{"title":"蒙特卡罗模拟方法在偏振信息处理中的应用","authors":"Haojie Ding, Xiaopeng Gao, Renbin Zhang, Zhongyi Guo","doi":"10.1002/adts.202500513","DOIUrl":null,"url":null,"abstract":"Monte Carlo (MC) simulation method, a sampling‐based approach used for quantification and propagation of uncertainties, is widely used in scattering simulation of light polarization in scattering media systems. In this review, first, the MC principles and polarization information are briefly introduced. Subsequently, the role of the MC method is introduced and analyzed thoroughly in the process of polarization information processing (PIP) under homogeneous dispersion system, inhomogeneous dispersion system and microsurface system respectively, which includes simulating light transmission, studying light polarization, exploring scattering mechanisms, verifying experimental model and assisting interpretation of experimental results. In addition, this review analyzes the shortcomings of the current MC simulation methods and provides suggestions for the development of cost‐effective MC systems. In summary, this work provides a comprehensive review of the research progress of the MC method in the PIP. It highlights the strong potential applications of the MC method for the polarization detection and polarization imaging in complex environments, which gives valuable guidance for developments of future technology.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"13 1","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Monte Carlo Simulation Method for Applications of Polarization Information Processing\",\"authors\":\"Haojie Ding, Xiaopeng Gao, Renbin Zhang, Zhongyi Guo\",\"doi\":\"10.1002/adts.202500513\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Monte Carlo (MC) simulation method, a sampling‐based approach used for quantification and propagation of uncertainties, is widely used in scattering simulation of light polarization in scattering media systems. In this review, first, the MC principles and polarization information are briefly introduced. Subsequently, the role of the MC method is introduced and analyzed thoroughly in the process of polarization information processing (PIP) under homogeneous dispersion system, inhomogeneous dispersion system and microsurface system respectively, which includes simulating light transmission, studying light polarization, exploring scattering mechanisms, verifying experimental model and assisting interpretation of experimental results. In addition, this review analyzes the shortcomings of the current MC simulation methods and provides suggestions for the development of cost‐effective MC systems. In summary, this work provides a comprehensive review of the research progress of the MC method in the PIP. It highlights the strong potential applications of the MC method for the polarization detection and polarization imaging in complex environments, which gives valuable guidance for developments of future technology.\",\"PeriodicalId\":7219,\"journal\":{\"name\":\"Advanced Theory and Simulations\",\"volume\":\"13 1\",\"pages\":\"\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2025-07-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advanced Theory and Simulations\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1002/adts.202500513\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced Theory and Simulations","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1002/adts.202500513","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
Monte Carlo Simulation Method for Applications of Polarization Information Processing
Monte Carlo (MC) simulation method, a sampling‐based approach used for quantification and propagation of uncertainties, is widely used in scattering simulation of light polarization in scattering media systems. In this review, first, the MC principles and polarization information are briefly introduced. Subsequently, the role of the MC method is introduced and analyzed thoroughly in the process of polarization information processing (PIP) under homogeneous dispersion system, inhomogeneous dispersion system and microsurface system respectively, which includes simulating light transmission, studying light polarization, exploring scattering mechanisms, verifying experimental model and assisting interpretation of experimental results. In addition, this review analyzes the shortcomings of the current MC simulation methods and provides suggestions for the development of cost‐effective MC systems. In summary, this work provides a comprehensive review of the research progress of the MC method in the PIP. It highlights the strong potential applications of the MC method for the polarization detection and polarization imaging in complex environments, which gives valuable guidance for developments of future technology.
期刊介绍:
Advanced Theory and Simulations is an interdisciplinary, international, English-language journal that publishes high-quality scientific results focusing on the development and application of theoretical methods, modeling and simulation approaches in all natural science and medicine areas, including:
materials, chemistry, condensed matter physics
engineering, energy
life science, biology, medicine
atmospheric/environmental science, climate science
planetary science, astronomy, cosmology
method development, numerical methods, statistics