arXiv subjects

Xizhe Xie

Publications and source records attributed to Xizhe Xie.

4 recordsLinked to original sources

arXiv2026

One cluster, two clusters, no cluster? -- The curious case of HSC 2686 and Lynga 3

This study was motivated by the search for Planetary Nebulae (PNe) and Open Cluster (OCs) associations. Only 5 PNe are currently in Galactic OCs among the previous $\sim4,800$ OCs known. They are valuable because as cluster members their properties can be linked to their progenitor stars, something not possible for general field PNe. Since the GAIA astrometric satellite first data release, thousands of new OC candidates have been identified. This offers fresh motivation to search them as hosts for known PNe. Statistically we expect 30 OC-PN pairs to be present. We searched these GAIA candidate OCs, found by Hunt \& Reffert (2023), for known central stars of PNe (CSPNe) as a practical proxy for the PNe themselves and one suited to automated searches. We identified 1 potential CSPN (IRAS 15127-5811) of "Likely" PN [GKF2010] MN18 in the HASH database, as a claimed member of the putative cluster HSC~2686. A second cluster (Lynga~3) with similar parameters and in very close angular proximity could also be a potential host. Here we reclassify CSPN IRAS 15127-5811 as a blue supergiant with a surrounding bi-polar nebula. Nevertheless, we collected all available information on proposed member stars for both these clusters. We conclude that neither claimed cluster is real. Our results shed considerable doubt on the veracity of many newly identified OCs that have modest numbers of stellar members.

astro-ph.SR↗

arXiv2026

Asymptotic-Preserving Neural Networks based on Even-odd Decomposition for Multiscale Gray Radiative Transfer Equations

We present a novel Asymptotic-Preserving Neural Network (APNN) approach utilizing even-odd decomposition to tackle the nonlinear gray radiative transfer equations (GRTEs). Our AP loss demonstrates consistent stability concerning the small Knudsen number, ensuring the neural network solution uniformly converges to the diffusion limit solution. This APNN method alleviates the rigorous conservation requirements while simultaneously incorporating an auxiliary deep neural network, distinguishing it from the APNN method based on micro-macro decomposition for GRTE. Several numerical problems are examined to demonstrate the effectiveness of our proposed APNN technique.

math.NA↗

arXiv2025

BF-APNN: A Low-Memory Method for Accelerating the Solution of Radiative Transfer Equations

The Radiative Transfer Equations (RTEs) exhibit high dimensionality and multiscale characteristics, rendering conventional numerical methods computationally intensive. Existing deep learning methods perform well in low-dimensional or linear RTEs, but still face many challenges with high-dimensional or nonlinear RTEs. To overcome these challenges, we propose the Basis Function Asymptotically Preserving Neural Network (BF-APNN), a framework that inherits the advantages of Radiative Transfer Asymptotically Preserving Neural Network (RT-APNN) and accelerates the solution process. By employing basis function expansion on the microscopic component, derived from micro-macro decomposition, BF-APNN effectively mitigates the computational burden associated with evaluating high-dimensional integrals during training. Numerical experiments, which involve challenging RTE scenarios featuring, nonlinearity, discontinuities, and multiscale behavior, demonstrate that BF-APNN substantially reduces training time compared to RT-APNN while preserving high solution accuracy. Moreover, BF-APNN exhibits superior performance in addressing complex, high-dimensional RTE problems, underscoring its potential as a robust tool for radiative transfer computations.

physics.comp-ph↗

arXiv2025

RT-APNN for Solving Gray Radiative Transfer Equations

The Gray Radiative Transfer Equations (GRTEs) are high-dimensional, multiscale problems that pose significant computational challenges for traditional numerical methods. Current deep learning approaches, including Physics-Informed Neural Networks (PINNs) and Asymptotically Preserving Neural Networks (APNNs), are largely restricted to low-dimensional or linear GRTEs. To address these challenges, we propose the Radiative Transfer Asymptotically Preserving Neural Network (RT-APNN), an innovative framework extending APNNs. RT-APNN integrates multiple neural networks into a cohesive architecture, reducing training time while ensuring high solution accuracy. Advanced techniques such as pre-training and Markov Chain Monte Carlo (MCMC) adaptive sampling are employed to tackle the complexities of long-term simulations and intricate boundary conditions. RT-APNN is the first deep learning method to successfully simulate the Marshak wave problem. Numerical experiments demonstrate its superiority over existing methods, including APNNs and MD-APNNs, in both accuracy and computational efficiency. Furthermore, RT-APNN excels at solving high-dimensional, nonlinear problems, underscoring its potential for diverse applications in science and engineering.

physics.comp-ph↗