{"title":"Modelling optical pulse propagation in nonlinear media using wavelets","authors":"I. Pierce, L. Watkins","doi":"10.1109/TFSA.1996.547488","DOIUrl":null,"url":null,"abstract":"A wavelet based model for propagation of optical pulses in nonlinear media is presented. We obtain an O(N) algorithm for linear propagation by replacing the wavelet-domain propagation operator by its Taylor series approximation. Nonlinear propagation is then achieved by adding the nonlinear term in mid-step in a method analogous to the split-step Fourier method. Using wavelets offers the advantage of O(N) computational complexity compared with O(N log N) for fast Fourier transform methods. Using a wavelet basis also leads naturally to the time-resolved spectrum of the signal. Another advantage is that the local properties of wavelets will allow locally adaptive algorithms to be implemented.","PeriodicalId":415923,"journal":{"name":"Proceedings of Third International Symposium on Time-Frequency and Time-Scale Analysis (TFTS-96)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1996-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of Third International Symposium on Time-Frequency and Time-Scale Analysis (TFTS-96)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TFSA.1996.547488","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
A wavelet based model for propagation of optical pulses in nonlinear media is presented. We obtain an O(N) algorithm for linear propagation by replacing the wavelet-domain propagation operator by its Taylor series approximation. Nonlinear propagation is then achieved by adding the nonlinear term in mid-step in a method analogous to the split-step Fourier method. Using wavelets offers the advantage of O(N) computational complexity compared with O(N log N) for fast Fourier transform methods. Using a wavelet basis also leads naturally to the time-resolved spectrum of the signal. Another advantage is that the local properties of wavelets will allow locally adaptive algorithms to be implemented.