Transformer-Based Neural Surrogate for Link-Level Path Loss Prediction from Variable-Sized Maps
Qualcomm Technologies, Inc.（高通技术公司）；Qualcomm Technologies Netherlands B.V.（高通技术荷兰有限公司）；Qualcomm Wireless GmbH（高通无线有限公司）；Qualcomm France S.A.R.L.（高通法国有限责任公司）
Thomas M. Hehn, Tribhuvanesh Orekondy, Ori Shental, Arash Behboodi, Juan Bucheli, Akash Doshi, June Namgoong, Taesang Yoo, Ashwin Sampath, Joseph B. Soriaga
备注：
Accepted at IEEE GLOBECOM 2023, v2: Changed license on arxiv
Estimating path loss for a transmitter-receiver location is key to many use-cases including network planning and handover. Machine learning has become a popular tool to predict wireless channel properties based on map data. In this work, we present a transformer-based neural network architecture that enables predicting link-level properties from maps of various dimensions and from sparse measurements. The map contains information about buildings and foliage. The transformer model attends to the regions that are relevant for path loss prediction and, therefore, scales efficiently to maps of different size. Further, our approach works with continuous transmitter and receiver coordinates without relying on discretization. In experiments, we show that the proposed model is able to efficiently learn dominant path losses from sparse training data and generalizes well when tested on novel maps.
Interpretable A-posteriori Error Indication for Graph Neural Network Surrogate Models
Transportation and Power Systems Division, Argonne National Laboratory（阿贡国家实验室交通与动力系统分部）；College of Information Sciences and Technology, Pennsylvania State University（宾夕法尼亚州立大学信息科学与技术学院）；Mathematics and Computer Science Division, Argonne National Laboratory（阿贡国家实验室数学与计算机科学分部）
Self-composing neural operators for high-frequency and multiscale PDE surrogates
Yau Mathematical Sciences Center, Tsinghua University（清华大学姚期智数学科学中心）；Ocean University of China（中国海洋大学）；King Abdullah University of Science and Technology（国王 Abdullah 科学与技术大学）
Interpretable epistemic uncertainty decomposition in sequential generative models via polynomial chaos surrogates
Mathematical Institute, University of Oxford, United Kingdom（牛津大学数学研究所，英国）；IBM Research, Thomas J. Watson Research Center, USA（IBM研究院，托马斯·J·沃森研究中心，美国）；IBM Research Europe, Daresbury, United Kingdom（IBM欧洲研究院，达尔斯伯里，英国）；MIT–IBM Watson AI Lab, Massachusetts, USA（麻省理工–IBM沃森人工智能实验室，马萨诸塞州，美国）；Université de Montréal, Mila–Quebec AI Institute, Montreal, Quebec, Canada（蒙特利尔大学，魁北克人工智能研究所，蒙特利尔，魁北克，加拿大）
Neural Operator-Based Surrogate Model for CFD:Helical Coil Steam Generator in Small Modular Reactor
Department of Quantum System Engineering, Jeonbuk National University（全子系统工程系，全州国立大学）；Department of Nuclear Engineering, Hanyang University（核工程系，翰阳大学）；Graduate School of Integrated Energy-AI, Jeonbuk National University（整合能源-人工智能研究生院，全州国立大学）
