{"title":"随机非线性微分方程。我","authors":"O.J. Heilmann, N.G. Van Kampen","doi":"10.1016/0031-8914(74)90261-4","DOIUrl":null,"url":null,"abstract":"<div><p>A solution method is developed for nonlinear differential equations having the following two properties. Their coefficients are stochastic through their dependence on a Markov process. The magnitude of the fluctuations, multiplied with their auto-correlation time, is a small quantity. Under these conditions, the solution is also approximately a Markov process. Its probability distribution obeys a master equation, whose kernel is found as an expansion in that small quantity. The general formula is derived. Applications will be given in the second part of this work.</p></div>","PeriodicalId":55605,"journal":{"name":"Physica","volume":"77 2","pages":"Pages 279-289"},"PeriodicalIF":0.0000,"publicationDate":"1974-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/0031-8914(74)90261-4","citationCount":"5","resultStr":"{\"title\":\"Stochastic nonlinear differential equations. I\",\"authors\":\"O.J. Heilmann, N.G. Van Kampen\",\"doi\":\"10.1016/0031-8914(74)90261-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>A solution method is developed for nonlinear differential equations having the following two properties. Their coefficients are stochastic through their dependence on a Markov process. The magnitude of the fluctuations, multiplied with their auto-correlation time, is a small quantity. Under these conditions, the solution is also approximately a Markov process. Its probability distribution obeys a master equation, whose kernel is found as an expansion in that small quantity. The general formula is derived. Applications will be given in the second part of this work.</p></div>\",\"PeriodicalId\":55605,\"journal\":{\"name\":\"Physica\",\"volume\":\"77 2\",\"pages\":\"Pages 279-289\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1974-10-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1016/0031-8914(74)90261-4\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Physica\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/0031891474902614\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physica","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/0031891474902614","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A solution method is developed for nonlinear differential equations having the following two properties. Their coefficients are stochastic through their dependence on a Markov process. The magnitude of the fluctuations, multiplied with their auto-correlation time, is a small quantity. Under these conditions, the solution is also approximately a Markov process. Its probability distribution obeys a master equation, whose kernel is found as an expansion in that small quantity. The general formula is derived. Applications will be given in the second part of this work.