{"id":136459,"date":"2013-07-07T05:00:06","date_gmt":"2013-07-07T04:00:06","guid":{"rendered":"http:\/\/www.madrimasd.org\/blogs\/matematicas\/?p=136459"},"modified":"2013-07-05T18:41:49","modified_gmt":"2013-07-05T17:41:49","slug":"mas-alla-del-alcance-matematico-metodos-numericos-para-aproximar-ecuaciones-diferenciales","status":"publish","type":"post","link":"https:\/\/www.madrimasd.org\/blogs\/matematicas\/2013\/07\/07\/136459","title":{"rendered":"M\u00e1s all\u00e1 del alcance matem\u00e1tico: m\u00e9todos num\u00e9ricos para aproximar ecuaciones diferenciales"},"content":{"rendered":"<p><strong>La pr\u00f3xima semana, del 8 al 12 de julio, se celebrar\u00e1 en el ICMAT el Workshop Topics in Numerical Analysis for Differential Equations, en el que se tratar\u00e1n diversos temas relacionados con el an\u00e1lisis num\u00e9rico aplicado a ecuaciones diferenciales. Asistir\u00e1n grandes expertos de este amplio campo de las matem\u00e1ticas, como Jes\u00fas Sanz-Serna (Universidad de Valladolid), Arieh Iserles (Cambridge, Reino Unido), David Mart\u00edn de Diego (ICMAT-CSIC, Madrid), y Hans Munthe-Kaas (Bergen, Noruega). Kurusch Ebrahimi-Fard, investigador Ram\u00f3n y Cajal en el ICMAT y coorganizador del encuentro, lo presenta a continuaci\u00f3n.<\/strong><\/p>\n<p><a href=\"https:\/\/www.madrimasd.org\/blogs\/matematicas\/files\/2013\/07\/ICMAT_22_b.jpg\"><img decoding=\"async\" class=\"aligncenter size-medium wp-image-136462\" title=\"ICMAT_22_b\" src=\"https:\/\/www.madrimasd.org\/blogs\/matematicas\/files\/2013\/07\/ICMAT_22_b-300x125.jpg\" alt=\"\" width=\"300\" height=\"125\" srcset=\"https:\/\/www.madrimasd.org\/blogs\/matematicas\/files\/2013\/07\/ICMAT_22_b-300x125.jpg 300w, https:\/\/www.madrimasd.org\/blogs\/matematicas\/files\/2013\/07\/ICMAT_22_b-1024x428.jpg 1024w, https:\/\/www.madrimasd.org\/blogs\/matematicas\/files\/2013\/07\/ICMAT_22_b.jpg 1291w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/p>\n<p>La Matem\u00e1tica Aplicada tiene importantes conexiones con otras \u00e1reas de las ciencias matem\u00e1ticas, desde las m\u00e1s obvias como, por ejemplo, la cin\u00e9tica de las reacciones qu\u00edmicas, la din\u00e1mica molecular, los circuitos electr\u00f3nicos, la din\u00e1mica de poblaciones, la ingenier\u00eda y la teor\u00eda de control, as\u00ed como la f\u00edsica cl\u00e1sica y la cu\u00e1ntica, a temas m\u00e1s modernos, como la rob\u00f3tica, los sistemas multi-agente, la biolog\u00eda computacional, las finanzas y la din\u00e1mica de fluidos. El objetivo general de esta disciplina es el desarrollo de nuevos modelos matem\u00e1ticos y m\u00e9todos de utilidad para todas las ciencias aplicadas.<\/p>\n<p>Muchos de los matem\u00e1ticos m\u00e1s destacados en la historia de la ciencia, como Gauss, Newton, Leibniz, Euler y Lagrange, hicieron algunas de sus m\u00e1s grandes contribuciones a las matem\u00e1ticas en el contexto de problemas aplicados. Esto sirve para ilustrar la complementariedad de las matem\u00e1ticas puras y aplicadas. De hecho tanto los avances fundamentales en matem\u00e1ticas puras como las aplicaciones del razonamiento matem\u00e1tico innovador en otras \u00e1reas de la ciencias se benefician mutuamente.<\/p>\n<p>Las ecuaciones diferenciales juegan un papel destacado en las matem\u00e1ticas aplicadas, y son omnipresentes en las ciencias matem\u00e1ticas: aparecen en muchas facetas en diferentes campos. Sin embargo, ya los grandes matem\u00e1ticos anteriormente mencionados se dieron cuenta de que la b\u00fasqueda de soluciones cerradas de ecuaciones diferenciales es o bien simplemente imposible, o por lo menos, hoy en d\u00eda, est\u00e1 m\u00e1s all\u00e1 del alcance matem\u00e1tico. Por otra parte, resulta que las soluciones conocidas a menudo son muy dif\u00edciles de calcular, incluso con la potencia de los ordenadores actuales. Por lo tanto, la t\u00e9cnica de c\u00e1lculo de aproximaci\u00f3n de soluciones de las ecuaciones diferenciales parciales, en los casos ordinario, lineal o no lineal, es de particular importancia, y ser\u00e1 a lo que se dedicar\u00e1 el Workshop \u201cTopics in Numerical Analysis for Differential Equations\u201d.<\/p>\n<figure id=\"attachment_136460\" aria-describedby=\"caption-attachment-136460\" style=\"width: 267px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/www.madrimasd.org\/blogs\/matematicas\/files\/2013\/07\/BP_3_Lagrange.jpg\"><img decoding=\"async\" class=\"size-medium wp-image-136460\" title=\"B&amp;P_3_Lagrange\" src=\"https:\/\/www.madrimasd.org\/blogs\/matematicas\/files\/2013\/07\/BP_3_Lagrange-267x300.jpg\" alt=\"\" width=\"267\" height=\"300\" srcset=\"https:\/\/www.madrimasd.org\/blogs\/matematicas\/files\/2013\/07\/BP_3_Lagrange-267x300.jpg 267w, https:\/\/www.madrimasd.org\/blogs\/matematicas\/files\/2013\/07\/BP_3_Lagrange.jpg 357w\" sizes=\"(max-width: 267px) 100vw, 267px\" \/><\/a><figcaption id=\"caption-attachment-136460\" class=\"wp-caption-text\">Joseph Louis Lagrange<\/figcaption><\/figure>\n<p>Este workshop de an\u00e1lisis num\u00e9rico de ecuaciones diferenciales, que se celebra la pr\u00f3xima semana en ICMAT, tiene como objetivo reunir a los principales expertos mundiales que trabajan en la teor\u00eda de ecuaciones diferenciales y \u00e1reas relacionadas. El encuentro se compone de cinco sesiones, cada una de los cuales se organiza en torno a un tema espec\u00edfico, de especial importancia en la investigaci\u00f3n actual. La lista de temas incluye los campos de integraci\u00f3n num\u00e9rica geom\u00e9trica, los problemas altamente oscilatorios, los m\u00e9todos splitting e integraci\u00f3n temporal de ecuaciones en derivadas parciales, la mec\u00e1nica y teor\u00eda de control discreta, y las estructuras algebraicas en la integraci\u00f3n num\u00e9rica.<\/p>\n<figure style=\"width: 301px\" class=\"wp-caption aligncenter\"><img decoding=\"async\" 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\" alt=\"\" width=\"301\" height=\"437\" \/><figcaption class=\"wp-caption-text\">Arieh Iserles<\/figcaption><\/figure>\n<p style=\"text-align: justify;\">Cada sesi\u00f3n empieza con la conferencia plenaria de un destacado experto en estos campos: Jes\u00fas Sanz-Serna (Universidad de Valladolid), Arieh Iserles (Cambridge, Reino Unido), David Mart\u00edn de Diego (ICMAT-CSIC, Madrid), y Hans Munthe-Kaas (Bergen, Noruega). Adem\u00e1s del discurso plenario, cada sesi\u00f3n incluye tres o cuatro charlas de investigaci\u00f3n, donde se mostrar\u00e1n los \u00faltimos avances en el campo y terminar\u00e1 con una mesa redonda, de especial importancia, ya que se ofrecer\u00e1 una plataforma a los investigadores de todos los temas para discutir problemas comunes, plantear preguntas abiertas actuales, as\u00ed como las perspectivas de futuro de cada campo. La reuni\u00f3n incluye tambi\u00e9n una Sesi\u00f3n de j\u00f3venes investigadores, as\u00ed como una sesi\u00f3n de posters, donde los cient\u00edficos tendr\u00e1n la oportunidad de presentar sus trabajos.<\/p>\n<p><strong>M\u00e1s informaci\u00f3n sobre el Workshop:<\/strong><\/p>\n<p>http:\/\/www.icmat.es\/congresos\/tnade2013\/TNADE2013.ht<\/p>\n<p>&#8212;<\/p>\n<pre><strong>Kurusch Ebrahimi-Fard<\/strong> es investigador Ram\u00f3n y Cajal en el <strong>ICMAT<\/strong>.<\/pre>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>La pr\u00f3xima semana, del 8 al 12 de julio, se celebrar\u00e1 en el ICMAT el Workshop Topics in Numerical Analysis for Differential Equations, en el que se tratar\u00e1n diversos temas relacionados con el an\u00e1lisis num\u00e9rico aplicado a ecuaciones diferenciales. Asistir\u00e1n grandes expertos de este amplio campo de las matem\u00e1ticas, como Jes\u00fas Sanz-Serna (Universidad de Valladolid), Arieh Iserles (Cambridge, Reino Unido), David Mart\u00edn de Diego (ICMAT-CSIC, Madrid), y Hans Munthe-Kaas (Bergen, Noruega). Kurusch Ebrahimi-Fard, investigador Ram\u00f3n y Cajal en el ICMAT y coorganizador del encuentro, lo presenta a continuaci\u00f3n. La Matem\u00e1tica Aplicada tiene importantes conexiones con otras \u00e1reas de las ciencias\u2026<\/p>\n","protected":false},"author":49,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"ngg_post_thumbnail":0},"categories":[1],"tags":[],"blocksy_meta":{"styles_descriptor":{"styles":{"desktop":"","tablet":"","mobile":""},"google_fonts":[],"version":4}},"aioseo_notices":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v18.0 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>M\u00e1s all\u00e1 del alcance matem\u00e1tico: m\u00e9todos num\u00e9ricos para aproximar ecuaciones diferenciales - Matem\u00e1ticas y sus fronteras<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.madrimasd.org\/blogs\/matematicas\/2013\/07\/07\/136459\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"M\u00e1s all\u00e1 del alcance matem\u00e1tico: m\u00e9todos num\u00e9ricos para aproximar ecuaciones diferenciales - Matem\u00e1ticas y sus fronteras\" \/>\n<meta property=\"og:description\" content=\"La pr\u00f3xima semana, del 8 al 12 de julio, se celebrar\u00e1 en el ICMAT el Workshop Topics in Numerical Analysis for Differential Equations, en el que se tratar\u00e1n diversos temas relacionados con el an\u00e1lisis num\u00e9rico aplicado a ecuaciones diferenciales. 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