arXiv subjects

Xiaolong He

Publications and source records attributed to Xiaolong He.

At least 19 recordsLinked to original sources

arXiv2026

Testing Cosmic Distance Duality Relation and Transparency with DESI DR2

The Cosmic Distance Duality Relation (CDDR) is a fundamental principle of standard cosmology, linking luminosity (LD) and angular diameter distances (ADD). This work investigates the validity of the CDDR and cosmic transparency by combining the latest Baryon Acoustic Oscillations (BAO) data from DESI DR2, Type Ia Supernovae from Pantheon+, and cosmic chronometers. To address the redshift mismatch between datasets, two distinct reconstruction techniques are employed: Gaussian Process Regression (GPR) and the Free-Knots Method (FKM). The analysis performs null tests on the CDDR under different cosmological priors, finding that the null hypothesis holds and the CDDR is valid within statistical uncertainties. Although mild deviations are observed from the local distance ladder prior, internal consistency calibration indicates that these discrepancies and the Hubble tension may share a common origin, possibly related to systematic effects or new physics. Using multiple phenomenological parameterizations, the deviation parameter is also found to be statistically consistent with zero (e.g., $η_1 = 0.023 \pm 0.027$ for the linear model under Planck priors). Furthermore, the study finds no statistically significant evidence for cosmic opacity. The average of the opacity derivative is compatible with zero ($\langle dτ/dz \rangle = 0.0409 \pm 0.1024$ for GPR and $0.0730 \pm 0.1607$ for FKM). Based on these null results, stringent constraints are placed on the parameter space of Axion-Like Particles (ALPs) and Mini-Charged Particles (MCPs).

astro-ph.CO↗

arXiv2026

Reconstructing Barrow Holographic Dark Energy in $f(Q,T)$ Gravity and Cosmic Constraint

In this work, we reconstruct the cosmological evolution of Barrow Holographic Dark Energy (BHDE) within the framework of modified gravity $f(Q,T)$. Working in the coincident gauge, we incorporate the holographic principle into non-metric gravity with non-minimal matter coupling. To address the dynamical complexity, we adopt a reconstruction approach, deriving the matter density evolution from the modified field equations rather than imposing a conserved fluid a priori. We perform parameter estimation using the latest observational data, including Type Ia supernovae, BAO, and direct Hubble parameter measurements. Our results show that the model provides a theoretical framework to describe late-time cosmic evolution and the universe's accelerated expansion. Despite the additional complexity introduced, the model offers an alternative approach for investigating dark energy within modified gravity theories.

astro-ph.CO↗

arXiv2026

Holographic dark energy in a coasting cosmology

Coasting cosmology offers an intriguing and straightforward framework for understanding the universe. In this work, we employ the Trans-Planckian Censorship Criterion (TCC) conjecture to test the viability of the coasting cosmology and propose an entropic dark energy (EDE) model within this framework. By applying the holographic principle to constrain the dark energy density and adopting the Bekenstein entropy and Tsallis entropy as the constraining entropies of the system, we find that, in a holographic coasting cosmological framework where dark energy and dark matter evolve independently, the Tsallis entropy satisfies certain general assumptions better than the Bekenstein entropy. Thus, there may be a fundamental relationship between Tsallis entropy and dark energy. We utilize observational data from Type Ia Supernovae (SNIa), Baryon Acoustic Oscillations (BAO), and Cosmic Chronometers (CC) to constrain EDE model. The optimal Tsallis parameter obtained aligns well with theoretical expectations. To evaluate the model's fit to the observed data, we calculate the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Kullback Information Criterion (KIC), and compare these metrics with those derived from $Λ$CDM, under which the model shows some improvement. Overall, this model provides a novel and simple on the evolution of the universe.

astro-ph.CO↗

arXiv2026

Testing the cosmological principle with quasars

The inferred velocity is consistent at the 1.56 σ level with the value of 370 km/s from a purely kinematic interpretation of the CMB dipole. Based on the motion direction component analysis, we have not found any significant deviation from cosmological principle in current released quasars data. The cosmological principle posits that the universe is homogeneous and isotropic on the large scales. In history, the cosmological principle was confirmed by various cosmological observations from CMB to large scale structure. However, several new challenges to the cosmological principle were reported in recent years, particularly in radio observations from overdispersed radio source counts to quasars. Here, we firstly present studies on the peculiar velocity of large-scale anisotropy by measuring the dipole signal from the DESI DR1 catalogue with a sample of 1,176,570 quasars (0.8 < z < 3.0). Our analysis reveals the peculiar velocity of $|v| = 443.8 \pm 204.1$ km/s towards $(l, b) = (107.4^\circ \pm 86.8^\circ, 28.4^\circ \pm 45.2^\circ)$ in Galactic coordinates.The motion direction deviates from the CMB dipole (264.02$^\circ$, 48.253$^\circ$). The inferred velocity is consistent at the 1.56 $σ$ level with the value of 370 km/s from a purely kinematic interpretation of the CMB dipole. Based on the motion direction component analysis, we have not found any significant deviation from cosmological principle in current released quasars data.

astro-ph.CO↗

arXiv2026

Identification and Structural Characterization of Twisted Atomically Thin Bilayer Materials by Deep Learning

Two-dimensional materials are expected to play an important role in next-generation electronics and optoelectronic devices. Recently, twisted bilayer graphene and transition metal dichalcogenides have attracted significant attention due to their unique physical properties and potential applications. In this study we describe the use of optical microscopy to collect the color space of chemical vapor deposition (CVD) molybdenum disulfide ($\mbox{MoS}_2$), and the application of a semantic segmentation convolutional neural network (CNN) to accurately and rapidly identify thicknesses of $\mbox{MoS}_2$ flakes. A second CNN model is trained to provide precise predictions on the twist angle of CVD-grown bilayer flakes. This model harnessed a dataset comprising over 10,000 synthetic images, encompassing geometries spanning from hexagonal to triangular shapes. Subsequent validation of the deep learning predictions on twist angles was executed through the second harmonic generation and Raman spectroscopy. Our results introduce a scalable methodology for automated inspection of twisted atomically thin CVD-grown bilayer.

cond-mat.mtrl-sci↗

arXiv2026

Building an Affordable Self-Driving Lab: Practical Machine Learning Experiments for Physics Education Using Internet-of-Things

Machine learning (ML) is transforming modern physics research, but practical, hands-on experience with ML techniques remains limited due to cost and complexity barriers. To address this gap, we introduce an affordable, autonomous, Internet-of-Things (IoT)-enabled experimental platform designed specifically for applied physics education. Utilizing an Arduino microcontroller, a customizable multi-wavelength light emitting diode (LED) array, and photosensors, our setup generates diverse, real-time optical datasets ideal for training and evaluating foundational ML algorithms, including traversal methods, Bayesian inference, and deep learning. The platform facilitates a closed-loop, self-driving experimental workflow, encompassing automated data collection, preprocessing, model training, and validation. Through systematic performance comparisons, we demonstrate the superior ability of deep learning to capture complex nonlinear relationships compared to traversal and Bayesian methods. At approximately $60, this open-source IoT platform provides an accessible, practical pathway for students to master advanced ML concepts, promoting deeper conceptual insights and essential technical skills required for the next generation of physicists and engineers.

physics.ed-ph↗

arXiv2026

Systematic Performance Assessment of Deep Material Networks for Multiscale Material Modeling

Deep Material Networks (DMNs) are structure-preserving, mechanistic machine learning models that embed micromechanical principles into their architectures, enabling strong extrapolation capabilities and significant potential to accelerate multiscale modeling of complex microstructures. A key advantage of these models is that they can be trained exclusively on linear elastic data and then generalized to nonlinear inelastic regimes during online prediction. Despite their growing adoption, systematic evaluations of their performance across the full offline-online pipeline remain limited. This work presents a comprehensive comparative assessment of DMNs with respect to prediction accuracy, computational efficiency, and training robustness. We investigate the effects of offline training choices, including initialization, batch size, training data size, and activation regularization on online generalization performance and uncertainty. The results demonstrate that both prediction error and variance decrease with increasing training data size, while initialization and batch size can significantly influence model performance. Moreover, activation regularization is shown to play a critical role in controlling network complexity and therefore generalization performance. Compared with the original DMN, the rotation-free Interaction-based Material Network (IMN) formulation achieves a 3.4x - 4.7x speed-up in offline training, while maintaining comparable online prediction accuracy and computational efficiency. These findings clarify key trade-offs between model expressivity and efficiency in structure-preserving material networks and provide practical guidance for their deployment in multiscale material modeling.

cs.LG↗

arXiv2025

Formation of the Dormant Black Holes with Luminous Companions from Binary or Triple Systems

Recently, a class of dormant black hole binaries with luminous companions (dBH-LC) has been observed, such as $Gaia$ BH1, BH2, and BH3. Unlike previously discovered X-ray BH binaries, this type of dBH-LC has relatively long orbital periods (typically more than several tens to a few hundred days) and shows very weak X-ray emission. Therefore, studying the formation and evolution of the whole dBH-LC population is also a very interesting problem. Our aim is to study the contribution of massive stars to the dBH-LC population under different evolutionary models (isolated binary evolution (IBE) and hierarchical triple evolution), and different formation channels (such as mass transfer, common envelope evolution). Using the Massive Objects in Binary Stellar Evolution (MOBSE) code, the Triple Stellar Evolution (TSE) code, and the latest initial multiple-star distributions, we model the populations of massive stars. Finally, we calculate the orbital properties, mass distributions, and birthrates of the BH-LC populations formed under these different conditions. In the Milky Way, we calculate that the birthrate of dBH-LC formed through IBE is about 4.35$\times$$10^{-5}$ ${\rm yr}^{-1}$, while the birthrate through triple evolution is about 1.47$\times$$10^{-3}$ ${\rm yr}^{-1}$. This means that the birthrate from triple evolution is one to two orders of magnitude higher than that from IBE. We find that in triple evolution, the main formation channel of dBH-LC is post-merger binaries formed from inner binary mergers triggered by von Zeipel$-$Lidov$-$Kozai oscillations.

astro-ph.SR↗

arXiv2025

Explanation of the Mass Distribution of Binary Black Hole Mergers

Gravitational wave detectors are observing an increasing number of binary black hole (BBH) mergers, revealing a bimodal mass distribution of BBHs, which hints at diverse formation histories for these systems. Using the rapid binary population synthesis code MOBSE, we simulate a series of population synthesis models that include chemically homogeneous evolution (CHE). By considering metallicity-specific star formation and selection effects, we compare the intrinsic merger rates and detection rates of each model with observations. We find that the observed peaks in the mass distribution of merging BBHs at the low-mass end (10\msun) and the high-mass end (35\msun) are contributed by the common envelope channel or stable mass transfer channel (depending on the stability criteria for mass transfer) and the CHE channel, respectively, in our model. The merger rates and detection rates predicted by our model exhibit significant sensitivity to the choice of physical parameters. Different models predict merger rates ranging from 15.4 to $96.7\,\rm{Gpc^{-3}yr^{-1}}$ at redshift $z$ = 0.2, and detection rates ranging from 22.2 to 148.3$\mathrm{yr^{-1}}$ under the assumption of a detectable redshift range of $z \le$ 1.0.

astro-ph.HE↗

arXiv2025

Revisit of Two-dimensional CEM on Crack Branching: from Single Crack-tip Tracking to Multiple Crack-tips Tracking

In this work, a Multiple Crack-tips Tracking algorithm in two-dimensional Crack Element Model (MCT-2D-CEM) is developed, aiming at modeling and predicting advanced and complicated crack patterns in two-dimensional dynamic fracturing problems, such as crack branching and fragmentation. Based on the developed fracture energy release rate formulation of split elementary topology, the Multiple Crack-tips Tracking algorithm is proposed and a series of benchmark examples are provided to validate effectiveness and efficiency in modeling crack branching and fragmentation. Besides, the proposed MCR-2D-CEM can still model single crack propagation but extra micro-cracks are introduced. GPU acceleration is employed in all two-dimensional simulations, providing high computational efficiency, consistency, and accuracy.

cs.CE↗

arXiv2025

Investigating the Correlation between Force Output, Strains, and Pressure for Active Skeletal Muscle Contractions

Measuring the forces of individual muscles in a muscle group around a joint is non-trivial, and researchers have suggested using surrogates for individual muscle forces instead. Traditionally, experimentalists have shown that the force output of the skeletal muscle tissue can be correlated to the intra-muscular pressure (IMP) generated by the muscle belly. However, IMP proves difficult to measure in vivo, due to variations from sensor placement and invasiveness of the procedure. Numerical biomechanical simulations offer a tool to analyze muscle contractions, enabling new insights into the correlations among non-invasive experimentally measurable quantities such as strains, and the force output. In this work, we investigate the correlations between the muscle force output, the principal, shear and volumetric strains experienced by the muscle, as well as the pressure developed within the muscle belly as the tissue undergoes isometric contractions with varying activation profiles and magnitudes. It is observed that pressure does not correlate well with force output under higher sub-maximal and maximal activation levels, especially at locations away from the center of the muscle belly due to pressure relaxation effects. This study reveals strong correlations between force output and the strains at all locations of the belly, irrespective of the type of activation considered. This observation offers evidence for further in vivo studies using experimentally measurable principal and volumetric strains in the muscle belly as proxies for the force generation by the individual muscle and consequently enables the estimation on the contribution of various muscle groups to the total force.

q-bio.TO↗

arXiv2025

Thermodynamically Consistent Latent Dynamics Identification for Parametric Systems

We propose an efficient thermodynamics-informed latent space dynamics identification (tLaSDI) framework for the reduced-order modeling of parametric nonlinear dynamical systems. This framework integrates autoencoders for dimensionality reduction with newly developed parametric GENERIC formalism-informed neural networks (pGFINNs), which enable efficient learning of parametric latent dynamics while preserving key thermodynamic principles such as free energy conservation and entropy generation across the parameter space. To further enhance model performance, a physics-informed active learning strategy is incorporated, leveraging a greedy, residual-based error indicator to adaptively sample informative training data, outperforming uniform sampling at equivalent computational cost. Numerical experiments on the Burgers' equation and the 1D/1V Vlasov-Poisson equation demonstrate that the proposed method achieves up to 3,528x speed-up with 1-3% relative errors, and significant reduction in training (50-90%) and inference (57-61%) cost. Moreover, the learned latent space dynamics reveal the underlying thermodynamic behavior of the system, offering valuable insights into the physical-space dynamics.

cs.LG↗

arXiv2024

Physics-informed active learning with simultaneous weak-form latent space dynamics identification

The parametric greedy latent space dynamics identification (gLaSDI) framework has demonstrated promising potential for accurate and efficient modeling of high-dimensional nonlinear physical systems. However, it remains challenging to handle noisy data. To enhance robustness against noise, we incorporate the weak-form estimation of nonlinear dynamics (WENDy) into gLaSDI. In the proposed weak-form gLaSDI (WgLaSDI) framework, an autoencoder and WENDy are trained simultaneously to discover intrinsic nonlinear latent-space dynamics of high-dimensional data. Compared to the standard sparse identification of nonlinear dynamics (SINDy) employed in gLaSDI, WENDy enables variance reduction and robust latent space discovery, therefore leading to more accurate and efficient reduced-order modeling. Furthermore, the greedy physics-informed active learning in WgLaSDI enables adaptive sampling of optimal training data on the fly for enhanced modeling accuracy. The effectiveness of the proposed framework is demonstrated by modeling various nonlinear dynamical problems, including viscous and inviscid Burgers' equations, time-dependent radial advection, and the Vlasov equation for plasma physics. With data that contains 5-10% Gaussian white noise, WgLaSDI outperforms gLaSDI by orders of magnitude, achieving 1-7% relative errors. Compared with the high-fidelity models, WgLaSDI achieves 121 to 1,779x speed-up.

cs.CE↗

arXiv2024

A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling

Numerical solvers of partial differential equations (PDEs) have been widely employed for simulating physical systems. However, the computational cost remains a major bottleneck in various scientific and engineering applications, which has motivated the development of reduced-order models (ROMs). Recently, machine-learning-based ROMs have gained significant popularity and are promising for addressing some limitations of traditional ROM methods, especially for advection dominated systems. In this chapter, we focus on a particular framework known as Latent Space Dynamics Identification (LaSDI), which transforms the high-fidelity data, governed by a PDE, to simpler and low-dimensional latent-space data, governed by ordinary differential equations (ODEs). These ODEs can be learned and subsequently interpolated to make ROM predictions. Each building block of LaSDI can be easily modulated depending on the application, which makes the LaSDI framework highly flexible. In particular, we present strategies to enforce the laws of thermodynamics into LaSDI models (tLaSDI), enhance robustness in the presence of noise through the weak form (WLaSDI), select high-fidelity training data efficiently through active learning (gLaSDI, GPLaSDI), and quantify the ROM prediction uncertainty through Gaussian processes (GPLaSDI). We demonstrate the performance of different LaSDI approaches on Burgers equation, a non-linear heat conduction problem, and a plasma physics problem, showing that LaSDI algorithms can achieve relative errors of less than a few percent and up to thousands of times speed-ups.

cs.CE↗

arXiv2023

Weak-Form Latent Space Dynamics Identification

Recent work in data-driven modeling has demonstrated that a weak formulation of model equations enhances the noise robustness of a wide range of computational methods. In this paper, we demonstrate the power of the weak form to enhance the LaSDI (Latent Space Dynamics Identification) algorithm, a recently developed data-driven reduced order modeling technique. We introduce a weak form-based version WLaSDI (Weak-form Latent Space Dynamics Identification). WLaSDI first compresses data, then projects onto the test functions and learns the local latent space models. Notably, WLaSDI demonstrates significantly enhanced robustness to noise. With WLaSDI, the local latent space is obtained using weak-form equation learning techniques. Compared to the standard sparse identification of nonlinear dynamics (SINDy) used in LaSDI, the variance reduction of the weak form guarantees a robust and precise latent space recovery, hence allowing for a fast, robust, and accurate simulation. We demonstrate the efficacy of WLaSDI vs. LaSDI on several common benchmark examples including viscid and inviscid Burgers', radial advection, and heat conduction. For instance, in the case of 1D inviscid Burgers' simulations with the addition of up to 100% Gaussian white noise, the relative error remains consistently below 6% for WLaSDI, while it can exceed 10,000% for LaSDI. Similarly, for radial advection simulations, the relative errors stay below 15% for WLaSDI, in stark contrast to the potential errors of up to 10,000% with LaSDI. Moreover, speedups of several orders of magnitude can be obtained with WLaSDI. For example applying WLaSDI to 1D Burgers' yields a 140X speedup compared to the corresponding full order model. Python code to reproduce the results in this work is available at (https://github.com/MathBioCU/PyWSINDy_ODE) and (https://github.com/MathBioCU/PyWLaSDI).

eess.SY↗

arXiv2023

Data-Driven Modeling of an Unsaturated Bentonite Buffer Model Test Under High Temperatures Using an Enhanced Axisymmetric Reproducing Kernel Particle Method

In deep geological repositories for high level nuclear waste with close canister spacings, bentonite buffers can experience temperatures higher than 100 °C. In this range of extreme temperatures, phenomenological constitutive laws face limitations in capturing the thermo-hydro-mechanical (THM) behavior of the bentonite, since the pre-defined functional constitutive laws often lack generality and flexibility to capture a wide range of complex coupling phenomena as well as the effects of stress state and path dependency. In this work, a deep neural network (DNN)-based soil-water retention curve (SWRC) of bentonite is introduced and integrated into a Reproducing Kernel Particle Method (RKPM) for conducting THM simulations of the bentonite buffer. The DNN-SWRC model incorporates temperature as an additional input variable, allowing it to learn the relationship between suction and degree of saturation under the general non-isothermal condition, which is difficult to represent using a phenomenological SWRC. For effective modeling of the tank-scale test, new axisymmetric Reproducing Kernel basis functions enriched with singular Dirichlet enforcement representing heater placement and an effective convective heat transfer coefficient representing thin-layer composite tank construction are developed. The proposed method is demonstrated through the modeling of a tank-scale experiment involving a cylindrical layer of MX-80 bentonite exposed to central heating.

cs.CE↗

arXiv2023

A catalogue and statistical analysis for magnetic stars

Magnetic fields are significant in the structure and evolution of stars. We present a comprehensive catalogue of 1784 known magnetic stars, detailing their identifications, HD numbers, precise locations, spectral types, and averaged quadratic effective magnetic fields among other important information. The group comprises 177 O-type stars, 551 B-type stars, 520 A-type stars, 91 F-type stars, 53 G-type stars, 61 K-type stars, 31 M-type stars, and an additional 300 stars whose spectral classification remains indeterminate. Our analysis examines the statistical properties of these magnetic stars. The relative integrated distribution function and number distribution function for all magnetic stars of the same spectral type can be effectively approximated using an exponential function of the averaged quadratic effective magnetic field. The analysis further reveals that A and B-type stars possess the strongest mean magnetic fields, indicating an easier detection of their magnetic fields.

astro-ph.SR↗

arXiv2023

A Multi-Resolution Physics-Informed Recurrent Neural Network: Formulation and Application to Musculoskeletal Systems

This work presents a multi-resolution physics-informed recurrent neural network (MR PI-RNN), for simultaneous prediction of musculoskeletal (MSK) motion and parameter identification of the MSK systems. The MSK application was selected as the model problem due to its challenging nature in mapping the high-frequency surface electromyography (sEMG) signals to the low-frequency body joint motion controlled by the MSK and muscle contraction dynamics. The proposed method utilizes the fast wavelet transform to decompose the mixed frequency input sEMG and output joint motion signals into nested multi-resolution signals. The prediction model is subsequently trained on coarser-scale input-output signals using a gated recurrent unit (GRU), and then the trained parameters are transferred to the next level of training with finer-scale signals. These training processes are repeated recursively under a transfer-learning fashion until the full-scale training (i.e., with unfiltered signals) is achieved, while satisfying the underlying dynamic equilibrium. Numerical examples on recorded subject data demonstrate the effectiveness of the proposed framework in generating a physics-informed forward-dynamics surrogate, which yields higher accuracy in motion predictions of elbow flexion-extension of an MSK system compared to the case with single-scale training. The framework is also capable of identifying muscle parameters that are physiologically consistent with the subject's kinematics data.

cs.LG↗