Dynamic Identification Research Articles

Coupling of Lagrangian Mechanics and Physics-Informed Neural Networks for the Identification of Migration Dynamics

An essential aspect of any government in a smart city is to examine the issues of internal and external migration. Migration is a complex phenomenon. In order to effectively manage it, it is not only necessary to be able to accurately predict migration patterns but also to understand which factors influence these patterns. Current approaches to the development of migration models rely on macroeconomic indicators without considering the specificities of intraregional interactions among individuals. In this paper, we propose a method for determining the dynamics of migration balance based on Lagrangian mechanics. We derive and interpret the potential energy of a migration network by introducing specific functions that determine migration patterns. The solution of the migration equations and selection of parameters, as well as external forces, are achieved through the use of physics-informed neural networks. We also use external factors to explain the non-homogeneity in the dynamic equation through the use of a regression model. We analyze settlement priorities using transfer operator theory and invariant density. The findings obtained enable the assessment of migration flows and analysis of external migration factors.

Dynamic critical factors identification: A novel fuzzy DEMATEL method considering heterogeneous information

Dynamic critical factors identification: A novel fuzzy DEMATEL method considering heterogeneous information

Epidemic models in measure spaces: persistence, concentration and oscillations

We investigate the long-time dynamics of a SIR epidemic model in the case of a population of pathogens infecting a homogeneous host population. The pathogen population is structured by a genotypic variable. When the initial mass of the maximal fitness set is positive, we give a precise description of the convergence of the orbit, including a formula for the asymptotic distribution. We also investigate precisely the case of a finite number of regular global maxima and show that the initial distribution may have an influence on the support of the eventual distribution. In particular, the natural process of competition is not always selecting a unique species, but several species may coexist as long as they maximize the fitness function. The dynamics admits a continuum of equilibria which are all non-hyperbolic and whose spectrum cannot be split; this makes the identification of the asymptotic dynamics particularly involved. Nevertheless, in many cases it is possible to compute the eventual distribution of the surviving competitors. In some configurations, species that maximize the fitness may still get extinct depending on the shape of the initial distribution and some other parameters of the model, and we provide a way to characterize when this unexpected extinction happens. Finally, we provide an example of a pathological situation in which the distribution never reaches a stationary distribution but oscillates forever around the set of fitness maxima.

Discovering the climate dependent disease transmission mechanism through learning-explaining framework.

Discovering the climate dependent disease transmission mechanism through learning-explaining framework.

Improved Greedy Identification of latent dynamics with application to fluid flows

Improved Greedy Identification of latent dynamics with application to fluid flows

An excitation trajectory for dynamic parameter identification of robot using Logistic function and Depth-first Search

Abstract The dynamic parameter identification is playing an important role in robot dynamic controller design. However, many excitation trajectories for parameter identification cannot cover enough motion states of the robot joints, which weakens the generalization of the dynamic model. In this paper, an excitation trajectory covering multi-axis motion states based on depth-first search is proposed and spliced from Logistic functions, in which the angular velocity and angular acceleration can be directly obtained from joint angle by the analytical formula. Taking the condition number of observation matrix as an optimization index, the excitation trajectory is optimized based on genetic algorithm. The strategy of iterative identification is adopted, and in each iteration the dynamic parameters are identified according to the last position and torque sequences and updated to the controller until the actual trajectory approaches the desired trajectory. Experimental results show that after two iterations, the tracking accuracy is greatly improved, and the effectiveness of the proposed approach is verified.

Dynamical modelling of rubble pile asteroids using data-driven techniques

Context. Most asteroids are likely rubble pile asteroids (i.e. aggregates of loosely consolidated material). Their dynamics are hard to model due to their granular nature leading to a complex multi-scale and multi-regime system. Aims. This paper proposes a new data-driven methodology that aims to provide an analytical dynamical model of rubble pile asteroids using data coming from different numerical simulations. This analytical model will allow for the use of tools from the dynamical systems theory in order to investigate the results of the simulations while also providing more general results outside of what is contained within the training data. Methods. We use a novel technique called sparse identification of nonlinear dynamics (SINDy) to obtain an analytical model. The data used originate from high-fidelity and numerically complex discrete element simulations that take into account the contact interactions of non-spherical particles and their gravitational forces. Results. We find a simple analytical model that is accurate across various dynamical regimes. The dynamical model was used to characterise the phase space of the system, and we found that the stable region shrinks as the angular momentum increases. This allows for the determination of the critical angular momentum limit for asteroids with different shapes and bulk densities, and what the dynamical fate is of the asteroid after it reaches this limit. Conclusions. This work shows that SINDy is capable of modelling the dynamics of rubble pile asteroids, thus allowing for the characterisation of the phase space using techniques from dynamical systems theory such as finite time Lyapunov exponents.

Quantitative Identification of Emission Sources and Emission Dynamics of Pressure-Relieved Methane Under Variable Mining Intensities

This study addresses the abnormal emission of pressure-relieved methane under high-intensity mining conditions by integrating geostatistical inversion, FLAC3D-COMSOL coupled numerical simulations, and stable carbon–hydrogen isotopic tracing. Focusing on the 12023 working face at Wangxingzhuang Coal Mine, we established a heterogeneous methane reservoir model to analyze the mechanical responses of surrounding rock, permeability evolution, and gas migration patterns under mining intensities of 2–6 m/d. Key findings include the following: (1) When the working face advanced 180 m, vertical stress in concentration zones increased significantly with mining intensity, peaking at 12.89% higher under 6 m/d compared to 2 m/d. (2) Higher mining intensities exacerbated plastic failure in floor strata, with a maximum depth of 47.9 m at 6 m/d, enhancing permeability to 223 times the original coal seam. (3) Isotopic fingerprinting and multi-method validation identified adjacent seams as the dominant gas source, contributing 77.88% of total emissions. (4) Implementing targeted long directional drainage boreholes in floor strata achieved pressure-relief gas extraction efficiencies of 34.80–40.95%, reducing ventilation air methane by ≥61.79% and maintaining return airflow methane concentration below 0.45%. This research provides theoretical and technical foundations for adaptive gas control in rapidly advancing faces through stress–permeability coupling optimization, enabling the efficient interception and resource utilization of pressure-relieved methane. The outcomes support safe, sustainable coal mining practices and advance China’s Carbon Peak and Neutrality goals.

Being One or the Other, Both or Neither: Self‐Categorization Theory, Social Identity Theory and the Issue of Mixed Identities

ABSTRACTIn this article, we discuss how social identity theory (SIT) and self‐categorization theory (SCT) may apply to mechanisms of social identification and self‐categorization among individuals with multiple identities within a single social domain. We focus on individuals with mixed racial–ethnic backgrounds, which provide unique flexibility for their racial–ethnic identities. In line with SCT, we suggest that their racial–ethnic self‐categorization is guided by perceptions of similarity with multiple racial–ethnic categories and that these are influenced by contextual factors, such as the frame of reference. Drawing on SIT, optimal distinctiveness theory and uncertainty reduction theory, we suggest that situationally significant motives may determine Mixed individuals’ levels of identification with different racial–ethnic groups. By integrating predictions from these theories with empirical evidence on Mixed individuals’ experiences, we provide a first step to building a comprehensive theoretical framework and outlining a future research program for understanding the dynamic social identification processes of these individuals.

Antibody-level Bacteria Grabbing by "Mechanic Invasion" of Bioinspired Hedgehog Artificial Mesoporous Nanostructure for Hierarchical Dynamic Identification and Light-Response Sterilization.

The interactions exploration between microorganisms and nanostructures are pivotal steps toward advanced applications, but the antibody-level bacteria grabbing is limited by the poor understanding of interface identification mechanisms in small-sized systems. Herein, the de novo design of a bioinspired hedgehog artificial mesoporous nanostructure (core-shell mesoporous Au@Pt (mAPt)) are proposed to investigate the association between the topography design and efficient bacteria grabbing. These observations indicate that virus-like spiky topography compensates for the obstacles faced by small-sized materials for bacteria grabbing, including the lack of requisite microscopic cavities and sufficient contact area. Molecular dynamics simulation reveals that spiky topography with heightened mechano-invasiveness (6.56×103 KJ mol-1) facilitates antibody-level bacteria grabbing, attributed to the "mechanic invasion"-induced hierarchical dynamic identification ranging from rough surface contact to penetration fixation. Furthermore, light reflectance and finite element calculation confirmed that mAPt exhibits near-superblack characteristic and plasmonic hot spot, facilitating enhanced photothermal conversion with power dissipation density at 2.04×1021W m-3. After integrating the hierarchical dynamic identification with enhanced light response, mAPt enables advanced applications in immunoassay with 50-fold sensitivity enhancement and over 99.99% in vitro photothermal sterilization. It is anticipated that this novel biomimetic design provides a deeper understanding of bacteria grabbing and a promising paradigm for bacteria combating.

Uncovering Dynamical Equations of Stochastic Decision Models Using Data-Driven SINDy Algorithm.

Decision formation in perceptual decision making involves sensory evidence accumulation instantiated by the temporal integration of an internal decision variable toward some decision criterion or threshold, as described by sequential sampling theoretical models. The decision variable can be represented in the form of experimentally observable neural activities. Hence, elucidating the appropriate theoretical model becomes crucial to understanding the mechanisms underlying perceptual decision formation. Existing computational methods are limited to either fitting of choice behavioral data or linear model estimation from neural activity data. In this work, we made use of sparse identification of nonlinear dynamics (SINDy), a data-driven approach, to elucidate the deterministic linear and nonlinear components of often-used stochastic decision models within reaction time task paradigms. Based on the simulated decision variable activities of the models and assuming the noise coefficient term is known beforehand, SINDy, enhanced with approaches using multiple trials, could readily estimate the deterministic terms in the dynamical equations, choice accuracy, and decision time of the models across a range of signal-to-noise ratio values. In particular, SINDy performed the best using the more memory-intensive multi-trial approach while trial-averaging of parameters performed more moderately. The single-trial approach, although expectedly not performing as well, may be useful for real-time modeling. Taken together, our work offers alternative approaches for SINDy to uncover the dynamics in perceptual decision making and, more generally, for first-passage time problems.

A Novel Improvement of Feature Selection for Dynamic Hand Gesture Identification Based on Double Machine Learning.

Causal machine learning is an approach that combines causal inference and machine learning to understand and utilize causal relationships in data. In current research and applications, traditional machine learning and deep learning models always focus on prediction and pattern recognition. In contrast, causal machine learning goes a step further by revealing causal relationships between different variables. We explore a novel concept called Double Machine Learning that embraces causal machine learning in this research. The core goal is to select independent variables from a gesture identification problem that are causally related to final gesture results. This selection allows us to classify and analyze gestures more efficiently, thereby improving models' performance and interpretability. Compared to commonly used feature selection methods such as Variance Threshold, Select From Model, Principal Component Analysis, Least Absolute Shrinkage and Selection Operator, Artificial Neural Network, and TabNet, Double Machine Learning methods focus more on causal relationships between variables rather than correlations. Our research shows that variables selected using the Double Machine Learning method perform well under different classification models, with final results significantly better than those of traditional methods. This novel Double Machine Learning-based approach offers researchers a valuable perspective for feature selection and model construction. It enhances the model's ability to uncover causal relationships within complex data. Variables with causal significance can be more informative than those with only correlative significance, thus improving overall prediction performance and reliability.

Dynamic Identification of Bridges Using Multiple Synchronized Cameras and Computer Vision

This study investigates the application of computer vision techniques in Structural Health Monitoring (SHM). The advantages of multiple synchronized camera setups in capturing and analyzing the dynamic behavior of bridges are researched. The proposed methodology encompasses approach, setup, and data analysis techniques, with the final scope of extracting modal parameters from videos of a vibrating bridge. An operational pedestrian footbridge forced by human-induced vibrations serves as a case study. The findings demonstrate that computer vision techniques employing a multiple synchronized camera approach offer a precise, cost-effective, efficient, and safe alternative to conventional SHM approaches for the dynamic identification of bridges.

Identification of recurrent dynamics in distributed neural populations.

Large-scale recordings of neural activity over broad anatomical areas with high spatial and temporal resolution are increasingly common in modern experimental neuroscience. Recently, recurrent switching dynamical systems have been used to tackle the scale and complexity of these data. However, an important challenge remains in providing insights into the existence and structure of recurrent linear dynamics in neural time series data. Here we test a scalable approach to time-varying autoregression with low-rank tensors to recover the recurrent dynamics in stochastic neural mass models with multiple stable attractors. We demonstrate that the parsimonious representation of time-varying system matrices in terms of temporal modes can recover the attractor structure of simple systems via clustering. We then consider simulations based on a human brain connectivity matrix in high and low global connection strength regimes, and reveal the hierarchical clustering structure of the dynamics. Finally, we explain the impact of the forecast time delay on the estimation of the underlying rank and temporal variability of the time series dynamics. This study illustrates that prediction error minimization is not sufficient to recover meaningful dynamic structure and that it is crucial to account for the three key timescales arising from dynamics, noise processes, and attractor switching.

Data-Driven Dynamics Learning on Time Simulation of SF6 HVDC-GIS Conical Solid Insulators

An HVDC-GIL system with a conical spacer in a radioactive environment is studied in this work using simulated data on COMSOL® Multiphysics. Electromagnetic simulations on a 2D model were performed with varying ion-pair generation rates and potential applied to the system. This article explores machine learning methods to derive time to steady state, dark current, gas conductivity, and surface charge density expressions. The focus was on constructing symbolic representations, which could be interpretable and less prone to overfitting, using the symbolic regression (SR) and sparse identification of nonlinear dynamics (SINDy) algorithms. The study successfully derived the intended expressions, demonstrating the power of symbolic regression. Predictions of dark currents in the gas–ground electrode interface reported an absolute error and mean absolute percentage error (MAPE) of 1.04 × 10−4 pA and 0.01%, respectively. The solid–ground electrode interface reported an error of 8.99 × 10−5 pA and MAPE of 0.04%, showing strong agreement with simulation data. Expressions for time to steady state had a test error of approximately 110 h with MAPE of around 3%. Steady-state gas conductivity expression achieved an absolute error of 0.55 log(S/m) and MAPE of 1%. An interpretable equation was created with SINDy to model the time evolution of surface charge density, achieving a root mean squared error of 1.12 nC/m2/s across time-series data. These results demonstrate the capability of SR and SINDy to provide interpretable and computationally efficient alternatives to time-consuming numerical simulations of HVDC systems under radiation conditions. While the model provides useful insights, performance and practical applications of the expressions can improve with more diverse datasets, which might include experimental data in the future.

Field Investigation and Numerical Modeling for the Seismic Assessment of the Castle of Lanjarón, Spain

The Castle of Lanjarón is a 16th century stronghold located in Andalucía, Spain. After losing its military function, the castle was abandoned, leading to significant decay. Designated a national heritage site in 1985, recent efforts have sought to preserve its historical and cultural value. This study outlines an inspection and diagnosis campaign carried out on the castle. Non-destructive tests (NDTs) were employed to characterize the properties of the masonry, using both mechanical and wave-based methods. Dynamic identification was performed to determine dynamic and modal properties of the structure, which were used to develop and calibrate a three-dimensional (3D) finite element model (FEM) of the west wall, based on homogenized masonry material. Limit analysis and non-linear static (pushover) analysis under various boundary conditions were conducted to determine the maximum relative load factor in the out-of-plane direction. The results were compared to the expected peak ground acceleration (PGA) of the area, showing that the maximum load capacity of the wall exceeds local seismic demands with a safety factor of 1.39. The study highlights the efficacy of pairing a homogenized macro-modeling approach with wave-based and dynamic identification methods, particularly for resource efficiency. Finally, recommendations for future conservation efforts have been provided.

A fast Time-domain identification of bushing dynamics

A fast Time-domain identification of bushing dynamics

BPS2025 - Identification and analysis of allosteric dynamics in troponin C by molecular dynamics simulation

BPS2025 - Identification and analysis of allosteric dynamics in troponin C by molecular dynamics simulation

Rapid Bayesian identification of sparse nonlinear dynamics from scarce and noisy data

Rapid Bayesian identification of sparse nonlinear dynamics from scarce and noisy data

An improved vehicle dynamic load identification method with optimal sensor placement strategy based on augmented Kalman filter

An improved vehicle dynamic load identification method with optimal sensor placement strategy based on augmented Kalman filter