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#dynamics

6 posts4 participants0 posts today
Public
Philo Sophies

#Zoomposium with Prof. Dr. #Thomas #Klinger: “The sun on earth - #nuclear fusion as an #energy source”

This time we talk about the #dis-/advantages of #nuclearfusiontechnology with Thomas Klinger, who has been a “Scientific Member” of the #MaxPlanckSociety since 2001 and is Director of the “#Stellarator #Dynamics and #Transport” division at the Institute for #PlasmaPhysics. There he heads the highly successful #Wendelstein7X project.

youtu.be/8bNqFmmXebk

More at: philosophies.de/index.php/2024

portrait of Thomas Klinger
Public
💧🌏 Greg Cocks

Interpretable Physics-Informed Graph Neural Networks For Flood Forecasting
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doi.org/10.1111/mice.13484 <-- shared paper
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github.com/MehdiTaghizadehUVa/ <-- shared GitHub repository
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#GIS #spatial #mapping #FloodForecasting #GraphNeuralNetworks #PhysicsInformedAI #Hydrology #ClimateResilience #ScientificMachineLearning #CivilEngineering #HydroGraphNet #PhyiscsNeMo #hydrology #water #flood #flooding #spatialanalysis #spatiotemporal #climatechange #model #modeling #machinelearning #AI #extremeweather #prediction #forecasting #planning #humanimpacts #loss #lossoflife #publicsafety #cost #economics #infrastructure #risk #hazard #disaster #mitigation #preparedness #naturaldisaster #HydroGraphNet #floodforecasting #dynamics #remotesensing #earthobservation

Public
Rxiv mechanobio

📰 "A Navier-Stokes-Peridynamics hybrid algorithm for the coupling of compressible flows and fracturing materials"
arxiv.org/abs/2504.11006 #Physics.Comp-Ph #Dynamics #Cell

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arXiv.orgA Navier-Stokes-Peridynamics hybrid algorithm for the coupling of compressible flows and fracturing materialsModeling and simulation of fluid-structure interactions are crucial to the success of aerospace engineering. This work addresses a novel hybrid algorithm that models the close coupling between compressible flows and deformable materials using a mesoscopic approach. Specifically, the high-speed flows are described by the gas-kinetic scheme, which is a robust Navier-Stokes alternative solver built on the molecular kinetic theory. The deformation, damage, and fracture of materials are depicted using the bond-based peridynamics, which serves as coarse-grained molecular dynamics to construct non-local extensions of classical continuum mechanics. The evolution of fluids and materials are closely coupled using the ghost-cell immersed boundary method. Within each time step, the solutions of flow and solid fields are updated simultaneously, and physics-driven boundary conditions are exchanged for each other via ghost cells. Extensive numerical experiments, including crack propagation in a pre-cracked plate, subsonic flow around the NACA0012 airfoil, supersonic flow around the circular cylinder, and shock wave impacting on the elastic panel, are performed to validate the algorithm. The simulation results demonstrate the unique advantages of current hybrid algorithm in solving fracture propagation induced by high-speed flows.
Public
Rxiv mechanobio

📰 "Theory of active self-organization of dense nematic structures in the actin cytoskeleton"
arxiv.org/abs/2306.15352 #Physics.Bio-Ph #Cond-Mat.Soft #Dynamics #Actin

arXiv logo
arXiv.orgTheory of active self-organization of dense nematic structures in the actin cytoskeletonThe actin cytoskeleton is remarkably adaptable and multifunctional. It often organizes into nematic bundles such as contractile rings or stress fibers. However, how a uniform and isotropic actin gel self-organizes into dense nematic bundles is not fully understood. Here, using an active gel model accounting for nematic order and density variations, we identify an active patterning mechanism leading to localized dense nematic structures. Linear stability analysis and nonlinear finite element simulations establish the conditions for nematic bundle self-assembly and how active gel parameters control the architecture, orientation, connectivity and dynamics of self-organized patterns. Finally, we substantiate with discrete network simulations the main requirements for nematic bundle formation according to our theory, namely increased active tension perpendicular to the nematic direction and generalized active forces conjugate to nematic order. Our work portrays actin gels a reconfigurable active materials with a spontaneous tendency to develop patterns of dense nematic bundles.
Public
Rxiv mechanobio

📰 "Topological $\pi/2$ modes in photonic waveguide arrays"
arxiv.org/abs/2504.09945 #Cond-Mat.Mes-Hall #Physics.Optics #Dynamics #Cell

arXiv logo
arXiv.orgTopological $π/2$ modes in photonic waveguide arraysPeriodic driving is a powerful tool to generate exotic topological phases without static counterparts, such as the anomalous chiral edge modes from bulk bands with zero Chern number and topological $π$ modes exhibiting period-doubled dynamics. Recently, a new class of Floquet topological mode, namely the $π/2$ mode, which carries four-period periodicity and has potential applications in quantum computing, was proposed based on a square-root method and realized in an acoustic system. Here we propose a laser-written waveguide array lattice to realize topological $π/2$ modes in photonics. Our photonic model simulates a square-root periodically driven Su-Schrieffer-Heeger model and has a rich phase diagram allowing for the co-existence of conventional zero, $π$ modes, and the new $π/2$ modes. Through numerical simulations of the wave equation, we uncover the unique four-period evolution feature of the $π/2$ modes. Our model, which only contains four waveguides per unit cell and two driving steps, is easy to implement with current fabrication techniques and may find applications in quantum optics.
Public
Rxiv mechanobio

📰 "From Radiation Dose to Cellular Dynamics: A Discrete Model for Simulating Cancer Therapy"
arxiv.org/abs/2504.08499 #Physics.Bio-Ph #Physics.Med-Ph #Dynamics #Forces #Cell

arXiv logo
arXiv.orgFrom Radiation Dose to Cellular Dynamics: A Discrete Model for Simulating Cancer TherapyRadiation therapy is one of the most common cancer treatments, and dose optimization and targeting of radiation are crucial since both cancerous and healthy cells are affected. Different mathematical and computational approaches have been developed for this task. The most common mathematical approach, dating back to the late 1970's, is the linear-quadratic (LQ) model for the survival probability given the radiation dose. Most simulation models consider tissue as a continuum rather than consisting of discrete cells. While reasonable for large scale models, e.g., for human organs, any cellular scale effects become, by necessity, neglected. They do, however, influence growth, morphology, and metastasis of tumors. Here, we propose a method for modeling the effect of radiation on cells based on the mechanobiological \textsc{CellSim3D} simulation model for growth, division, and proliferation of cells. To model the effect of a radiation beam, we incorporate a Monte Carlo procedure into \textsc{CellSim3D} with the LQ model by introducing a survival probability at each beam delivery. Effective removal of dead cells by phagocytosis was also implemented. Systems with two types of cells were simulated: stiff slowly proliferating healthy cells and soft rapidly proliferating cancer cells. For model verification, the results were compared to prostate cancer (PC-3 cell line) data for different doses and we found good agreement. In addition, we simulated proliferating systems and analyzed the probability density of the contact forces. We determined the state of the system with respect to the jamming transition and found very good agreement with experiments.
Public
Rxiv mechanobio

📰 "An Efficient Integrator Scheme for Sampling the (Quantum) Isobaric-Isothermal Ensemble in (Path Integral) Molecular Dynamics Simulations"
arxiv.org/abs/2504.08342 #Cond-Mat.Stat-Mech #Physics.Class-Ph #Physics.Comp-Ph #Physics.Chem-Ph #Physics.Bio-Ph #Dynamics #Pressure #Cell

arXiv logo
arXiv.orgAn Efficient Integrator Scheme for Sampling the (Quantum) Isobaric-Isothermal Ensemble in (Path Integral) Molecular Dynamics SimulationsBecause most chemical or biological experiments are performed under conditions of controlled pressure and temperature, it is important to simulate the isobaric-isothermal ensemble at the atomic level to reveal the microscopic mechanism. By extending our configuration sampling protocol for the canonical ensemble, we propose a unified middle scheme to sample the coordinate (configuration) and volume distribution and thereby are able to accurately simulate either classical or quantum isobaric-isothermal processes. Various barostats and thermostats can be employed in the unified middle scheme for simulating real molecular systems with or without holonomic constraints. In particular, we demonstrate the recommended middle scheme by employing the Martyna-Tuckerman-Tobias-Klein barostat and stochastic cell-rescaling barostat, with the Langevin thermostat, in molecular simulation packages (DL_POLY, Amber, Gromacs, etc.). Benchmark numerical tests show that, without additional numerical effort, the middle scheme is competent in increasing the time interval by a factor of 5~10 to achieve the same accuracy of converged results for most thermodynamic properties in (path integral) molecular dynamics simulations.
Public
Charo del Genio

New paper, just out.

Often, in real-world situations, one does not know the full structure of a network. However, at the same time, one can often observe some interactions that take place on it, and may be interested in knowing its full structure. For example, one may be detecting some partial criminal activity and may want to determine the whole organization. We consider higher-order networks, which are structures with many-body interactions, and specifically simplicial complexes, and show that one can reconstruct a whole network almost perfectly simply by observing the transient of the dynamics that takes place on it. In fact, we give 3 different algorithms to do it, with different complexities and accuracies, so you can choose which one suits you best.

Nine plots, arranged in a 3-by-3 grid. The columns, left to right, correspond to three real-world data sets, marked "Malawi", "SFHH" and "LH10", left to right. The rows, from top to bottom, correspond to the three algorithms introduced in the article, namely PBP, GLO and GLOC. Each plot show how the F1 score, on the y axis, very quickly approaches almost 100% for 1-simplices and 2-simplices as the size of the observation set, on the x axis, increases. In general, GLO and GLOC perform better than PBP.
#physics#mathematics#networks
Public
Rxiv mechanobio

📰 "FJ-MM: The Friedkin-Johnsen Opinion Dynamics Model with Memory and Higher-Order Neighbors"
arxiv.org/abs/2504.06731 #Physics.Soc-Ph #Dynamics #Eess.Sy #Math.Oc #Matrix #Cs.Sy #Cs.Ma

arXiv logo
arXiv.orgFJ-MM: The Friedkin-Johnsen Opinion Dynamics Model with Memory and Higher-Order NeighborsThe Friedkin-Johnsen (FJ) model has been extensively explored and validated, spanning applications in social science, systems and control, game theory, and algorithmic research. In this paper, we introduce an advanced generalization of the FJ model, termed FJ-MM which incorporates both memory effects and multi-hop (higher-order neighbor) influence. This formulation allows agents to naturally incorporate both current and previous opinions at each iteration stage. Our numerical results demonstrate that incorporating memory and multi-hop influence significantly reshapes the opinion landscape; for example, the final opinion profile can exhibit reduced polarization. We analyze the stability and equilibrium properties of the FJ-MM model, showing that these properties can be reduced to those of a comparison model--namely, the standard FJ model with a modified influence matrix. This reduction enables us to leverage established stability results from FJ dynamics. Additionally, we examine the convergence rate of the FJ-MM model and demonstrate that, as can be expected, the time lags introduced by memory and higher-order neighbor influences result in slower convergence.
Public
Rxiv mechanobio

📰 "Retinotopic Mechanics derived using classical physics"
arxiv.org/abs/2109.11632 #Physics.Bio-Ph #Mechanics #Q-Bio.Nc #Dynamics #Cell

arXiv logo
arXiv.orgRetinotopic Mechanics derived using classical physicsThe concept of a cell$'$s receptive field is a bedrock in systems neuroscience, and the classical static description of the receptive field has had enormous success in explaining the fundamental mechanisms underlying visual processing. Borne out by the spatio-temporal dynamics of visual sensitivity to probe stimuli in primates, I build on top of this static account with the introduction of a new computational field of research, retinotopic mechanics. At its core, retinotopic mechanics assumes that during active sensing receptive fields are not static but can shift beyond their classical extent. Specifically, the canonical computations and the neural architecture that supports these computations are inherently mediated by a neurobiologically inspired force field (e.g.,$R_s\propto \sim 1 /ΔM$). For example, when the retina is displaced because of a saccadic eye movement from one point in space to another, cells across retinotopic brain areas are tasked with discounting the retinal disruptions such active surveillance inherently introduces. This neural phenomenon is known as spatial constancy. Using retinotopic mechanics, I propose that to achieve spatial constancy or any active visually mediated task, retinotopic cells, namely their receptive fields, are constrained by eccentricity dependent elastic fields. I propose that elastic fields are self-generated by the visual system and allow receptive fields the ability to predictively shift beyond their classical extent to future post-saccadic location such that neural sensitivity which would otherwise support intermediate eccentric locations likely to contain retinal disruptions is transiently blunted.
Public
Rxiv mechanobio

📰 "Retinotopic Mechanics derived using classical physics"
arxiv.org/abs/2109.11632 #Physics.Bio-Ph #Mechanics #Dynamics #Q-Bio.Nc #Cell

arXiv logo
arXiv.orgRetinotopic Mechanics derived using classical physicsThe concept of a cell$'$s receptive field is a bedrock in systems neuroscience, and the classical static description of the receptive field has had enormous success in explaining the fundamental mechanisms underlying visual processing. Borne out by the spatio-temporal dynamics of visual sensitivity to probe stimuli in primates, I build on top of this static account with the introduction of a new computational field of research, retinotopic mechanics. At its core, retinotopic mechanics assumes that during active sensing receptive fields are not static but can shift beyond their classical extent. Specifically, the canonical computations and the neural architecture that supports these computations are inherently mediated by a neurobiologically inspired force field (e.g.,$R_s\propto \sim 1 /ΔM$). For example, when the retina is displaced because of a saccadic eye movement from one point in space to another, cells across retinotopic brain areas are tasked with discounting the retinal disruptions such active surveillance inherently introduces. This neural phenomenon is known as spatial constancy. Using retinotopic mechanics, I propose that to achieve spatial constancy or any active visually mediated task, retinotopic cells, namely their receptive fields, are constrained by eccentricity dependent elastic fields. I propose that elastic fields are self-generated by the visual system and allow receptive fields the ability to predictively shift beyond their classical extent to future post-saccadic location such that neural sensitivity which would otherwise support intermediate eccentric locations likely to contain retinal disruptions is transiently blunted.
Public
Rxiv mechanobio

📰 "From short-sighted to far-sighted: A comparative study of recursive machine learning approaches for open quantum systems"
arxiv.org/abs/2504.02218 #Physics.Chem-Ph #Dynamics #Matrix

arXiv logo
arXiv.orgFrom short-sighted to far-sighted: A comparative study of recursive machine learning approaches for open quantum systemsAccurately modeling open quantum system dynamics is crucial for advancing quantum technologies, yet traditional methods struggle to balance accuracy and efficiency. Machine learning (ML) provides a promising alternative, particularly through recursive models that predict system evolution based on past history. While these models have shown success in predicting single observables, their effectiveness in more complex tasks, such as forecasting the full reduced density matrix (RDM), remains unclear. We extend history-based recursive ML approaches to complex quantum systems, comparing four physics-informed neural network (PINN) architectures: (i) single-RDM-predicting PINN (SR-PINN), (ii) SR-PINN with simulation parameters (PSR-PINN), (iii) multi-RDMs-predicting PINN (MR-PINN), and (iv) MR-PINN with simulation parameters (PMR-PINN). These models are applied to the spin-boson (SB) model and the Fenna-Matthews-Olson (FMO) complex. Our results show that SR-PINN and PSR-PINN, constrained by a narrow history window, fail to capture complex quantum evolution, leading to unstable long-term predictions, especially in nonlinear and highly correlated dynamics. In contrast, MR-PINN and PMR-PINN improve accuracy by extending the forecast horizon, incorporating long-range correlations, and reducing error propagation. Surprisingly, explicitly including simulation parameters such as temperature and reorganization energy in PSR-PINN and PMR-PINN does not consistently enhance accuracy and can even reduce performance, suggesting that these effects are already encoded in the RDM evolution. These findings highlight the limitations of short-sighted recursive forecasting and demonstrate the superior stability and accuracy of far-sighted approaches for long-term predictions.
Public
Rxiv mechanobio

📰 "Identity-Based Language Shift Modeling"
arxiv.org/abs/2504.01552 #Physics.Soc-Ph #Dynamics #Math.Na #Cs.Na #Cell

arXiv logo
arXiv.orgIdentity-Based Language Shift ModelingThe preservation of endangered languages is a widely discussed issue nowadays. Languages represent essential cultural heritage and can provide valuable botanical, biological, and geographical information. Therefore, it is necessary to develop efficient measures to preserve and revitalize endangered languages. However, the language shift process is complex and requires an interdisciplinary approach, including mathematical modeling techniques. This paper develops a new mathematical model that extends previous works on this topic. We introduce the factor of ethnic identity, which is a proxy for a more complex nexus of variables involved in an individual's self-identity and/or a group's identity. This proxy is socially constructed rather than solely inherited, shaped by community-determined factors, with language both indexing and creating the identity. In our model, we divide speakers into groups depending on with which language they identify themselves with. Moreover, every group includes monolinguals and bilinguals. The proposed model naturally allows us to consider cases of language coexistence and describe a broader class of linguistic situations. For example, the simulation results show that our model can result in cyclic language dynamics, drawing a parallel to cell population models. In this way, the proposed mathematical model can serve as a useful tool for developing efficient measures for language preservation and revitalization.
Public
Rxiv mechanobio

📰 "A kinetic model of jet-corona coupling in accreting black holes"
arxiv.org/abs/2504.01062 #Physics.Plasm-Ph #Astro-Ph.He #Dynamics #Cell

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arXiv.orgA kinetic model of jet-corona coupling in accreting black holesBlack hole (BH) accretion disks are often coupled to ultramagnetized and tenuous plasma coronae close to their central BHs. The coronal magnetic field can exchange energy between the disk and the BH, power X-ray emission, and lead to jetted outflows. Up until now, the coronal physics of BH accretion has only been studied using fluid modeling. We construct the first model of a BH feeding on a zero-net-flux accretion disk corona based on kinetic plasma physics. This allows us to self-consistently capture how collisionless relativistic magnetic reconnection regulates the coronal dynamics. We present global, axisymmetric, general relativistic particle-in-cell simulation of a BH coupled, via a series of magnetic loops, to a razor-thin accretion disk. We target the jet-launching regime where the loops are much larger than the BH. We ray-trace high-energy synchrotron lightcurves and track the flow of Poynting flux through the system, including along specific field-line bundles. Reconnection on field lines coupling the BH to the disk dominates the synchrotron output, regulates the flux threading the BH, and ultimately untethers magnetic loops from the disk, ejecting them via a magnetically striped Blandford-Znajek jet. The jet is initially Poynting-dominated, but reconnection operates at all radii, depleting the Poynting power logarithmically in radius. Coronal emission and jet launch are linked through reconnection in our model. This link might explain coincident X-ray flaring and radio-jet ejections observed during hard-to-soft X-ray binary state transitions. It also suggests that striped jet launch could be heralded by a bright coronal counterpart. Our synchrotron signatures resemble variability observed from the peculiar changing-look AGN, 1ES 1927+654, and from Sagittarius A*, hinting that processes similar to our model may be at work in these contexts.
Public
Rxiv mechanobio

📰 "Influence of erythrocyte density on aggregability as a marker of cell age: Dissociation dynamics in extensional flow"
arxiv.org/abs/2409.08877 #Physics.Bio-Ph #Mechanical #Dynamics #Cell

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arXiv.orgInfluence of erythrocyte density on aggregability as a marker of cell age: Dissociation dynamics in extensional flowBlood rheology and microcirculation are strongly influenced by red blood cell (RBC) aggregation. The aggregability of RBCs can vary significantly due to factors such as their mechanical and membrane surface properties, which are affected by cell aging in vivo. In this study, we investigate RBC aggregability as a function of their density, a marker of cell age and mechanical properties, by separating RBCs from healthy donors into different density fractions using Percoll density gradient centrifugation. We examine the dissociation rates of aggregates in a controlled medium supplemented with Dextran, employing an extensional flow technique based on hyperbolic microfluidic constrictions and image analysis, assisted by a convolutional neural network (CNN). In contrast to other techniques, our microfluidic experimental approach highlights the behavior of RBC aggregates in dynamic flow conditions relevant to microcirculation. Our results demonstrate that aggregate dissociation is strongly correlated with cell density and that aggregates formed from the denser fractions of RBCs are significantly more robust than those from the average cell population. This study provides insight into the effect of RBC aging in vivo on their mechanical properties and aggregability, underscoring the importance of further exploration of RBC aggregation in the context of cellular senescence and its potential implications for hemodynamics. Additionally, it suggests that this technique can complement existing methods for improved evaluation of RBC aggregability in health and disease.
Public
Rxiv mechanobio

📰 "Active Hydrodynamic Theory of Euchromatin and Heterochromatin"
arxiv.org/abs/2503.20964 #Physics.Bio-Ph #Cond-Mat.Soft #Q-Bio.Sc #Dynamics #Cell

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arXiv.orgActive Hydrodynamic Theory of Euchromatin and HeterochromatinThe genome contains genetic information essential for cell's life. The genome's spatial organization inside the cell nucleus is critical for its proper function including gene regulation. The two major genomic compartments -- euchromatin and heterochromatin -- contain largely transcriptionally active and silenced genes, respectively, and exhibit distinct dynamics. In this work, we present a hydrodynamic framework that describes the large-scale behavior of euchromatin and heterochromatin, and accounts for the interplay of mechanical forces, active processes, and nuclear confinement. Our model shows contractile stresses from cross-linking proteins lead to the formation of heterochromatin droplets via mechanically driven phase separation. These droplets grow, coalesce, and in nuclear confinement, wet the boundary. Active processes, such as gene transcription in euchromatin, introduce non-equilibrium fluctuations that drive long-range, coherent motions of chromatin as well as the nucleoplasm, and thus alter the genome's spatial organization. These fluctuations also indirectly deform heterochromatin droplets, by continuously changing their shape. Taken together, our findings reveal how active forces, mechanical stresses and hydrodynamic flows contribute to the genome's organization at large scales and provide a physical framework for understanding chromatin organization and dynamics in live cells.
Public
Rxiv mechanobio

📰 "From Chaos to Coherence: Effects of High-Order Synaptic Correlations on Neural Dynamics"
arxiv.org/abs/2504.00300 #Cond-Mat.Dis-Nn #Physics.Bio-Ph #Dynamics #Q-Bio.Nc #Matrix

arXiv logo
arXiv.orgFrom Chaos to Coherence: Effects of High-Order Synaptic Correlations on Neural DynamicsRecurrent Neural Network models have elucidated the interplay between structure and dynamics in biological neural networks, particularly the emergence of irregular and rhythmic activities in cortex. However, most studies have focused on networks with random or simple connectivity structures. Experimental observations find that high-order cortical connectivity patterns affect the temporal patterns of network activity, but a theory that relates such complex structure to network dynamics has yet to be developed. Here, we show that third- and higher-order cyclic correlations in synaptic connectivities greatly impact neuronal dynamics. Specifically, strong cyclic correlations in a network suppress chaotic dynamics, and promote oscillatory or fixed activity. The change in dynamics is related to the form of the unstable eigenvalues of the random connectivity matrix. A phase transition from chaotic to fixed or oscillatory activity coincides with the development of a cusp at the leading edge of the eigenvalue support. We also relate the dimensions of activity to the network structure.
Public
Rxiv mechanobio

📰 "Scalable Superconducting Nanowire Memory Array with Row-Column Addressing"
arxiv.org/abs/2503.22897 #Physics.App-Ph #Dynamics #Cell

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arXiv.orgScalable Superconducting Nanowire Memory Array with Row-Column AddressingDeveloping ultra-low-energy superconducting computing and fault-tolerant quantum computing will require scalable superconducting memory. While conventional superconducting logic-based memory cells have facilitated early demonstrations, their large footprint poses a significant barrier to scaling. Nanowire-based superconducting memory cells offer a compact alternative, but high error rates have hindered their integration into large arrays. In this work, we present a superconducting nanowire memory array designed for scalable row-column operation, achieving a functional density of 2.6$\,$Mb/cm$^{2}$. The array operates at $1.3\,$K, where we implement and characterize multi-flux quanta state storage and destructive readout. By optimizing write and read pulse sequences, we minimize bit errors while maximizing operational margins in a $4\times 4$ array. Circuit-level simulations further elucidate the memory cell's dynamics, providing insight into performance limits and stability under varying pulse amplitudes. We experimentally demonstrate stable memory operation with a minimum bit error rate of $10^{-5}$. These results suggest a promising path for scaling superconducting nanowire memories to high-density architectures, offering a foundation for energy-efficient memory in superconducting electronics.
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