arXiv subjects

Fan Meng

Publications and source records attributed to Fan Meng.

At least 19 recordsLinked to original sources

arXiv2026

Generation of vortex-squeezed light in a coherently prepared medium

In this Letter, we theoretically propose an alternative scheme for generating vortex optical squeezing, based on the Raman scattering process in a coherently prepared medium, distinct from approaches such as parametric down-conversion and four-wave mixing. Our analysis reveals that both the input control field and the generated signal field can exhibit squeezing upon adjusting the relevant system parameters, with the control field exhibiting a greater degree of squeezing under identical conditions. We further demonstrate the existence of optimal squeezing values over a range of tuning parameters, highlighting the flexibility and robustness of the proposed scheme. These findings may offer a new reference for continuous-variable vortex optical squeezing and possess potential applications in domains such as quantum information processing, quantum precision measurement, and quantum sensing.

quant-ph↗

arXiv2026

Propagation dynamics of high-gain vortex beams in symmetry-broken media via forward and backward three-wave mixing

In recent years, vortex light, a distinctive form of structured light, has attracted considerable attention owing to its unique properties and the rich physical phenomena arising from its interaction with matter. In this paper, we investigate the propagation dynamics of vortex beams in a symmetry-broken three-level system based on forward and backward three-wave mixing (TWM) processes. We find that both processes enable the transfer of high-gain vortex light, with the associated topological charges obeying identical algebraic relations. The forward process exhibits periodic oscillatory transmission and modulates the transverse spatial profile of the generated signal vortex field, whereas the backward process features stable transmission and produces a signal field with higher gain and improved fidelity. Under the Autler-Townes splitting (ATS) regime, the probe field detuning in both schemes has a negligible influence on the gain. Optical depth influences only the rate at which the gain approaches its maximum, while the peak value remains unchanged. These results constitute a meaningful extension of the work reported in reference [40] and may provide a feasible approach for quantum communication, quantum computation, and the generation of high-gain, high-fidelity vortex light.

quant-ph↗

arXiv2026

Channel Knowledge Map Reconstruction From Sparse Measurements via Pilot-Anchored Layout-Conditioned Fourier Refinement

Channel knowledge maps (CKMs) enable environment-aware wireless systems by providing location-specific channel knowledge, but long-term environmental variations, such as construction, traffic redistribution, and foliage changes, require periodic map refresh. In practice, channel measurements are often sparse and irregular, while environmental knowledge may be limited to coarse layout or topology descriptors. This paper studies CKM reconstruction from sparse measurements. We show that reconstruction pipelines that apply local aggregation or spectral operators directly to a zero-filled pilot grid can entangle the sampling mask with the channel field, allowing structural priors to act on mask-induced distortions before the measurements define a supported radio field. To address this issue, we propose Anchor-CKM, a measurement-first, knowledge-aided reconstruction framework. Anchor-CKM first uses support-aware partial convolutions to construct a pilot-supported representation, and then performs layout-conditioned dual-path Fourier refinement followed by coordinate-based heteroscedastic prediction of the CKM mean and per-location predictive variance. Experiments on transmitter-disjoint DeepMIMO scenarios cover missing ratios from 0.3 to 0.95, including stringent 5% to 10% pilot-coverage settings. In explicit-layout outdoor scenarios, Anchor-CKM reduces received-power root-mean-square error (RMSE) by 0.79 to 1.33 dB relative to the strongest reproduced baseline, while ablations identify pilot-support stabilization as the largest contributor and layout conditioning as beneficial for line-of-sight/non-line-of-sight (LOS/NLOS) boundary fidelity.

eess.SP↗

arXiv2026

TianJi-Environ: An Autonomous AI Scientist for Atmospheric Environmental Research

As atmospheric environmental prediction continues to improve, interpretable validation of pollution mechanisms and feedback processes has become a main challenge in atmospheric chemistry. Yet mechanism validation based on complex numerical models still relies heavily on expert knowledge: mechanistic hypotheses must be operationalized into executable experiments, and model outputs must be organized into traceable evidence. We present TianJi-Environ, an auditable AI Scientist for atmospheric-chemistry mechanism validation. TianJi-Environ establishes the first WRF-Chem-based multi-agent framework that autonomously drives complex atmospheric-chemistry simulations, converting mechanistic hypotheses into executable configurations, testing experiments, and evidence criteria. Using ozone response and particulate-matter feedback as two representative examples, we demonstrate TianJi-Environ's capability for mechanism validation. In a summertime ozone case over the North China Plain, the system detects directionally consistent aerosol-radiation-interaction signals in shortwave radiation and boundary-layer height, but judges the evidence for ozone response to NOx control to be incomplete. In a wintertime PM2.5 case over the Guanzhong Basin, it localizes the unsupported link to insufficient propagation from black-carbon perturbation to particulate response and missing diagnostics of vertical absorptive heating. These results show that TianJi-Environ makes expert-driven mechanism validation explicit, structured, and auditable, offering a reproducible paradigm for multi-agent systems coupled with complex atmospheric-chemistry models.

physics.ao-ph↗

arXiv2026

RieIF: Knowledge-Driven Riemannian Information Flow for Robust Spatio-Temporal Graph Signal Prediction in 6G Wireless Networks

With 6G evolving towards intelligent network autonomy, artificial intelligence (AI)-native operations are becoming pivotal. Wireless networks continuously generate rich and heterogeneous data, which inherently exhibits spatio-temporal graph structure. However, limited radio resources result in incomplete and noisy network measurements. This challenge is further intensified when a target variable and its strongest correlates are missing over contiguous intervals, forming systemic blind spots. To tackle this issue, we propose RieIF (Knowledge-driven Riemannian Information Flow), a geometry-consistent framework that incorporates knowledge graphs (KGs) for robust spatio-temporal graph signal prediction. For analytical tractability within the Fisher-Rao geometry, we project the input from a Riemannian manifold onto a positive unit hypersphere, where angular similarity is computationally efficient. This projection is implemented via a graph transformer, using the KG as a structural prior to constrain attention and generate a micro stream. Simultaneously, a Long Short-Term Memory (LSTM) model captures temporal dynamics to produce a macro stream. Finally, the micro stream (highlighting geometric shape) and the macro stream (emphasizing signal strength) are adaptively fused through a geometric gating mechanism for signal recovery. Experiments on three wireless datasets show consistent improvements under systemic blind spots, including up to 31% reduction in root mean squared error and up to 3.2 dB gain in recovery signal-to-noise ratio, while maintaining robustness to graph sparsity and measurement noise.

eess.SP↗

arXiv2026

TianJi:An autonomous AI meteorologist for discovering physical mechanisms in atmospheric science

Artificial intelligence (AI) has achieved breakthroughs comparable to traditional numerical models in data-driven weather forecasting, yet it remains essentially statistical fitting and struggles to uncover the physical causal mechanisms of the atmosphere. Physics-oriented mechanism research still heavily relies on domain knowledge and cumbersome engineering operations of human scientists, becoming a bottleneck restricting the efficiency of Earth system science exploration. Here, we propose TianJi - the first "AI meteorologist" system capable of autonomously driving complex numerical models to verify physical mechanisms. Powered by a large language model-driven multi-agent architecture, TianJi can autonomously conduct literature research and generate scientific hypotheses. We further decouple scientific research into cognitive planning and engineering execution: the meta-planner interprets hypotheses and devises experimental roadmaps, while a cohort of specialized worker agents collaboratively complete data preparation, model configuration, and multi-dimensional result analysis. In two classic atmospheric dynamic scenarios (squall-line cold pools and typhoon track deflections), TianJi accomplishes expert-level end-to-end experimental operations with zero human intervention, compressing the research cycle to a few hours. It also delivers detailed result analyses and autonomously judges and explains the validity of the hypotheses from outputs. TianJi reveals that the role of AI in Earth system science is transitioning from a "black-box predictor" to an "interpretable scientific collaborator", offering a new paradigm for high-throughput exploration of scientific mechanisms.

cs.AI↗

arXiv2026

Transfer of Orbital Angular Momentum in Vortex Light through Four-Wave Mixing and the Manipulation of Slow and Fast Light

Vortex light, a unique optical field that carries orbital angular momentum (OAM), has attracted considerable attention in recent years. In this paper, we present a detailed theoretical analysis of OAM transfer from the input field to the generated signal field in a four-level double-Lambda system via the four-wave mixing (FWM) process, showing that their OAMs follow a specific algebraic relationship. We identify the optimal conditions for efficient vortex light transmission, analyze the influence of detuning on transmission efficiency and phase distortion, and specifically examine the scenario where the control field carries OAM the latter being essential for a complete characterization of OAM conservation in the FWM process, while all three aspects have been largely overlooked in the existing literature. Furthermore, we investigated the tunability of the group velocity between the probe and signal fields by modulating the Rabi frequencies of the two control fields and the relative phase between the probe and signal fields during the FWM process. We demonstrate that the conversion between matched vortex slow and fast light can be realized an effect that has not been widely explored in dual-Lambda-type systems. These results may hold promise for applications in quantum information storage and processing, quantum computing, and ultrasensitive detection.

physics.optics↗

arXiv2025

Continuous variable entanglement with orbital angular momentum multiplexing in coherently prepared media

Quantum entanglement constitutes a pivotal resource, serving as a fundamental cornerstone within the field of quantum information science. In recent years, the study of vortex light entanglement has garnered widespread attention due to its unique structure and inherent advantages; however, the majority of these investigations are primarily focused on discrete variable (DV) systems. In this paper, we present a theoretical framework for generating vortex optical entanglement in coherently prepared media, employing continuous variable (CV) analysis and leveraging Raman scattering as an alternative to the conventional spontaneous parametric down-conversion (SPDC) method. The entanglement arises from the quantum correlation between the two light fields, induced by atomic coherence. Using numerical simulations, we thoroughly explore the impact of various tunable system parameters on the degree of entanglement, ultimately identifying the optimal conditions for maximal entanglement. Our findings offer a reference framework for vortex light entanglement, with potential implications across quantum teleportation, quantum key distribution, quantum computing, high-dimensional quantum information, and other related fields.

quant-ph↗

arXiv2025

Meteorologically-Informed Adaptive Conformal Prediction for Tropical Cyclone Intensity Forecasting

Rapid intensification (RI) of tropical cyclones (TCs) poses a great challenge due to their highly nonlinear dynamics and inherent uncertainties. Conventional statistical dynamics and artificial intelligence prediction models typically rely on static parameterization schemes, which limits their ability to capture the non-stationary error structure in the intensity evolution. To address this issue, we propose a physically-inspired covariate adaptive conformal prediction framework that dynamically adjusts uncertainty quantification by incorporating process information such as intensity and evolutionary stage. Our approach not only surpasses state-of-the-art models in point prediction accuracy, but also delivers physically consistent and interpretable forecast intervals, establishing a more process-aware framework for probabilistic prediction of extreme weather events.

physics.ao-ph↗

arXiv2025

Physics-Informed Residual Neural Ordinary Differential Equations for Enhanced Tropical Cyclone Intensity Forecasting

Accurate tropical cyclone (TC) intensity prediction is crucial for mitigating storm hazards, yet its complex dynamics pose challenges to traditional methods. Here, we introduce a Physics-Informed Residual Neural Ordinary Differential Equation (PIR-NODE) model to precisely forecast TC intensity evolution. This model leverages the powerful non-linear fitting capabilities of deep learning, integrates residual connections to enhance model depth and training stability, and explicitly models the continuous temporal evolution of TC intensity using Neural ODEs. Experimental results in the SHIPS dataset demonstrate that the PIR-NODE model achieves a significant improvement in 24-hour intensity prediction accuracy compared to traditional statistical models and benchmark deep learning methods, with a 25. 2\% reduction in the root mean square error (RMSE) and a 19.5\% increase in R-square (R2) relative to a baseline of neural network. Crucially, the residual structure effectively preserves initial state information, and the model exhibits robust generalization capabilities. This study details the PIR-NODE model architecture, physics-informed integration strategies, and comprehensive experimental validation, revealing the substantial potential of deep learning techniques in predicting complex geophysical systems and laying the foundation for future refined TC forecasting research.

physics.ao-ph↗

arXiv2024

Joint Optimization of Data- and Model-Driven Probing Beams and Beam Predictor

Hierarchical search in millimeter-wave (mmWave) communications incurs significant beam training overhead and delay, especially in a dynamic environment. Deep learning-enabled beam prediction is promising to significantly mitigate the overhead and delay, efficiently utilizing the site-specific channel prior. In this work, we propose to jointly optimize a data- and model-driven probe beam module and a cascaded data-driven beam predictor, with limitations in that the probe and communicate beams are restricted within the manifold space of uniform planer array and quantization of the phase modulator. First, The probe beam module senses the mmWave channel with a complex-valued neural network and outputs the counterpart RSRPs of probe beams. Second, the beam predictor estimates the RSRPs in the entire beamspace to minimize the prediction cross entropy and selects the optimal beam with the maximum RSRP value for data transmission. Additionally, we propose to add noise to the phase variables in the probe beam module, against quantization error. Simulation results show the effectiveness of our proposed scheme.

cs.IT↗

arXiv2024

Investigating the Molecular Design Mechanism Behind the Hydrophobicity of Biological Surface Nanostructures: Insights from Butterfly and Mosquito Systems

Wettability is a fundamental physicochemical property of solid surfaces, with unique wettability patterns playing pivotal roles across diverse domains. Inspired by nature's ingenious designs, bio-inspired materials have emerged as a frontier of scientific inquiry. They showcase remarkable hydrophobic properties observed in phenomena such as mosquitoes preventing fog condensation, and lotus leaves exhibiting self-cleaning attributes. This groundbreaking research delves into the hydrophobic characteristics of biomimetic surfaces using coarse-grained molecular simulation and the free energy barrier evaluation system. By analyzing the butterfly wings and mosquito eyes model, we aim to pioneer a comprehensive framework that factors in the influence of surface parameters on the free energy barrier. Through meticulous simulation and analysis, we strive to validate and enhance the reliability of the free energy barrier assessment method, deepening our understanding of hydrophobicity across diverse biomaterials and paving the way for optimizing their properties for a myriad of applications. During our investigation, we shed light on the elusive intermediate state, a departure from the typical Cassie or Wenzel state, enriching our theoretical framework for surfaces with distinctive properties. This research is a catalyst for developing biomimetic materials with superior hydrophobic characteristics and innovative fabrication processes, transcending academic boundaries and promising significant strides in environmental conservation, medicine, and beyond, offering hope for a greener, healthier, and more sustainable future.

physics.bio-ph↗

arXiv2024

Entropy-based Probing Beam Selection and Beam Prediction via Deep Learning

Hierarchical beam search in mmWave communications incurs substantial training overhead, necessitating deep learning-enabled beam predictions to effectively leverage channel priors and mitigate this overhead. In this study, we introduce a comprehensive probabilistic model of power distribution in beamspace, and formulate the joint optimization problem of probing beam selection and probabilistic beam prediction as an entropy minimization problem. Then, we propose a greedy scheme to iteratively and alternately solve this problem, where a transformer-based beam predictor is trained to estimate the conditional power distribution based on the probing beams and user location within each iteration, and the trained predictor selects an unmeasured beam that minimizes the entropy of remaining beams. To further reduce the number of interactions and the computational complexity of the iterative scheme, we propose a two-stage probing beam selection scheme. Firstly, probing beams are selected from a location-specific codebook designed by an entropy-based criterion, and predictions are made with corresponding feedback. Secondly, the optimal beam is identified using additional probing beams with the highest predicted power values. Simulation results demonstrate the superiority of the proposed schemes compared to hierarchical beam search and beam prediction with uniform probing beams.

cs.IT↗

arXiv2023

GenoCraft: A Comprehensive, User-Friendly Web-Based Platform for High-Throughput Omics Data Analysis and Visualization

The surge in high-throughput omics data has reshaped the landscape of biological research, underlining the need for powerful, user-friendly data analysis and interpretation tools. This paper presents GenoCraft, a web-based comprehensive software solution designed to handle the entire pipeline of omics data processing. GenoCraft offers a unified platform featuring advanced bioinformatics tools, covering all aspects of omics data analysis. It encompasses a range of functionalities, such as normalization, quality control, differential analysis, network analysis, pathway analysis, and diverse visualization techniques. This software makes state-of-the-art omics data analysis more accessible to a wider range of users. With GenoCraft, researchers and data scientists have access to an array of cutting-edge bioinformatics tools under a user-friendly interface, making it a valuable resource for managing and analyzing large-scale omics data. The API with an interactive web interface is publicly available at https://genocraft.stanford. edu/. We also release all the codes in https://github.com/futianfan/GenoCraft.

q-bio.GN↗

arXiv2023

Traffic-Aware Hierarchical Beam Selection for Cell-Free Massive MIMO

Beam selection for joint transmission in cell-free massive multi-input multi-output systems faces the problem of extremely high training overhead and computational complexity. The traffic-aware quality of service additionally complicates the beam selection problem. To address this issue, we propose a traffic-aware hierarchical beam selection scheme performed in a dual timescale. In the long-timescale, the central processing unit collects wide beam responses from base stations (BSs) to predict the power profile in the narrow beam space with a convolutional neural network, based on which the cascaded multiple-BS beam space is carefully pruned. In the short-timescale, we introduce a centralized reinforcement learning (RL) algorithm to maximize the satisfaction rate of delay w.r.t. beam selection within multiple consecutive time slots. Moreover, we put forward three scalable distributed algorithms including hierarchical distributed Lyapunov optimization, fully distributed RL, and centralized training with decentralized execution of RL to achieve better scalability and better tradeoff between the performance and the execution signal overhead. Numerical results demonstrate that the proposed schemes significantly reduce both model training cost and beam training overhead and are easier to meet the user-specific delay requirement, compared to existing methods.

cs.IT↗

arXiv2022

Learnable Model-Driven Performance Prediction and Optimization for Imperfect MIMO System: Framework and Application

State-of-the-art schemes for performance analysis and optimization of multiple-input multiple-output systems generally experience degradation or even become invalid in dynamic complex scenarios with unknown interference and channel state information (CSI) uncertainty. To adapt to the challenging settings and better accomplish these network auto-tuning tasks, we propose a generic learnable model-driven framework in this paper. To explain how the proposed framework works, we consider regularized zero-forcing precoding as a usage instance and design a light-weight neural network for refined prediction of sum rate and detection error based on coarse model-driven approximations. Then, we estimate the CSI uncertainty on the learned predictor in an iterative manner and, on this basis, optimize the transmit regularization term and subsequent receive power scaling factors. A deep unfolded projected gradient descent based algorithm is proposed for power scaling, which achieves favorable trade-off between convergence rate and robustness.

cs.IT↗

arXiv2022

Learning-Aided Beam Prediction in mmWave MU-MIMO Systems for High-Speed Railway

The problem of beam alignment and tracking in high mobility scenarios such as high-speed railway (HSR) becomes extremely challenging, since large overhead cost and significant time delay are introduced for fast time-varying channel estimation. To tackle this challenge, we propose a learning-aided beam prediction scheme for HSR networks, which predicts the beam directions and the channel amplitudes within a period of future time with fine time granularity, using a group of observations. Concretely, we transform the problem of high-dimensional beam prediction into a two-stage task, i.e., a low-dimensional parameter estimation and a cascaded hybrid beamforming operation. In the first stage, the location and speed of a certain terminal are estimated by maximum likelihood criterion, and a data-driven data fusion module is designed to improve the final estimation accuracy and robustness. Then, the probable future beam directions and channel amplitudes are predicted, based on the HSR scenario priors including deterministic trajectory, motion model, and channel model. Furthermore, we incorporate a learnable non-linear mapping module into the overall beam prediction to allow non-linear tracks. Both of the proposed learnable modules are model-based and have a good interpretability. Compared to the existing beam management scheme, the proposed beam prediction has (near) zero overhead cost and time delay. Simulation results verify the effectiveness of the proposed scheme.

cs.IT↗

arXiv2022

TCR-GAN: Predicting tropical cyclone passive microwave rainfall using infrared imagery via generative adversarial networks

Tropical cyclones (TC) generally carry large amounts of water vapor and can cause large-scale extreme rainfall. Passive microwave rainfall (PMR) estimation of TC with high spatial and temporal resolution is crucial for disaster warning of TC, but remains a challenging problem due to the low temporal resolution of microwave sensors. This study attempts to solve this problem by directly forecasting PMR from satellite infrared (IR) images of TC. We develop a generative adversarial network (GAN) to convert IR images into PMR, and establish the mapping relationship between TC cloud-top bright temperature and PMR, the algorithm is named TCR-GAN. Meanwhile, a new dataset that is available as a benchmark, Dataset of Tropical Cyclone IR-to-Rainfall Prediction (TCIRRP) was established, which is expected to advance the development of artificial intelligence in this direction. Experimental results show that the algorithm can effectively extract key features from IR. The end-to-end deep learning approach shows potential as a technique that can be applied globally and provides a new perspective tropical cyclone precipitation prediction via satellite, which is expected to provide important insights for real-time visualization of TC rainfall globally in operations.

cs.LG↗