart Inclusion with Machine Learning from Streaming, Audio Signals, and Perceptual Features
Christos Mountzouris
https://arxiv.org/abs/2509.24856

Beyond the Hook: Predicting Billboard Hot 100 Chart Inclusion with Machine Learning from Streaming, Audio Signals, and Perceptual Features
The advent of digital streaming platforms have recently revolutionized the landscape of music industry, with the ensuing digitalization providing structured data collections that open new research avenues for investigating popularity dynamics and mainstream success. The present work explored which determinants hold the strongest predictive influence for a track's inclusion in the Billboard Hot 100 charts, including streaming popularity, measurable audio signal attributes, and probabilistic indi…

Shadow Ringing of Black Holes from Photon Sphere Quasinormal Modes
Reggie C. Pantig
https://arxiv.org/abs/2509.24479 https://arxiv.org/pdf/2509.24479

Shadow Ringing of Black Holes from Photon Sphere Quasinormal Modes
The recent convergence of gravitational-wave (GW) observations and black hole imaging provides complementary probes of strong-gravity dynamics. While the black hole shadow is typically modeled as a static feature, a dynamically perturbed spacetime in its ringdown phase must induce temporal modulations in the shadow's apparent size and shape. We develop a theoretical framework within linear perturbation theory to investigate this shadow ringing effect for a Schwarzschild black hole. By modeling …

Nonuniqueness of Leray-Hopf solutions to the unforced incompressible 3D Navier-Stokes Equation
Thomas Hou, Yixuan Wang, Changhe Yang
https://arxiv.org/abs/2509.25116 https://

Nonuniqueness of Leray-Hopf solutions to the unforced incompressible 3D Navier-Stokes Equation
The nonuniqueness of Leray-Hopf solutions to the unforced incompressible 3D Navier-Stokes equations is one of the central open problems in mathematical fluid dynamics. In this paper, we provide, to our knowledge, the first rigorous computer-assisted proof demonstrating such nonuniqueness. Inspired by earlier works in this area, we construct a Leray-Hopf solution in the self-similar setting and then establish the existence of a second solution by analyzing the stability of the linearized operato…

Passive polymers in active turbulence undergo a collapse-stretch transition
Zahra K. Valei, Davide Marenduzzo, Tyler N. Shendruk
https://arxiv.org/abs/2509.23382 https://…

Passive polymers in active turbulence undergo a collapse-stretch transition
Active processes in living systems generate nonequilibrium forces that deform embedded passive macromolecules. To understand how such dynamics influence polymer conformation, we study a flexible passive chain in an active nematic fluid. Using hybrid simulations, we uncover a length-dependent transition in polymer behavior: long chains align with and stretch along defect-driven flows, while short chains bend and collapse due to localized stresses. These responses are controlled by a competition …

Dynamical feedback control with operator learning for the Vlasov-Poisson system
Jingcheng Lu, Li Wang, Jeff Calder
https://arxiv.org/abs/2509.23063 https://

Dynamical feedback control with operator learning for the Vlasov-Poisson system
To meet the demands of instantaneous control of instabilities over long time horizons in plasma fusion, we design a dynamic feedback control strategy for the Vlasov-Poisson system by constructing an operator that maps state perturbations to an external control field. In the first part of the paper, we propose learning such an operator using a neural network. Inspired by optimal control theory for linearized dynamics, we introduce a low-rank neural operator architecture and train it via adjoint …

wiki_link_dyn: Wikipedia link dynamics (2011)
Six networks of the evolving hyperlink structure among wikipedia articles, for simple English (en), German (de), Dutch (nl), Polish (pl), Italian (it), French (fr), taken in August 2011. Each edge is timestamped, and an edge (i,j) indicates that article i hyperlinks to j. The edge sign indicates the addition ( 1) or deletion (-1) of that link at the specified time.
This network has 100312 nodes and 1627472 edges.
Tags: Informati…

[2025-09-30 Tue (UTC), 22 new articles found for physics.flu-dyn Fluid Dynamics]
toXiv_bot_toot

Model-Free Dynamic Consensus in Multi-Agent Systems: A Q-Function Perspective
Maryam Babazadeh, Naim Bajcinca
https://arxiv.org/abs/2509.24598 https://arxi…

Model-Free Dynamic Consensus in Multi-Agent Systems: A Q-Function Perspective
This paper presents a new method for achieving dynamic consensus in linear discrete-time homogeneous multi-agent systems (MAS) with marginally stable or unstable dynamics. The guarantee of consensus in this setting involves a set of constraints based on the graph's spectral properties, complicating the design of the coupling gains. This challenge intensifies for large-scale systems with diverse graph Laplacian spectra. The proposed approach reformulates the dynamic consensus problem with a pres…

PoseDiff: A Unified Diffusion Model Bridging Robot Pose Estimation and Video-to-Action Control
Haozhuo Zhang, Michele Caprio, Jing Shao, Qiang Zhang, Jian Tang, Shanghang Zhang, Wei Pan
https://arxiv.org/abs/2509.24591

PoseDiff: A Unified Diffusion Model Bridging Robot Pose Estimation and Video-to-Action Control
We present PoseDiff, a conditional diffusion model that unifies robot state estimation and control within a single framework. At its core, PoseDiff maps raw visual observations into structured robot states-such as 3D keypoints or joint angles-from a single RGB image, eliminating the need for multi-stage pipelines or auxiliary modalities. Building upon this foundation, PoseDiff extends naturally to video-to-action inverse dynamics: by conditioning on sparse video keyframes generated by world mod…

Enhancing work-extraction in quantum batteries via correlated reservoirs
Sejal Ahuja, Tanoy Kanti Konar, Aditi Sen De
https://arxiv.org/abs/2509.25109 https://

Enhancing work-extraction in quantum batteries via correlated reservoirs
Going beyond isolated system dynamics, we examine how local and spatially correlated reservoirs influence the work extraction in quantum batteries. By employing a one-dimensional spin-1/2 model coupled to baths via dephasing and amplitude-damping noise, we demonstrate that correlations in reservoirs can significantly enhance battery's performance compared to local noise. In the dephasing scenario, we prove that correlated reservoirs produce a finite amount of extractable work, or ergotropy, dur…

JuggleRL: Mastering Ball Juggling with a Quadrotor via Deep Reinforcement Learning
Shilong Ji, Yinuo Chen, Chuqi Wang, Jiayu Chen, Ruize Zhang, Feng Gao, Wenhao Tang, Shu'ang Yu, Sirui Xiang, Xinlei Chen, Chao Yu, Yu Wang
https://arxiv.org/abs/2509.24892

JuggleRL: Mastering Ball Juggling with a Quadrotor via Deep Reinforcement Learning
Aerial robots interacting with objects must perform precise, contact-rich maneuvers under uncertainty. In this paper, we study the problem of aerial ball juggling using a quadrotor equipped with a racket, a task that demands accurate timing, stable control, and continuous adaptation. We propose JuggleRL, the first reinforcement learning-based system for aerial juggling. It learns closed-loop policies in large-scale simulation using systematic calibration of quadrotor and ball dynamics to reduce…

Crosslisted article(s) found for eess.SY. https://arxiv.org/list/eess.SY/new
[2/2]:
- Learning Hybrid Dynamics via Convex Optimizations
Kaito Iwasaki, Sangli Teng, Anthony Bloch, Maani Ghaffari

VIVALDy: A Hybrid Generative Reduced-Order Model for Turbulent Flows, Applied to Vortex-Induced Vibrations
Niccol\`o Tonioni, Lionel Agostini, Franck Kerherv\'e, Laurent Cordier, Ricardo Vinuesa
https://arxiv.org/abs/2509.24965

VIVALDy: A Hybrid Generative Reduced-Order Model for Turbulent Flows, Applied to Vortex-Induced Vibrations
Developing reduced-order models applicable to fluid-dynamics problems involving complex geometries and different flow conditions remains a critical challenge for turbulent flows. This study introduces VIVALDy, a novel machine-learning framework that employs a hybrid $β$-Variational Autoencoder-Generative Adversarial Network ($β$-VAE-GAN) architecture with masked convolutions to extract dominant flow features into a compact latent space while preserving fidelity at solid-fluid interfaces. A bi…

wiki_link_dyn: Wikipedia link dynamics (2011)
Six networks of the evolving hyperlink structure among wikipedia articles, for simple English (en), German (de), Dutch (nl), Polish (pl), Italian (it), French (fr), taken in August 2011. Each edge is timestamped, and an edge (i,j) indicates that article i hyperlinks to j. The edge sign indicates the addition ( 1) or deletion (-1) of that link at the specified time.
This network has 100312 nodes and 1627472 edges.
Tags: Informati…

Reducing Spatial and Temporal Dimensionality in the Multidimensional Caldeira-Leggett Model
Hongfei Zhan, Ernest W. Z. Pan, Zhenning Cai
https://arxiv.org/abs/2509.24525 https:/…

Reducing Spatial and Temporal Dimensionality in the Multidimensional Caldeira-Leggett Model
Focusing on the real-time dynamics of the reduced density matrix of the multidimensional Caldeira-Leggett model, several techniques are adopted in this paper to reduce the spatial and temporal dimensionality, combined into an efficient algorithm. From a spatial perspective, an equivalent formulation of the Dyson series is presented. With the aid of a low-rank approximation, the spatial dimensionality of open quantum system simulations is halved. From a temporal perspective, the frozen Gaussian …

Rebuild AC Power Flow Models with Graph Attention Networks
Yuting Hu, Jinjun Xiong
https://arxiv.org/abs/2509.22733 https://arxiv.org/pdf/2509.22733…

Rebuild AC Power Flow Models with Graph Attention Networks
A full power flow (PF) model is a complete representation of the physical power network. Traditional model-based methods rely on the full PF model to implement power flow analysis. In practice, however, some PF model parameters can be inaccurate or even unavailable due to the uncertainties or dynamics in the power systems. Moreover, because the power network keeps evolving with possibly changing topology, the generalizability of a PF model to different network sizes and typologies should be con…

Advancing three dimensional modeling of adsorption physics in porous media for gas separation towards a new design of fixed bed adsorbers
Mohamad Najib Nadamani, Mostafa Safdari Shadloo, Talib Dbouk
https://arxiv.org/abs/2509.25062

Advancing three dimensional modeling of adsorption physics in porous media for gas separation towards a new design of fixed bed adsorbers
A new three-dimensional (3D) multiphase computational fluid dynamics (CFD) model for adsorption physics in porous media is developed and validated. The model is constituted at a macroscopic scale that integrates new volumetric source terms in the multi-species gas transport and energy conservation equations. These new terms, for the first time, take into account the impact of pores adsorption occupation rate (PAOR), or gas loading. Transient 3D simulations are performed at an atmospheric pressu…

Quantum Zeno Effect in the Spatial Evolution of a Single Atom
Zheng-Yuan Zhang, Han-Chao Chen, Xin Liu, Li-Hua Zhang, Bang Liu, Shi-Yao Shao, Jun Zhang, Qi-Feng Wang, Qing Li, Yu Ma, Tian-Yu Han, Ya-Jun Wang, Dong-Yang Zhu, Jia-Dou Nan, Yi-Ming Yin, Qiao-Qiao Fang, Dong-Sheng Ding, Bao-Sen Shi
https://arxiv.org/abs/2509.24438

Quantum Zeno Effect in the Spatial Evolution of a Single Atom
The quantum Zeno effect (QZE) reveals that frequent measurements can suppress quantum evolution, but the detailed dynamics of the system under finite-duration measurements in experiments remain insufficiently explored. Here, we employ an optical dipole trap as a projective measurement to study the motion of a single cold atom in free space. By monitoring atomic loss, we directly observe the QZE in single-atom motion in free space and find that the effect of dipole measurements on the atom compr…

Frequency Control and Optimal Power Sharing in Combined Power and Heating Networks with Heat Pumps
Xin Qin, Ioannis Lestas
https://arxiv.org/abs/2509.24051 https://

Frequency Control and Optimal Power Sharing in Combined Power and Heating Networks with Heat Pumps
Heat pumps have the capability for fast adjustments in power consumption with potential connections to large heating-inertia district heating networks, and are thus a very important resource for providing frequency support in low-inertia power systems. Nevertheless, the coupling of power networks with district heating systems renders the underlying dynamics much more involved. It is therefore important to ensure that system stability and appropriate power sharing are maintained. In this paper, …

Scrambling-Enhanced Quantum Battery Charging in Black Hole Analogues
Zhilong Liu, Ying Li, Zehua Tian, Jieci Wang
https://arxiv.org/abs/2509.23598 https://…

Scrambling-Enhanced Quantum Battery Charging in Black Hole Analogues
Black holes constitute nature's fastest quantum information scramblers, a phenomenon captured by gravitational analogue systems such as position-dependent XY spin chains. In these models, scrambling dynamics are governed exclusively by the hopping interactions profile, independent of system size. Utilizing such curved spacetime analogues as quantum batteries, we explore how the black hole scrambling affects charging via controlled quenches of preset scrambling parameters. Our analysis reveals t…

Cause-and-effect approach to turbulence forecasting
\'Alvaro Mart\'inez-S\'anchez, Adri\'an Lozano-Dur\'an
https://arxiv.org/abs/2509.25065 https://

Cause-and-effect approach to turbulence forecasting
Traditional approaches to turbulence forecasting often rely on correlation-based criteria for input selection. These methods may select variables that correlate with the target without truly driving its dynamics, which limits interpretability, generalization, and efficiency. In this work, we introduce a causality-based approach for input selection in turbulence forecasting based on the Synergistic-Unique-Redundant Decomposition (SURD) of causality. This method decomposes the information from ca…

Universality of Shallow Global Quenches in Critical Spin Chains
Julia Wei, M\'eabh Allen, Jack Kemp, Chenbing Wang, Zixia Wei, Joel E. Moore, Norman Y. Yao
https://arxiv.org/abs/2509.22773

Universality of Shallow Global Quenches in Critical Spin Chains
Measuring universal data in the strongly correlated regime of quantum critical points remains a fundamental objective for quantum simulators. In foundational work, Calabrese and Cardy demonstrated how this data governs the dynamics of certain global quenches to 1+1-dimensional conformal field theories. While the quasiparticle picture they introduce has been widely successful in both theory and experiment, their seminal prediction that the critical exponents are simply encoded in the relaxation …

Hill-Type Stability Analysis of Periodic Solutions of Fractional-Order Differential Equations
Paul-Erik Haacker, Remco I. Leine, Renu Chaudhary, Kai Diethelm, Andr\'e Schmidt, Safoura Hashemishahraki
https://arxiv.org/abs/2509.24639

Hill-Type Stability Analysis of Periodic Solutions of Fractional-Order Differential Equations
This paper explores stability properties of periodic solutions of (nonlinear) fractional-order differential equations (FODEs). As classical Caputo-type FODEs do not admit exactly periodic solutions, we propose a framework of Liouville-Weyl-type FODEs, which do admit exactly periodic solutions and are an extension of Caputo-type FODEs. Local linearization around a periodic solution results in perturbation dynamics governed by a linear time-periodic differential equation. In the classical integer…

Scaling rules for multiphase flow in vertical Venturis
Meng-Ke Zhan, Cheng-Gang Xie, Jian-Jun Shu
https://arxiv.org/abs/2509.23786 https://arxiv.org/pdf/25…

Scaling rules for multiphase flow in vertical Venturis
Employing a suitable scaling rule in gas-liquid flow can produce dynamically comparable results, which helps in the development of flow models applicable to a wide range of flow conditions and reduces the carbon footprint; however, matching all dimensionless numbers in gas-liquid flow is a challenge. This study uses a computational fluid dynamics approach to identify key dimensionless numbers that can produce dimensionally equivalent results under a variety of flow conditions in the vertical Ve…

Probabilistic Graybox Characterization of Quantum Devices with Bayesian Neural Networks
Poramet Pathumsoot, Michal Hajdu\v{s}ek, Rodney Van Meter
https://arxiv.org/abs/2509.24232

Probabilistic Graybox Characterization of Quantum Devices with Bayesian Neural Networks
While the Graybox characterization method allows for implicit noise models and is platform-agnostic, the method lacks uncertainty quantification. Characterization of quantum devices is a crucial process that enables researchers to gain insight from experimental settings. Graybox characterization combines known system dynamics with unknown transformations, where the latter is modeled using machine learning. Prediction uncertainty helps researchers make informed decisions. It allows valuable insi…

Tracking Quantum State Collapse/Decoherence in Real Time via a Superposition Trap
Hardeep Singh, Tim Kovachy
https://arxiv.org/abs/2509.23053 https://arxiv…

Tracking Quantum State Collapse/Decoherence in Real Time via a Superposition Trap
We propose a novel method for probing quantum state collapse and decoherence in real time using a mechanism we term the superposition trap. This approach exploits the continuity equation in quantum mechanics to engineer a configuration that spatially confines only coherent superpositions, while allowing decohered or collapsed states to escape. By monitoring this leakage, the dynamics of collapse processes-whether environmentally induced or intrinsic to objective collapse models-can be experimen…

Simultaneous Quantization and Reduction of Constrained Systems
Jianhao M. Yang
https://arxiv.org/abs/2509.22747 https://arxiv.org/pdf/2509.22747

Simultaneous Quantization and Reduction of Constrained Systems
We present a novel framework for quantizing constrained quantum systems in which the processes of quantization and constraint enforcement are performed simultaneously. The approach is based on an extension of the stationary action principle, incorporating an information-theoretic term arising from vacuum fluctuations. Constraints are included directly in the Lagrangian via Lagrange multipliers, allowing the subsequent variational procedure to yield the quantum dynamics without ambiguity regardi…

Study of nuclear magnetic resonance spectra with the multi-model multi-level quantum complex exponential least squares algorithm
Antonio Marquez Romero, Josh J. M. Kirsopp, Giuseppe Buonaiuto, Michal Krompiec
https://arxiv.org/abs/2509.24664

Study of nuclear magnetic resonance spectra with the multi-model multi-level quantum complex exponential least squares algorithm
We present a novel application of the multi-modal, multi-level quantum complex exponential least squares (MM-QCELS) algorithm, a state-of-the-art, early fault-tolerant quantum phase estimation (QPE) technique, to the simulation and analysis of nuclear magnetic resonance (NMR) of spin systems. By leveraging the robustness and precision of MM-QCELS, we demonstrate enhanced phase resolution in quantum simulations of spin dynamics, also in systems with complex coupling topologies. Our approach enab…