DeepRTE: Pre-trained Attention-based Neural Network for Radiative Tranfer

• URL: http://arxiv.org/abs/2505.23190v2
• Date: Tue, 03 Jun 2025 15:56:54 GMT
• Title: DeepRTE: Pre-trained Attention-based Neural Network for Radiative Tranfer
• Authors: Yekun Zhu, Min Tang, Zheng Ma,
• Abstract summary: We propose a novel neural network approach, termed DeepRTE, to address the steady-state Radiative Transfer Equation (RTE)<n>Our DeepRTE framework demonstrates superior computational efficiency for solving the steady-state RTE.<n>DeepRTE achieves high accuracy with significantly fewer parameters, largely due to its incorporation of mechanisms such as multi-head attention.
• Score: 3.5335723405425714
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a novel neural network approach, termed DeepRTE, to address the steady-state Radiative Transfer Equation (RTE). The RTE is a differential-integral equation that governs the propagation of radiation through a participating medium, with applications spanning diverse domains such as neutron transport, atmospheric radiative transfer, heat transfer, and optical imaging. Our DeepRTE framework demonstrates superior computational efficiency for solving the steady-state RTE, surpassing traditional methods and existing neural network approaches. This efficiency is achieved by embedding physical information through derivation of the RTE and mathematically-informed network architecture. Concurrently, DeepRTE achieves high accuracy with significantly fewer parameters, largely due to its incorporation of mechanisms such as multi-head attention. Furthermore, DeepRTE is a mesh-free neural operator framework with inherent zero-shot capability. This is achieved by incorporating Green's function theory and pre-training with delta-function inflow boundary conditions into both its architecture design and training data construction. The efficacy of the proposed approach is substantiated through comprehensive numerical experiments.

Related papers

• GeNeRT: A Physics-Informed Approach to Intelligent Wireless Channel Modeling via Generalizable Neural Ray Tracing [40.29573574383523]
  We propose GeNeRT, a Generalizable Neural RT framework with enhanced generalization, accuracy and efficiency.<n>By incorporating Fresnel-inspired neural network design, it also achieves higher accuracy in multipath component (MPC) prediction.<n>Experiments conducted in outdoor scenarios demonstrate that GeNeRT generalizes well across untrained regions within a scenario and entirely unseen environments.
  arXiv  Detail & Related papers  (2025-06-23T05:17:01Z)
• A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation [0.0]
  This communication explores a novel approach using deep neural networks to estimate the pattern propagation factor.<n>Deep neural networks can be trained to analyze multiple frequencies and reasonably predict the pattern propagation factor.
  arXiv  Detail & Related papers  (2025-05-21T17:56:02Z)
• RadioDiff-$k^2$: Helmholtz Equation Informed Generative Diffusion Model for Multi-Path Aware Radio Map Construction [69.96295462931168]
  We propose a physics-informed generative learning approach, termed RadioDiff-$bmk2$, for accurate and efficient multipath-aware radio map (RM) construction.<n>We establish a direct correspondence between EM singularities, which correspond to the critical spatial features influencing wireless propagation, and regions defined by negative wave numbers in the Helmholtz equation.
  arXiv  Detail & Related papers  (2025-04-22T06:28:13Z)
• Resource-Efficient Beam Prediction in mmWave Communications with Multimodal Realistic Simulation Framework [57.994965436344195]
  Beamforming is a key technology in millimeter-wave (mmWave) communications that improves signal transmission by optimizing directionality and intensity.<n> multimodal sensing-aided beam prediction has gained significant attention, using various sensing data to predict user locations or network conditions.<n>Despite its promising potential, the adoption of multimodal sensing-aided beam prediction is hindered by high computational complexity, high costs, and limited datasets.
  arXiv  Detail & Related papers  (2025-04-07T15:38:25Z)
• SpINR: Neural Volumetric Reconstruction for FMCW Radars [0.15193212081459279]
  We introduce SpINR, a novel framework for volumetric reconstruction using Frequency-Modulated Continuous-Wave (FMCW) radar data.<n>We demonstrate that SpINR significantly outperforms classical backprojection methods and existing learning-based approaches.
  arXiv  Detail & Related papers  (2025-03-30T04:44:57Z)
• EMWaveNet: Physically Explainable Neural Network Based on Electromagnetic Propagation for SAR Target Recognition [4.251056028888424]
  This study proposes a physically explainable framework for complex-valued SAR image recognition.<n>The network architecture is fully parameterized, with all learnable parameters endowed with clear physical meanings.<n>The proposed method possesses a strong physical decision logic, high physical explainability and robustness, as well as excellent de-aliasing capabilities.
  arXiv  Detail & Related papers  (2024-10-13T07:04:49Z)
• RadioDiff: An Effective Generative Diffusion Model for Sampling-Free Dynamic Radio Map Construction [42.596399621642234]
  Radio map (RM) is a promising technology that can obtain pathloss based on only location. In this paper, a sampling-free RM construction is modeled as a conditional generative problem, where a denoised diffusion-based method, named RadioDiff, is proposed to achieve high-quality RM construction. Experimental results show that the proposed RadioDiff achieves state-of-the-art performance in all three metrics of accuracy, structural similarity, and peak signal-to-noise ratio.
  arXiv  Detail & Related papers  (2024-08-16T08:02:00Z)
• Neuromorphic Split Computing with Wake-Up Radios: Architecture and Design via Digital Twinning [97.99077847606624]
  This work proposes a novel architecture that integrates a wake-up radio mechanism within a split computing system consisting of remote, wirelessly connected, NPUs. A key challenge in the design of a wake-up radio-based neuromorphic split computing system is the selection of thresholds for sensing, wake-up signal detection, and decision making.
  arXiv  Detail & Related papers  (2024-04-02T10:19:04Z)
• Fast and Accurate Cooperative Radio Map Estimation Enabled by GAN [63.90647197249949]
  In the 6G era, real-time radio resource monitoring and management are urged to support diverse wireless-empowered applications. In this paper, we present a cooperative radio map estimation approach enabled by the generative adversarial network (GAN)
  arXiv  Detail & Related papers  (2024-02-05T05:01:28Z)
• Lyapunov-Driven Deep Reinforcement Learning for Edge Inference Empowered by Reconfigurable Intelligent Surfaces [30.1512069754603]
  We propose a novel algorithm for energy-efficient, low-latency, accurate inference at the wireless edge. We consider a scenario where new data are continuously generated/collected by a set of devices and are handled through a dynamic queueing system.
  arXiv  Detail & Related papers  (2023-05-18T12:46:42Z)
• Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces [56.35676570414731]
  The emerging technology of Reconfigurable Intelligent Surfaces (RISs) is provisioned as an enabler of smart wireless environments. RISs offer a highly scalable, low-cost, hardware-efficient, and almost energy-neutral solution for dynamic control of the propagation of electromagnetic signals over the wireless medium. One of the major challenges with the envisioned dense deployment of RISs in such reconfigurable radio environments is the efficient configuration of multiple metasurfaces.
  arXiv  Detail & Related papers  (2022-05-08T06:21:33Z)
• Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Access [65.21117658030235]
  This work proposes a novel design for a practical RSMA receiver based on model-based deep learning (MBDL) methods. The MBDL receiver is evaluated in terms of uncoded Symbol Error Rate (SER), throughput performance through Link-Level Simulations (LLS) and average training overhead. Results reveal that the MBDL outperforms by a significant margin the SIC receiver with imperfect CSIR.
  arXiv  Detail & Related papers  (2022-05-02T12:23:55Z)
• Energy-Efficient Model Compression and Splitting for Collaborative Inference Over Time-Varying Channels [52.60092598312894]
  We propose a technique to reduce the total energy bill at the edge device by utilizing model compression and time-varying model split between the edge and remote nodes. Our proposed solution results in minimal energy consumption and $CO$ emission compared to the considered baselines.
  arXiv  Detail & Related papers  (2021-06-02T07:36:27Z)
• A SAR speckle filter based on Residual Convolutional Neural Networks [68.8204255655161]
  This work aims to present a novel method for filtering the speckle noise from Sentinel-1 data by applying Deep Learning (DL) algorithms, based on Convolutional Neural Networks (CNNs) The obtained results, if compared with the state of the art, show a clear improvement in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM)
  arXiv  Detail & Related papers  (2021-04-19T14:43:07Z)
• Physics Informed Neural Networks for Simulating Radiative Transfer [16.758334184623152]
  We propose a novel machine learning algorithm for simulating radiative transfer. Our algorithm is based on physics informed neural networks (PINNs), which are trained by minimizing the residual of the underlying radiative tranfer equations.
  arXiv  Detail & Related papers  (2020-09-25T16:07:02Z)
• Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning [21.770491711632832]
  The reconfigurable intelligent surface (RIS) has been speculated as one of the key enabling technologies for the future six generation (6G) wireless communication systems. In this paper, we investigate the joint design of transmit beamforming matrix at the base station and the phase shift matrix at the RIS, by leveraging recent advances in deep reinforcement learning (DRL) The proposed algorithm is not only able to learn from the environment and gradually improve its behavior, but also obtains the comparable performance compared with two state-of-the-art benchmarks.
  arXiv  Detail & Related papers  (2020-02-24T04:28:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.