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#dynamics

9 posts5 participants0 posts today
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

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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

📰 "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

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

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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

📰 "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

📰 "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

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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

📰 "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

📰 "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.
Public
Rxiv mechanobio

📰 "A soft particle dynamics method based on shape degrees of freedom"
arxiv.org/abs/2503.22827 #Physics.Comp-Ph #Dynamics #Cell

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arXiv.orgA soft particle dynamics method based on shape degrees of freedomIn this paper, we present a 2D numerical model developed to simulate the dynamics of soft, deformable particles. To accommodate significant particle deformations, the particle surface is represented as a narrow shell composed of mass points that interact through elasto-plastic force laws governing their linear and angular relative displacements. Particle shape changes are controlled by these interactions, in conjunction with a uniform particle core stiffness. We calibrate and verify this model by comparing the deformation of constrained beams under load with theoretical predictions. Subsequently, we explore the diametral compression of a single particle between two walls, focusing on the influence of the particle core stiffness and shell plasticity. Our findings indicate that increased core stiffness reduces particle volume change and promotes the development of faceting through flat contact areas with the walls. To further illustrate the model's capabilities, we apply it to the uniaxial compaction of a granular material composed of core-shell particles. We show that, depending on the core stiffness and shell plasticity, the compaction leads to either a significant reduction of particle volumes or an improved pore filling due to particle shape changes. At high compaction, particle shapes vary: elastic particles without core stiffness become mostly elongated, elastic particles with core stiffness form polygonal shapes, while plastic particles develop elliptical or highly irregular forms. Finally, we simulate the tensile fracture of a tissue composed of elastic or plastic cells, illustrating the model's potential applicability to soft tissues that undergo both large cell deformations and fracture.
Public
Rxiv mechanobio

📰 "Evolution of robust cell differentiation mechanisms under epigenetic feedback"
arxiv.org/abs/2503.20651 #Physics.Bio-Ph #Dynamics #Nlin.Ao #Cell

arXiv.orgEvolution of robust cell differentiation mechanisms under epigenetic feedbackIn multi-cellular organisms, cells differentiate into multiple types as they divide. States of these cell types, as well as their numbers, are known to be robust to external perturbations; as conceptualized by Waddington's epigenetic landscape where cells embed themselves in valleys corresponding with final cell types. How is such robustness achieved by developmental dynamics and evolution? To address this question, we consider a model of cells with gene expression dynamics and epigenetic feedback, governed by a gene regulation network. By evolving the network to achieve more cell types, we identified three major differentiation mechanisms exhibiting different properties regarding their variance, attractors, stability, and robustness. The first of these mechanisms, type A, exhibits chaos and long-lived oscillatory dynamics that slowly transition until reaching a steady state. The second, type B, follows a channeled annealing process where the epigenetic changes in combination with noise shift the stable landscape of the cells towards varying final cell states. Lastly, type C exhibits a quenching process where cell fate is quickly decided by falling into pre-existing fixed points while cell trajectories are separated through periodic attractors or saddle points. We find types A and B to correspond well with Waddington's landscape while being robust. Finally, the dynamics of type B demonstrate a novel method through dimensional reduction of gene-expression states during differentiation. Correspondence with the experimental data of gene expression variance through differentiation is also discussed.
Public
Rxiv mechanobio

📰 "On-the-fly Reduced-Order Modeling of the Filter Density Function with Time-Dependent Subspaces"
arxiv.org/abs/2503.18271 #Physics.Flu-Dyn #Physics.Comp-Ph #Dynamics #Matrix

arXiv.orgOn-the-fly Reduced-Order Modeling of the Filter Density Function with Time-Dependent SubspacesA dynamical low-rank approximation is developed for reduced-order modeling (ROM) of the filtered density function (FDF) transport equation, which is utilized for large eddy simulation (LES) of turbulent reacting flows. In this methodology, the evolution of the composition matrix describing the FDF transport via a set of Langevin equations is constrained to a low-rank matrix manifold. The composition matrix is approximated using a low-rank factorization, which consists of two thin, time-dependent matrices representing spatial and composition bases, along with a small time-dependent coefficient matrix. The evolution equations for spatial and composition subspaces are derived by projecting the composition transport equation onto the tangent space of the low-rank matrix manifold. Unlike conventional ROMs, such as those based on principal component analysis, both subspaces are time-dependent and the ROM does not require any prior data to extract the low-dimensional subspaces. As a result, the constructed ROM adapts on the fly to changes in the dynamics. For demonstration, LES via the time-dependent bases (TDB) is conducted of the canonical configuration of a temporally developing planar CO/H2 jet flame. The flame is rich with strong flame-turbulence interactions resulting in local extinction followed by re-ignition. The combustion chemistry is modeled via the skeletal kinetics, containing 11 species with 21 reaction steps. It is shown that the FDF-TDB yields excellent predictions of various statistics of the thermo-chemistry variables, as compared to the full-order model (FOM).
Public
Rxiv mechanobio

📰 "Phase Stability Analysis of Volume-preserving Algorithms for Accurate Single Particle Orbit Simulations in Tokamak Plasmas"
arxiv.org/abs/2503.18482 #Physics.Plasm-Ph #Dynamics #Matrix

arXiv.orgPhase Stability Analysis of Volume-preserving Algorithms for Accurate Single Particle Orbit Simulations in Tokamak PlasmasSecond-order Volume-preserving algorithms (VPAs) for simulating charged particle motion in electromagnetic fields have been generalized to a rotating angle formulation by using the matrix decomposition methods. Based on this method, the phase stability of this class of VPAs has been analyzed by using the Discrete Fourier Transformations (DFT) technique. It is found that two prominent VPAs, namely the $G_h^2$ and the Boris algorithm, exhibit optimal phase precision for high-frequency (gyro motion) and low-frequency dynamics (transit/bounce motion), respectively. These findings have been empirically verified through numerical experiments. The insights gained from this study enable the selection of an appropriate VPA for practical simulations based on the characteristic frequencies of specific physics problems, which can substantially enhance numerical accuracy and improve computational efficiency for long-term simulations.
Public
Rxiv mechanobio

📰 "Time irreversibility, entropy production and effective temperature are independently regulated in the actin cortex of living cells"
arxiv.org/abs/2503.17016 #Physics.Bio-Ph #Cytoskeletal #Dynamics #Actin

arXiv.orgTime irreversibility, entropy production and effective temperature are independently regulated in the actin cortex of living cellsLiving cells exhibit non-equilibrium dynamics emergent from the intricate interplay between molecular motor activity and its viscoelastic cytoskeletal matrix. The deviation from thermal equilibrium can be quantified through frequency-dependent effective temperature or time-reversal symmetry breaking quantified e.g. through the Kullback-Leibler divergence. Here, we investigate the fluctuations of an AFM tip embedded within the active cortex of mitotic human cells with and without perturbations that reduce cortex activity through inhibition of material turnover or motor proteins. While inhibition of motor activity significantly reduces both effective temperature and time irreversibility, inhibited material turnover leaves the effective temperature largely unchanged but lowers the time irreversibility and entropy production rate. Our experimental findings in combination with a minimal model highlight that time irreversibility, effective temperature and entropy production rate can follow opposite trends in active living systems, challenging in particular the validity of effective temperature as a proxy for the distance from thermal equilibrium. Furthermore, we propose that the strength of thermal noise and the occurrence of time-asymmetric deflection spikes in the dynamics of regulated observables are inherently coupled in living systems, revealing a previously unrecognized link between entropy production and time irreversibility.
Public
Rxiv mechanobio

📰 "Hydrodynamic Interactions in Particle Suspensions: A Perspective on Stokesian Dynamics"
arxiv.org/abs/2503.16083 #Physics.Flu-Dyn #Cond-Mat.Soft #Dynamics #Matrix

arXiv.orgHydrodynamic Interactions in Particle Suspensions: A Perspective on Stokesian DynamicsStokesian Dynamics (SD) is a numerical framework used for simulating hydrodynamic interactions in particle suspensions at low Reynolds number. It combines far-field approximations with near-field lubrication corrections, offering a balance between accuracy and efficiency. This work reviews SD and provides a perspective on future directions for this approach. We outline the mathematical foundations, the method's strengths and weaknesses, and the computational challenges that need to be overcome to work with SD effectively. We also discuss recent advancements that improve the algorithm's efficiency, including the use of iterative solvers and matrix-free approaches. In addition, we highlight the limitations of making stronger, albeit more cost-effective approximations to studying hydrodynamic interactions in dense suspensions than made in SD, such as the two-body Rotne-Prager-Yamakawa (RPY) approximation. To overcome these issues, we propose a hybrid framework that replaces SD's full many-body computations with a neural network trained on SD data. That is, we correct the RPY approximation, while avoiding costly matrix inversions. We demonstrate the potential of this method on a simple system, where we find a close match to SD data while algorithmically outperforming RPY. Our work provides an outlook on the way in which large-scale simulations of particle suspensions can be performed in the foreseeable future.
Public
Rxiv mechanobio

📰 "Efficient approximations of transcriptional bursting effects on the dynamics of a gene regulatory network"
arxiv.org/abs/2406.19109 #Physics.Bio-Ph #Dynamics #Q-Bio.Mn #Q-Bio.Sc #Cell

arXiv.orgEfficient approximations of transcriptional bursting effects on the dynamics of a gene regulatory networkMathematical models of gene regulatory networks are widely used to study cell fate changes and transcriptional regulation. When designing such models, it is important to accurately account for sources of stochasticity. However, doing so can be computationally expensive and analytically untractable, posing limits on the extent of our explorations and on parameter inference. Here, we explore this challenge using the example of a simple auto-negative feedback motif, in which we incorporate stochastic variation due to transcriptional bursting and noise from finite copy numbers. We find that transcriptional bursting may change the qualitative dynamics of the system by inducing oscillations when they would not otherwise be present, or by magnifying existing oscillations. We describe multiple levels of approximation for the model in the form of differential equations, piecewise deterministic processes, and stochastic differential equations. Importantly, we derive how the classical chemical Langevin equation can be extended to include a noise term representing transcriptional bursting. This approximation drastically decreases computation times and allows us to analytically calculate properties of the dynamics, such as their power spectrum. We explore when these approximations break down and provide recommendations for their use. Our analysis illustrates the importance of accounting for transcriptional bursting when simulating gene regulatory network dynamics and provides recommendations to do so with computationally efficient methods.
Public
Rxiv mechanobio

📰 "Virtual reality and web browser visualization of high-intensity laser-matter interactions"
arxiv.org/abs/2503.14632 #Physics.Plasm-Ph #Dynamics #Cell

arXiv.orgVirtual reality and web browser visualization of high-intensity laser-matter interactionsWe present the Virtual Beamline (VBL) application, an interactive web-based platform for visualizing high-intensity laser-matter simulations (and experimental data in the future). Developed at ELI Beamlines facility, VBL integrates a custom-built WebGL engine with WebXR-based Virtual Reality (VR) support, allowing users to explore complex plasma dynamics in non-VR mode on a computer screen or in fully immersive VR mode using a head-mounted display. The application runs directly in a standard web browser, ensuring broad accessibility. VBL enhances the visualization of particle-in-cell simulations by efficiently processing and rendering four main data types: point particles, 1D lines, 2D textures, and 3D volumes. By utilizing interactive 3D visualization, it overcomes the limitations of traditional 2D representations, offering enhanced spatial understanding and real-time manipulation of visualization parameters such as time steps, data layers, colormaps. The user can interactively explore the visualized data by moving their body or using a controller for navigation, zooming, and rotation. These interactive capabilities improve data exploration and interpretation, making the platform valuable for both scientific analysis and educational outreach. We demonstrate the application of VBL in visualizing various high-intensity laser-matter interaction scenarios, including ion acceleration, electron acceleration, $γ$-flash generation, electron-positron pair production, attosecond and spiral pulse generation. The visualizations are hosted online and freely accessible on our server. These studies highlight VBL's ability to provide an intuitive and dynamic approach to exploring large-scale simulation datasets, enhancing research capabilities and knowledge dissemination in high-intensity laser-matter physics.
Public
Rxiv mechanobio

📰 "Nanodroplet Dynamics: Coalescence and Impact"
arxiv.org/abs/2503.13659 #Physics.Flu-Dyn #Dynamics #Adhesion

arXiv.orgNanodroplet Dynamics: Coalescence and ImpactThis study aims to investigate the coalescence-induced jumping of water nanodroplets in a high Ohnesorge number regime (0.4 < Oh < 1) on a superhydrophobic surface and the dynamics of droplets when a stationary droplet on a solid surface is struck by another droplet of similar size from above, using molecular dynamics simulation. The first part of this study identified the critical droplet size below which coalescence-induced jumping terminates, developed a universal jumping mechanism for droplets of all types, explained a special phenomenon of jumping velocity becoming maximum before it approaches zero, and investigated how jumping terminates due to the size difference between droplets. These findings align well with prior micro-level studies and experimental predictions. The second part of this study investigated the jumping process of the merged droplet after the impact of a moving droplet upon a stationary one. The impact velocity, droplet size, surface textures, and wettability are influential factors on the jumping velocity in this case. Scaling laws for maximum spreading time, spreading factor, and restitution coefficient are formulated based on the Weber (We) number and the Reynolds (Re) number. These laws differ from those established for single-droplet impacts. For superhydrophobic surfaces, the spreading time is approximated by three times the droplet radius and impact velocity, and with the dimensionless spreading time, it exhibits a linear relationship with We 0.31. For both cases, the jumping process is primarily governed by the energy available for conversion into the kinetic energy of the merged droplet following dissipation. For the droplet impact case, the energy conversion efficiency becomes constant at high-impact droplet velocities. About 1% of the energy is dissipated due to surface adhesion, which reduces at higher impact velocity.
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