arXiv subjects

Wenlong Tang

Publications and source records attributed to Wenlong Tang.

5 recordsLinked to original sources

arXiv2026

Tunable thermal conductivity through dual spin-phonon coupling in van der Waals ferromagnetic insulator Cr2Ge2Te6

The active manipulation of phonon transport remains a central challenge in phononics and spin caloritronics due to the charge-neutral nature of heat carriers. Spin-phonon coupling (SPC) offers a promising route for the dynamic control of heat carriers, yet its progress has been limited due to the lack of a unified framework and suitable material platforms. Here, we report on the magnetic field-tunable phonon transport behavior in the ferromagnetic insulator Cr2Ge2Te6. We observed two distinct anomalous regimes at both the high and low fields that were governed by isotropic magnon-phonon hybridization and an anisotropic magnon softening process, respectively. By integrating detailed transport behavior with Brillouin light scattering and ferromagnetic resonance, we uncovered the microscopic origins of these anomalous regimes and demonstrated that both the field magnitude and orientation could act as versatile tuning knobs to manipulate the thermal conductivity. Our findings provide experimental evidence of the SPC effect on phonon transport, demonstrating the dual impact of SPC within a unified system. This work will not only broaden the fundamental understanding of quasiparticle interactions but also establish a viable framework for dynamic phonon engineering. Furthermore, the characteristics of this system highlight the potential for achieving field-tunable phonon transport in similar platforms such as two-dimensional (2D) magnetic materials.

cond-mat.str-el↗

arXiv2026

Influence of Cross-sectional Expansion on Coronal Emissions from a Radiatively Cooling Solar Flare Loop

Loop-aligned hydrodynamic modelings help better understand the thermodynamic evolution of flaring plasma confined in solar flare loops. Conventional loop modelings typically assume a uniform loop cross section. With a variation of the cross section taken into account, in this work we carry out both analytical and numerical modelings of the radiative cooling in a solar flare loop. It is found that a cross-sectional expansion with height can efficiently suppress the draining of loop material from the corona while not significantly affecting the decrease of loop temperature. Reflected to the loop energetics, the coronal part of the loop cools more dominantly by radiation, and more importantly, the loop radiative outputs are shifted toward lower temperatures. These findings pose important physical implications for extreme-ultraviolet (EUV) late-phase emissions discovered in some solar flares. The late-phase loops in these flares are believed to bear a more notable cross-sectional expansion owing to their longer lengths. Compared with the main-phase loops, the late-phase loops would emit more effectively at middle temperatures, which could, to a certain degree, mitigate the severe heating requirement for the production of a prominent warm coronal late-phase peak. In addition, the cross-sectional expansion also affects the shape of the emission lights curves, causing a sharper decay after the emission peak. Such an emission pattern has been validated with the observations of an EUV late-phase flare, and could serve as a potential diagnostic tool to judge the degree of loop cross-sectional expansion in an extended flare dataset.

astro-ph.SR↗

arXiv2025

Learning Evolving Latent Strategies for Multi-Agent Language Systems without Model Fine-Tuning

This study proposes a multi-agent language framework that enables continual strategy evolution without fine-tuning the language model's parameters. The core idea is to liberate the latent vectors of abstract concepts from traditional static semantic representations, allowing them to be continuously updated through environmental interaction and reinforcement feedback. We construct a dual-loop architecture: the behavior loop adjusts action preferences based on environmental rewards, while the language loop updates the external latent vectors by reflecting on the semantic embeddings of generated text. Together, these mechanisms allow agents to develop stable and disentangled strategic styles over long-horizon multi-round interactions. Experiments show that agents' latent spaces exhibit clear convergence trajectories under reflection-driven updates, along with structured shifts at critical moments. Moreover, the system demonstrates an emergent ability to implicitly infer and continually adapt to emotional agents, even without shared rewards. These results indicate that, without modifying model parameters, an external latent space can provide language agents with a low-cost, scalable, and interpretable form of abstract strategic representation.

cs.LG↗

arXiv2023

Deep Learning Illuminates Spin and Lattice Interaction in Magnetic Materials

Atomistic simulations hold significant value in clarifying crucial phenomena such as phase transitions and energy transport in materials science. Their success stems from the presence of potential energy functions capable of accurately depicting the relationship between system energy and lattice changes. In magnetic materials, two atomic scale degrees of freedom come into play: the lattice and the spin. However, accurately tracing the simultaneous evolution of both lattice and spin in magnetic materials at an atomic scale is a substantial challenge. This is largely due to the complexity involved in depicting the interaction energy precisely, and its influence on lattice and spin-driving forces, such as atomic force and magnetic torque, which continues to be a daunting task in computational science. Addressing this deficit, we present DeepSPIN, a versatile approach that generates high-precision predictive models of energy, atomic forces, and magnetic torque in magnetic systems. This is achieved by integrating first-principles calculations of magnetic excited states with deep learning techniques via active learning. We thoroughly explore the methodology, accuracy, and scalability of our proposed model in this paper. Our technique adeptly connects first-principles computations and atomic-scale simulations of magnetic materials. This synergy presents opportunities to utilize these calculations in devising and tackling theoretical and practical obstacles concerning magnetic materials.

cond-mat.mtrl-sci↗

arXiv2022

Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE)

Extracting quantitative information about highly scattering surfaces from an imaging system is challenging because the phase of the scattered light undergoes multiple folds upon propagation, resulting in complex speckle patterns. One specific application is the drying of wet powders in the pharmaceutical industry, where quantifying the particle size distribution (PSD) is of particular interest. A non-invasive and real-time monitoring probe in the drying process is required, but there is no suitable candidate for this purpose. In this report, we develop a theoretical relationship from the PSD to the speckle image and describe a physics-enhanced autocorrelation-based estimator (PEACE) machine learning algorithm for speckle analysis to measure the PSD of a powder surface. This method solves both the forward and inverse problems together and enjoys increased interpretability, since the machine learning approximator is regularized by the physical law.

eess.IV↗