arXiv subjects

Juan Zhang

Publications and source records attributed to Juan Zhang.

At least 37 records · Page 2Linked to original sources

arXiv2026

Circuit-Inspired High-Order Neural Networks with Unified Neural Dynamics Modeling for PDE Solving and Visual Perception

Deep networks often rely on architectural heuristics to shape representation evolution, limiting their ability to model data governed by intrinsic dynamics. We present the Circuit-inspired High-Order Neural Network (CHONN), a modular framework that treats representation evolution as a latent potential process and increases its effective order through Kirchhoff-inspired cascade composition. A single Kirchhoff Neural Cell implements a stable first-order update, while serially composed cells form higher-order dynamical operators within one block. This construction is interpretable, numerically stable and compatible with common neural backbones. Theoretical analysis shows that cascaded cells induce end-to-end high-order operators, and controlled experiments demonstrate that intra-block high-order construction differs from generic depth stacking, especially on derivative-sensitive measures. Across steady-state operator learning, long-horizon physical forecasting and ImageNet-1K recognition, CHONN improves structural fidelity, rollout stability and visual representation learning. These results identify high-order circuit composition as a general principle for neural dynamics modeling.

cs.LG↗

arXiv2026

Multiwavelength Analysis of PSR J0437-4715 with Pulse Profile Modeling

We present a multi-wavelength analysis of the nearby millisecond pulsar PSR J0437--4715, combining Hubble Space Telescope (HST) far-ultraviolet, ROSAT soft X-ray, and XMM-Newton X-ray data, to model its broadband emission and energy-resolved pulse profiles, and infer key stellar parameters via Bayesian inference. The broadband emission includes cold thermal, hot thermal, and non-thermal components: cold bulk surface emission is modeled with a non-magnetized partially-ionized hydrogen atmosphere; hot-spot emission adopts the pulse profile modeling technique with a non-magnetized fully-ionized hydrogen atmosphere model; and non-thermal emission is included as a phase-invariant power-law component. By adopting an informative prior on the hot-spot geometry informed by radio polarization position angle measurements, the joint multi-instrument analysis yields a statistically viable and radio-consistent solution with a gravitational mass of 1.38$\pm$0.03~M$_\odot$ and an equatorial circumferential radius of 13.25$_{-0.35}^{+0.34}$~km (68\% confidence intervals). The hot-spot geometry consists of two spherical caps with uniform temperature distributions: the primary hot spot is situated at a colatitude of $\approx$130$^{\circ}$, and the secondary hot spot lies at a colatitude of $\approx$9$^{\circ}$, close to the north pole. It yields tighter radius constraints than HST+ROSAT fits and shifts the radius posterior distribution to larger values relative to NICER-only fits. This work demonstrates the importance of multi-wavelength data in refining neutron star mass-radius measurements and resolving geometric degeneracies.

astro-ph.HE↗

arXiv2026

Coefficient-level output-feedback stabilization of linear port-Hamiltonian descriptor systems

This paper studies coefficient-level, structure-preserving output-feedback stabilization of linear port-Hamiltonian (pH) descriptor systems. Existing stabilization conditions generally require explicit pH representations, which may be costly to compute. We consider descriptor systems for which only the coefficient matrices are available and for which a pH representation is known to exist but is not explicitly given. For proportional output feedback, we derive coefficient-level conditions that are equivalent to the known solvability criteria in the explicit pH setting. These conditions ensure that the closed-loop system is regular, impulse-free, asymptotically stable, and remains port-Hamiltonian. We further extend the framework to proportional-derivative output feedback and enable the assignment of a prescribed dynamical order. Under the proposed conditions, the proportional gain may be chosen as any symmetric positive definite matrix, and the derivative gain is constructed from coefficient-based decompositions, without computing a pH representation.

math.OC↗

arXiv2026

HiRL: Hierarchical Reinforcement Learning for Coordinated Resource Management in Heterogeneous Edge Computing

Edge computing faces unprecedented resource orchestration challenges from multi-dimensional heterogeneity across device architectures, diverse task requirements in CPU-intensive, GPU-intensive, I/O-intensive, and dynamic network conditions. The edge environments demand real-time task processing within strict energy budgets, yet conventional approaches struggle with mixed continuous-discrete optimization while meeting deadline and energy constraints. This paper presents HiRL, a hierarchical reinforcement learning framework that decomposes complex resource orchestration into coordinated power control and task allocation decisions. Our approach separates continuous power management using the Twin Delayed Deep Deterministic Policy Gradient (TD3) and discrete task placement using Double Deep Q-Network (DDQN), unified through a coordination engine with five-dimensional queue state representation. We propose a heterogeneous assessment of resource compatibility with deadline-oriented prioritization and failure-penalized adaptive sampling to enhance decision quality under resource constraints. To improve practical applicability, the framework models comprehensive system dynamics including device mobility, queue congestion patterns, infrastructure heterogeneity, and priority-sensitive scheduling demands. Experimental results show that HiRL achieves effective latency-energy trade-offs with 28% latency reduction compared to Single-DDQN and maintains nearly 100% task completion rates under all load conditions. Compared to baseline algorithms, HiRL reduces energy consumption by up to 51% under low load while achieving 24% better latency performance than static optimization approaches under high load, establishing effective resource orchestration in heterogeneous edge environments.

cs.DC↗

arXiv2026

The trigger and localization system of SVOM-GRM

The Space multi-band Variable Object Monitor (SVOM) is an astronomical satellite jointly developed by China and France, primarily focused on the detection of gamma-ray bursts (GRBs) and transient sources. The SVOM satellite was launched on 22nd June, 2024 with four payloads installed onboard. As one of payload, GRM comprises 3 gamma-ray detectors (each detector has an effective area of approximately 200~cm$^{2}$) with distinct pointing directions, enabling the temporal and spectral measurements as well as localization of GRBs in the energy range of 15-5000 keV. This article firstly introduces the on-board localization algorithm design for GRM and presents preliminary test results. Then, leveraging abundant ground-based computational resources, a joint fitting method for spectral and localization analysis using Monte Carlo Markov Chain (MCMC) is implemented. In contrast to the on-board localization algorithm, the on-ground MCMC method comprehensively considers the influence of spectral characteristics, thereby mitigating systematic biases. Finally, a systematic analysis based on this method is provided, highlighting the localization and spectral measurement capabilities of GRM. The preliminary localization analysis result for the on-board detected GRB 240629A by both GRM and Fermi/GBM shows that the localization result (error$\sim$4.14$^{\circ}$) of GRM is consistent with the Fermi/GBM result.

astro-ph.IM↗

arXiv2026

Study on the detector energy response of SVOM/GRM

The SVOM mission is specifically designed to for the detection and localization of Gamma-Ray Bursts (GRBs) and subsequent follow-up observations. Among the four telescopes installed on the SVOM satellite, the Gamma-Ray Monitor (GRM) plays a crucial role in capturing the prompt emission of GRBs due to its wide field of view (FOV) and broad energy range. Accurate determination of the detector's energy response is vital for analyzing GRM data, particularly considering the significant impact of the atmospheric albedo effect on this response. This research focuses on deriving the detector's energy response and establishing a calibration database for the GRM, with particular emphasis on investigating the atmospheric albedo effect. The study shows that the contribution of albedo photons to the detector's effective area depends strongly on the orientation of the GRD line of sight (LoS) relative to Earth and on the incident direction of the GRB. When the GRD LoS is anti-Earth oriented, the albedo effect is minimal, with the highest proportion of albedo effective area accounting for approximately 10% of the total effective area. This occurs when the incident angle of the GRB is nearly perpendicular to the LoS. Conversely, if the GRD LoS is not pointing away from Earth and the GRB arrives from angles greater than about 90$^{\circ}$, the albedo component can become predominant, contributing up to around 100% of the total effective area. This is especially pronounced in the 8-20 keV range, where the direct effective area drops to zero due to the large GRB injection angle. Our results show that, it is necessary for GRM to consider the atmospheric albedo effects in detector response, otherwise the spectral and localization analyses will result in biased measurements.

astro-ph.HE↗

arXiv2026

The Gamma-Ray Monitor onboard the SVOM satellite

The Gamma-Ray Monitor (GRM) is a key scientific payload onboard the Space-based Multi-band Variable Object Monitor (SVOM) satellite, designed specifically for the detection and study of gamma-ray bursts (GRBs). Launched into a 625 km low-Earth orbit on 22 June 2024, GRM serves as a large-area, wide-field-of-view instrument capable of observing the hard X-ray and soft gamma-ray emissions in the energy range of 15 keV to 5 MeV. Its primary scientific objectives include: promptly triggering and localizing GRBs (with particular sensitivity to short-hard GRBs), measuring spectral and temporal properties of bursts, monitoring charged particle fluxes in orbit. GRM successfully detected its first GRB (GRB 240627B) on 27 June 2024, and has since maintained a detection rate of more than 100 GRBs per year. Cross-instrument comparisons with detectors such as GECAM and Fermi/GBM have validated the performance and data quality of GRM. This paper provides a comprehensive overview of GRM instrument design, reliability verification through ground testing, in-orbit triggering and localization algorithms, performance calibration, and preliminary in-orbit results, demonstrating its capability as a versatile gamma-ray all-sky monitor.

astro-ph.IM↗

arXiv2026

Adaptive Polynomial Filtering for Hermitian Interior Eigenproblems: Convergence Analysis

Interior eigenvalue problems for large-scale sparse Hermitian matrices are fundamental in computational science. We propose an adaptive polynomial filtering strategy based on Chebyshev expansion of a step function, integrated into a filtered subspace iteration framework. We establish pointwise convergence bounds in both undamped and damped settings and incorporate an enhanced spurious eigenvalue detection technique to improve efficiency and robustness. At the implementation level, we employ MaSpMM to accelerate the polynomial filtering step. Numerical results demonstrate the efficiency and robustness of the proposed method compared with classical approaches.

math.NA↗

arXiv2026

Learning domain-invariant features through channel-level sparsification for Out-Of Distribution Generalization

Out-of-Distribution (OOD) generalization has become a primary metric for evaluating image analysis systems. Since deep learning models tend to capture domain-specific context, they often develop shortcut dependencies on these non-causal features, leading to inconsistent performance across different data sources. Current techniques, such as invariance learning, attempt to mitigate this. However, they struggle to isolate highly mixed features within deep latent spaces. This limitation prevents them from fully resolving the shortcut learning problem.In this paper, we propose Hierarchical Causal Dropout (HCD), a method that uses channel-level causal masks to enforce feature sparsity. This approach allows the model to separate causal features from spurious ones, effectively performing a causal intervention at the representation level. The training is guided by a Matrix-based Mutual Information (MMI) objective to minimize the mutual information between latent features and domain labels, while simultaneously maximizing the information shared with class labels.To ensure stability, we incorporate a StyleMix-driven VICReg module, which prevents the masks from accidentally filtering out essential causal data. Experimental results on OOD benchmarks show that HCD performs better than existing top-tier methods.

cs.CV↗

arXiv2026

Weak-PDE-Net: Discovering Open-Form PDEs via Differentiable Symbolic Networks and Weak Formulation

Discovering governing Partial Differential Equations (PDEs) from sparse and noisy data is a challenging issue in data-driven scientific computing. Conventional sparse regression methods often suffer from two major limitations: (i) the instability of numerical differentiation under sparse and noisy data, and (ii) the restricted flexibility of a pre-defined candidate library. We propose Weak-PDE-Net, an end-to-end differentiable framework that can robustly identify open-form PDEs. Weak-PDE-Net consists of two interconnected modules: a forward response learner and a weak-form PDE generator. The learner embeds learnable Gaussian kernels within a lightweight MLP, serving as a surrogate model that adaptively captures system dynamics from sparse observations. Meanwhile, the generator integrates a symbolic network with an integral module to construct weak-form PDEs, avoiding explicit numerical differentiation and improving robustness to noise. To relax the constraints of the pre-defined library, we leverage Differentiable Neural Architecture Search strategy during training to explore the functional space, which enables the efficient discovery of open-form PDEs. The capability of Weak-PDE-Net in multivariable systems discovery is further enhanced by incorporating Galilean Invariance constraints and symmetry equivariance hypotheses to ensure physical consistency. Experiments on several challenging PDE benchmarks demonstrate that Weak-PDE-Net accurately recovers governing equations, even under highly sparse and noisy observations.

cs.LG↗

arXiv2026

A New Method for Identifying Contaminating Sources and Locating Target Sources through the Cross-Arm Features of Micro Pore Optics

The Pathfinder of the Type-A satellites in the Chasing All Transients Constellation Hunters (CATCH) space mission is equipped with Micro-Pore Optics (MPOs) and four single-pixel Silicon Drift Detectors (SDDs). Due to the lack of position resolution in an individual SDD, we propose a new method based on the cross-arms in the point spread function (PSF) of MPOs to enhance the satellite's capability in identifying contaminating sources and locating target sources. By placing one detector on each of the horizontal and vertical cross-arms on the focal plane, we can use the changes in the relative counts on the cross-arms detectors to deduce the location of the source. Simulated observations demonstrate that, for a target source with a flux of 1 Crab and an exposure time of 200 s, the cross-arms detectors can identify contaminating source with the same flux level at an off-axis angle larger than 8', and improve positioning accuracy to 6'. Furthermore, we extend the simulation study to CATCH Type-A, which plans to use an SDD array. In situations where sources exhibit the same flux of 1 Crab and the exposure time is merely 1 s, a 16x16 SDD array is capable of identifying contaminating source with an off-axis angle greater than 2.4' and can achieve a positioning precision of 1.8'.

astro-ph.IM↗

arXiv2026

Error Analysis of Parameter Prediction via Gaussian Process Regression and Its Application to Weighted Jacobi Iteration

In this paper, we introduce a novel theoretical framework for Gaussian process regression error analysis, leveraging a function-space decomposition. Based on this framework, we develop a weighted Jacobi iterative method that utilizes Gaussian process regression for parameter prediction and provide a corresponding convergence analysis. Moreover, the convergence conditions are designed to be compatible with other error bounds, enabling a more general analysis. Experimental results show that the parameters predicted based on Gaussian process regression significantly accelerate the convergence speed of Jacobi iterations.

math.NA↗

arXiv2026

Long-term timing evolution of four Anomalous X-Ray Pulsars

Anomalous X-ray pulsars (AXPs) and soft gamma-ray repeaters (SGRs) are believed to be manifestations of magnetars. Typically, AXPs exhibit higher X-ray luminosities, whereas SGRs are generally fainter and display significantly high signal-to-noise ratios only during their outburst phases. In this work, we report the long-term timing evolution of four AXPs: 1E 2259+586, 4U 0142+61, 1RXS J170849.0-400910 and 1E 1841-045, which were regularly monitored with NICER from 2017 to 2024. Over this period, we identify a total of 10 timing events. In addition to one glitch and one anti-glitch in 1E 2259+586 reported in literature, we detect another 8 new timing events: 5 glitches, 2 anti-glitches, and 1 unusual state transition event. Notably, both anti-glitches were observed in 4U 0142+61, making it the most frequent source of such events, and there is a hint of regular evolution in its pulse profile. In the case of 1RXS J170849.0-400910, it continues to exhibit pronounced high-frequency timing anomalies and undergoes a state transition event. Finally, we study the evolution of the pulse profiles and find that the profiles of 1E 2259+586 and 4U 0142+61 both evolve. This is consistent with the earlier finding that pulse profile evolution is a generic feature of magnetars.

astro-ph.HE↗

arXiv2026

UniSymNet: A Unified Symbolic Network Guided by Transformer

Symbolic Regression (SR) is a powerful technique for automatically discovering mathematical expressions from input data. Mainstream SR algorithms search for the optimal symbolic tree in a vast function space, but the increasing complexity of the tree structure limits their performance. Inspired by neural networks, symbolic networks have emerged as a promising new paradigm. However, most existing symbolic networks still face certain challenges: binary nonlinear operators $\{\times, ÷\}$ cannot be naturally extended to multivariate operators, and training with fixed architecture often leads to higher complexity and overfitting. In this work, we propose a Unified Symbolic Network that unifies nonlinear binary operators into nested unary operators and define the conditions under which UniSymNet can reduce complexity. Moreover, we pre-train a Transformer model with a novel label encoding method to guide structural selection, and adopt objective-specific optimization strategies to learn the parameters of the symbolic network. UniSymNet shows high fitting accuracy, excellent symbolic solution rate, and relatively low expression complexity, achieving competitive performance on low-dimensional Standard Benchmarks and high-dimensional SRBench.

cs.LG↗

arXiv2026

ViSymRe: Vision Multimodal Symbolic Regression

Extracting interpretable equations from observational datasets to describe complex natural phenomena is one of the core goals of artificial intelligence. This field is known as symbolic regression (SR). In recent years, Transformer-based paradigms have become a new trend in SR, addressing the well-known problem of inefficient search. However, the modal heterogeneity between datasets and equations often hinders the convergence and generalization of these models. In this paper, we propose ViSymRe, a Vision Symbolic Regression framework, to explore the positive role of visual modality in enhancing the performance of Transformer-based SR paradigms. To overcome the challenge where the visual SR model is untrainable in high-dimensional scenarios, we present Multi-View Random Slicing (MVRS). By projecting multivariate equations into 2-D space using random affine transformations, MVRS avoids common defects in high-dimensional visualization, such as variable degradation, non-linear interaction missing, and exponentially increasing sampling complexity, enabling ViSymRe to be trained with low computational costs. To support dataset-only deployment of ViSymRe, we design a dual-vision pipeline architecture based on generative techniques, which reconstructs visual features directly from the datasets via an auxiliary Visual Decoder and automatically suppresses the attention weights of reconstruction noise through a proposed Biased Cross-Attention feature fusion module, ensuring that subsequent processes are not affected by noisy modalities. Ablation studies demonstrate the positive contribution of visual modality to improving model convergence level and enhancing various SR metrics. Furthermore, evaluation results on mainstream benchmarks indicate that ViSymRe achieves competitive performance compared to baselines, particularly in low-complexity and rapid-inference scenarios.

cs.LG↗

arXiv2026

A simple, flexible method for timing cross-calibration of space missions

The timing (cross-)calibration of astronomical instruments is often done by comparing pulsar times-of-arrival (TOAs) to a reference timing model. In high-energy astronomy, the choice of solar system ephemerides and source positions used to barycenter the photon arrival times has a significant impact on the procedure, requiring a full reprocessing of the data each time a new convention is used. Our method, developed as part of the activities of the International Astronomical Consortium for High Energy Calibration (IACHEC), adapts an existing pulsar solution to arbitrary JPL ephemerides and source positions by simulating geocentric TOAs and refitting timing models (implemented with PINT). We validate the procedure and apply it to thousands of observations of the Crab pulsar from 15 missions spanning 1996--2025, demonstrating inter-ephemeris TOA consistency at the $\lesssim5 μ$s level, using the DE200/FK5-based Jodrell Bank Monthly Ephemeris as a common reference. We release the TOAExtractor open-source tool and a TOA database to support future calibration and scientific studies. Instrument timing performance is broadly consistent with mission specifications; the X-ray-to-radio phase offset varies with energy and time at a level that is marginally consistent with the uncertainties of the radio ephemeris, motivating coordinated multiwavelength follow-up.

astro-ph.IM↗

arXiv2025

Mixed Precision General Alternating-Direction Implicit Method for Solving Large Sparse Linear Systems

In this article, we introduce a three-precision formulation of the General Alternating-Direction Implicit method (GADI) designed to accelerate the solution of large-scale sparse linear systems $Ax=b$. GADI is a framework that can represent many existing Alternating-Direction Implicit (ADI) methods. These methods are a class of linear solvers based on a splitting of $A$ such that the solution of the original linear system can be decomposed into the successive computation of easy-to-solve structured subsystems. Our proposed mixed precision scheme for GADI solves these subsystems in low precision to reduce the overall execution time while computing the residual and solution update in high precision to enable the solution to converge to high accuracy. We develop a rounding error analysis of mixed precision GADI that establishes the rates of convergence of the forward and backward errors to certain limiting accuracies. Our analysis also highlights the conditions on the splitting matrices under which mixed precision GADI is guaranteed to converge for a given set of precisions. We then discuss a systematic and robust strategy for selecting the GADI regularization parameter $α$, whose adjustment is critical for performance. Specifically, our proposed strategy makes use of a Gaussian Process Regression (GPR) model trained on a dataset of low-dimensional problems to initialize $α$. Finally, we proceed to a performance analysis of mixed precision GADI on an NVIDIA A100 GPU to validate our approach. Using low precision (Bfloat16 or FP32) to solve the subsystems, we obtain speedups of $2.6\times$, $1.7\times$, and $3.1\times$ over a full double precision GADI implementation on large-scale 2D, 3D convection-diffusion and complex reaction-diffusion problems (up to $1.3\times 10^{8}$ unknowns), respectively.

math.NA↗

arXiv2025

Convergent analysis of algebraic multigrid method with data-driven parameter learning for non-selfadjoint elliptic problems

In this paper, we apply the practical GADI-HS iteration as a smoother in algebraic multigrid (AMG) method for solving second-order non-selfadjoint elliptic problem. Additionally, we prove the convergence of the derived algorithm and introduce a data-driven parameter learing method called Gaussian process regression (GPR) to predict optimal parameters. Numerical experimental results show that using GPR to predict parameters can save a significant amount of time cost and approach the optimal parameters accurately.

math.NA↗