{"id":495,"date":"2024-11-09T20:16:25","date_gmt":"2024-11-09T23:16:25","guid":{"rendered":"https:\/\/eventos.cmm.uchile.cl\/enim2024\/?page_id=495"},"modified":"2024-12-04T12:42:22","modified_gmt":"2024-12-04T15:42:22","slug":"tesistas","status":"publish","type":"page","link":"https:\/\/eventos.cmm.uchile.cl\/enim2024\/tesistas\/","title":{"rendered":"Tesistas"},"content":{"rendered":"
<\/div>\n

\u00c1lvaro M\u00e1rquez: <\/strong>Generaci\u00f3n de modelos de atm\u00f3sferas estelares mediante PINNs<\/em><\/h2>\n
Universidad de Chile, Departamento de Ingenier\u00eda Matem\u00e1tica.<\/h6>\n

En el \u00e1rea de astronom\u00eda existe lo que se conoce como la espectroscopia estelar, que corresponde al estudio del espectro procedente de las estrellas captado mediante diversos instrumentos como ser\u00edan los telescopios usuales o las antenas usadas por ALMA. Esta se divide en dos \u00e1reas de trabajo, por un lado, est\u00e1 el estudio del espectro como tal y por el otro, se tiene la generaci\u00f3n de espectros sint\u00e9ticos, generados a partir de modelos num\u00e9ricos de atm\u00f3sferas, los cuales ser\u00e1n usados como material de estudio. Estos modelos son pocos comparados al n\u00famero de combinaciones posibles de par\u00e1metros que definen a dichas atm\u00f3sferas debido al costo computacional que implica generar uno, llevando a necesitar hacer uso de la interpolaci\u00f3n de modelos para conseguir nuevos modelos de una manera r\u00e1pida, m\u00e9todo que resulta en un modelo que no es f\u00edsicamente consistente. Debido a esto nace el proyecto que consiste en generar una red neuronal informada por f\u00edsica (PINN) que sea capaz de generar modelos num\u00e9ricos de atm\u00f3sferas estelares con la combinaci\u00f3n de par\u00e1metros que se desee.<\/p>\n

Volver arriba<\/a><\/div>\n

Mat\u00edas Ortiz Angel: <\/strong>C\u00f3mo escoger al amor de tu vida con Optimizaci\u00f3n en L\u00ednea<\/em><\/h2>\n
Universidad de Chile, Departamento de Ingenier\u00eda Matem\u00e1tica.<\/h6>\n

Suponga que usted administra una empresa y necesita contratar de forma urgente un secretario o secretaria. Para ello, publica una oferta de trabajo a la que n personas se muestran interesadas, y las cita el mismo d\u00eda para una entrevista. Estas llegan en un orden aleatorio y las entrevistas se realizan una a una. La decisi\u00f3n que tome afectar\u00e1 significativamente a la empresa, por lo que usted busca solamente lo mejor. Sin embargo, la situaci\u00f3n del pa\u00eds es tal que la mayor\u00eda de las empresas est\u00e1n en la misma condici\u00f3n, por lo que si usted no le da una respuesta inmediatamente a alg\u00fan candidato, otra empresa seguramente lo contratar\u00e1, imposibilitando que usted pueda contar con sus servicios. Por otro lado, el tiempo apremia y, en cuanto encuentre a alg\u00fan candidato que considere pueda ser el mejor, debe contratarlo, finalizando as\u00ed la b\u00fasqueda.En otras palabras la decisi\u00f3n debe tomarla al momento en que entrevista a un candidato y esta es irrevocable. \u00bfDe qu\u00e9 manera podemos detenernos correctamente en el mejor candidato?<\/p>\n

El p\u00e1rrafo anterior corresponde a una amena descripci\u00f3n de un problema cl\u00e1sico de toma
\nde decisiones en l\u00ednea denominado hist\u00f3ricamente como Problema de la Secretaria. En la charla estudiaremos diferentes generalizaciones del problema -como su extensi\u00f3n a matroides-, algoritmos hist\u00f3ricos que los resuelven as\u00ed como sus competitividades. Finalmente se plantear\u00e1 y resolver\u00e1 el problema definido en la tesis de magister: Multiple Matroid Secretary Problem, para la matroide gr\u00e1fica y transversal.<\/p>\n

Volver arriba<\/a><\/div>\n

Ivette Henr\u00edquez Carter: <\/strong>Optimizaci\u00f3n de Modelos de Lenguaje LLM para Evaluaciones Universitarias en F\u00edsica y Matem\u00e1tica<\/em><\/h2>\n
Universidad de Concepci\u00f3n | Financiada por el proyecto FONDECYT No11241189, Enhancing University Physics and Mathematics Learning Through AI Generated Feedback.<\/h6>\n

Los modelos de lenguaje grandes (LLMs) han llamado la atenci\u00f3n de manera significativa en varios campos de investigaci\u00f3n debido a sus notables capacidades. En el \u00e1mbito de la ingenier\u00eda de software, los LLMs han mostrado promesa en generar texto similar al humano, ayudando en tareas relacionadas con el c\u00f3digo (Al-Kaswan, 2024).<\/p>\n

En los \u00faltimos a\u00f1os, la integraci\u00f3n de modelos de lenguaje grandes en la educaci\u00f3n ha mostrado aplicaciones prometedoras. El estudio de Kung et al. (2023)<\/a> explora el rendimiento de ChatGPT en el USMLE, resaltando el potencial de los LLMs para mejorar la educaci\u00f3n m\u00e9dica y potencialmente ayudar en la toma de decisiones cl\u00ednicas. Esto subraya el papel transformador de los LLMs en la formaci\u00f3n y educaci\u00f3n m\u00e9dica. Adem\u00e1s, Kortemeyer (2023)<\/a> profundiza en la viabilidad de usar la IA para calificar soluciones estudiantiles en f\u00edsica introductoria, indicando el potencial de la IA para agilizar los procesos de evaluaci\u00f3n y proporcionar retroalimentaci\u00f3n personalizada a los estudiantes.<\/p>\n

Sin embargo, la selecci\u00f3n del modelo adecuado implica un equilibrio entre eficacia, costo operativo y la configuraci\u00f3n \u00f3ptima de par\u00e1metros. Las instituciones educativas enfrentan el reto de integrar estas tecnolog\u00edas en sus sistemas de ense\u00f1anza, lo que requiere no solo una inversi\u00f3n financiera, sino tambi\u00e9n una capacitaci\u00f3n adecuada del personal docente para maximizar el uso de estas herramientas.<\/p>\n

La metodolog\u00eda de esta investigaci\u00f3n se centrar\u00e1 en seleccionar distintos modelos y realizar distintas configuraciones en cuanto a variables como la temperatura, t\u00e9cnicas de prompt y costos por tokens.<\/p>\n

Los resultados de este estudio proporcionar\u00e1n un marco de referencia para las instituciones educativas, permitiendo una implementaci\u00f3n sostenible de LLMs en la ense\u00f1anza universitaria. Al optimizar la selecci\u00f3n, el uso de estos modelos y sus par\u00e1metros operativos, se espera mejorar la calidad de la ense\u00f1anza y el aprendizaje, ofreciendo un apoyo educativo integral que contribuya al \u00e9xito acad\u00e9mico de los estudiantes.<\/p>\n

Volver arriba<\/a><\/div>\n

Jessica Trespalacios Julio: <\/strong>Din\u00e1mica de solitones para las ecuaciones de Einstein<\/em><\/h2>\n
Universidad de Chile, Departamento de Ingenier\u00eda Matem\u00e1tica.<\/h6>\n

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\ud835\udc45_\u03bc\ud835\udf08(\ud835\udc54\u0303) = \ud835\udfe2,<\/p>\n

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(\ud835\uddc0\ud835\uddcb\ud835\uddba\ud835\uddcf\ud835\uddc2\ud835\uddcc\ud835\uddc8\ud835\uddc5\ud835\uddc2\ud835\uddcd\ud835\uddc8\ud835\uddc7\ud835\uddbe\ud835\uddcc), \ud835\uddc9\ud835\uddba\ud835\uddcb\ud835\uddba \ud835\uddc5\ud835\uddba \ud835\uddc5\ud835\uddc5\ud835\uddba\ud835\uddc6\ud835\uddba\ud835\uddbd\ud835\uddba \ud835\uddbe\ud835\uddbc\ud835\uddce\ud835\uddba\ud835\uddbc\ud835\uddc2\ud835\uddc8\u0301\ud835\uddc7 \ud835\uddcb\ud835\uddbe\ud835\uddbd\ud835\uddce\ud835\uddbc\ud835\uddc2\ud835\uddbd\ud835\uddba \ud835\uddbd\ud835\uddbe \ud835\udda4\ud835\uddc2\ud835\uddc7\ud835\uddcc\ud835\uddcd\ud835\uddbe\ud835\uddc2\ud835\uddc7, \ud835\uddcf\ud835\uddc2\ud835\uddcc\ud835\uddcd\ud835\uddba \ud835\uddbc\ud835\uddc8\ud835\uddc6\ud835\uddc8 \ud835\uddce\ud835\uddc7\ud835\uddba \ud835\uddc2\ud835\uddbd\ud835\uddbe\ud835\uddc7\ud835\uddcd\ud835\uddc2\ud835\uddbf\ud835\uddc2\ud835\uddbc\ud835\uddba\ud835\uddbc\ud835\uddc2\ud835\uddc8\u0301\ud835\uddc7 \ud835\uddbd\ud835\uddbe\ud835\uddc5 \ud835\uddc6\ud835\uddc8\ud835\uddbd\ud835\uddbe\ud835\uddc5\ud835\uddc8 \ud835\uddbd\ud835\uddbe\ud835\uddc5 \ud835\udda2\ud835\uddba\ud835\uddc6\ud835\uddc9\ud835\uddc8 \ud835\uddb0\ud835\uddce\ud835\uddc2\ud835\uddcb\ud835\uddba\ud835\uddc5 \ud835\uddaf\ud835\uddcb\ud835\uddc2\ud835\uddc7\ud835\uddbc\ud835\uddc2\ud835\uddc9\ud835\uddba\ud835\uddc5 (\ud835\uddaf\ud835\udda2\ud835\udda5).<\/p>\n

Volver arriba<\/a><\/div>\n

Constanza Gacit\u00faa Fuentes: <\/strong>Isomorfismos entre grafos aleatorios densos no homog\u00e9neos<\/em><\/h2>\n
Universidad de Concepci\u00f3n.<\/h6>\n

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\ud835\uddbc\ud835\uddc8\ud835\uddc7\ud835\uddcc\ud835\uddc2\ud835\uddbd\ud835\uddbe\ud835\uddcb\ud835\uddba\ud835\uddc7\ud835\uddbd\ud835\uddc8 \ud835\uddc8\ud835\uddcd\ud835\uddcb\ud835\uddc8\ud835\uddcc \ud835\uddc6\ud835\uddc8\ud835\uddbd\ud835\uddbe\ud835\uddc5\ud835\uddc8\ud835\uddcc \ud835\uddbd\ud835\uddbe \ud835\uddc0\ud835\uddcb\ud835\uddba\ud835\uddbf\ud835\uddc8\ud835\uddcc \ud835\uddba\ud835\uddc5\ud835\uddbe\ud835\uddba\ud835\uddcd\ud835\uddc8\ud835\uddcb\ud835\uddc2\ud835\uddc8\ud835\uddcc.<\/p>\n

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\ud835\uddbd\ud835\uddbe\ud835\uddbf\ud835\uddc2\ud835\uddc7\ud835\uddbe \ud835\uddce\ud835\uddc7 \ud835\uddc6\ud835\uddc8\ud835\uddbd\ud835\uddbe\ud835\uddc5\ud835\uddc8 \ud835\uddc7\ud835\uddc8 \ud835\uddc1\ud835\uddc8\ud835\uddc6\ud835\uddc8\ud835\uddc0\ud835\uddbe\u0301\ud835\uddc7\ud835\uddbe\ud835\uddc8 \ud835\uddbd\ud835\uddbe \ud835\uddc0\ud835\uddcb\ud835\uddba\ud835\uddbf\ud835\uddc8\ud835\uddcc \ud835\uddba\ud835\uddc5\ud835\uddbe\ud835\uddba\ud835\uddcd\ud835\uddc8\ud835\uddcb\ud835\uddc2\ud835\uddc8\ud835\uddcc \ud835\udc3a(\ud835\udc5b, \ud835\udc4a). \ud835\udda4\ud835\uddc5 \ud835\uddc6\ud835\uddc8\ud835\uddbd\ud835\uddbe\ud835\uddc5\ud835\uddc8 \ud835\udc3a(\ud835\udc5b, \ud835\udc4a) \ud835\uddbc\ud835\uddc8\ud835\uddc2\ud835\uddc7\ud835\uddbc\ud835\uddc2\ud835\uddbd\ud835\uddbe \ud835\uddbc\ud835\uddc8\ud835\uddc7 \ud835\uddbe\ud835\uddc5 \ud835\uddc6\ud835\uddc8\ud835\uddbd\ud835\uddbe\ud835\uddc5\ud835\uddc8 \ud835\uddc1\ud835\uddc8\ud835\uddc6\ud835\uddc8\ud835\uddc0\ud835\uddbe\u0301\ud835\uddc7\ud835\uddbe\ud835\uddc8 \ud835\uddbc\ud835\uddce\ud835\uddba\ud835\uddc7\ud835\uddbd\ud835\uddc8 \ud835\uddbe\ud835\uddc5 \ud835\uddc0\ud835\uddcb\ud835\uddba\ud835\uddbf\ud835\uddc8\ud835\uddc7 \ud835\uddbe\ud835\uddcc \ud835\uddce\ud835\uddc7\ud835\uddba \ud835\uddbf\ud835\uddce\ud835\uddc7\ud835\uddbc\ud835\uddc2\ud835\uddc8\u0301\ud835\uddc7 \ud835\uddbc\ud835\uddc8\ud835\uddc7\ud835\uddcc\ud835\uddcd\ud835\uddba\ud835\uddc7\ud835\uddcd\ud835\uddbe. \ud835\udda4\ud835\uddc7\ud835\uddcd\ud835\uddc8\ud835\uddc7\ud835\uddbc\ud835\uddbe\ud835\uddcc, \ud835\uddce\ud835\uddc7\ud835\uddba \ud835\uddc9\ud835\uddcb\ud835\uddbe\ud835\uddc0\ud835\uddce\ud835\uddc7\ud835\uddcd\ud835\uddba \ud835\uddc7\ud835\uddba\ud835\uddcd\ud835\uddce\ud835\uddcb\ud835\uddba\ud835\uddc5 \ud835\uddca\ud835\uddce\ud835\uddbe \ud835\uddc7\ud835\uddc8\ud835\uddcc \ud835\uddc9\ud835\uddc8\ud835\uddbd\ud835\uddbe\ud835\uddc6\ud835\uddc8\ud835\uddcc \ud835\uddc1\ud835\uddba\ud835\uddbc\ud835\uddbe\ud835\uddcb \ud835\uddbe\ud835\uddcc: \u00bf\ud835\uddbc\ud835\uddce\ud835\uddba\u0301\ud835\uddc5 \ud835\uddbe\ud835\uddcc \ud835\uddbe\ud835\uddc5 \ud835\uddcd\ud835\uddba\ud835\uddc6\ud835\uddba\ud835\uddc7\u0303\ud835\uddc8 \ud835\uddbd\ud835\uddbe\ud835\uddc5 \ud835\uddcc\ud835\uddce\ud835\uddbb\ud835\uddc0\ud835\uddcb\ud835\uddba\ud835\uddbf\ud835\uddc8 \ud835\uddbc\ud835\uddc8\ud835\uddc6\ud835\uddce\u0301\ud835\uddc7 \ud835\uddc2\ud835\uddc7\ud835\uddbd\ud835\uddce\ud835\uddbc\ud835\uddc2\ud835\uddbd\ud835\uddc8 \ud835\uddc6\ud835\uddba\u0301\ud835\uddcc \ud835\uddc0\ud835\uddcb\ud835\uddba\ud835\uddc7\ud835\uddbd\ud835\uddbe \ud835\uddbe\ud835\uddc7\ud835\uddcd\ud835\uddcb\ud835\uddbe \ud835\uddbd\ud835\uddc8\ud835\uddcc \ud835\uddc0\ud835\uddcb\ud835\uddba\ud835\uddbf\ud835\uddc8\ud835\uddcc \ud835\uddba\ud835\uddc5\ud835\uddbe\ud835\uddba\ud835\uddcd\ud835\uddc8\ud835\uddcb\ud835\uddc2\ud835\uddc8\ud835\uddcc \ud835\uddc7\ud835\uddc8 \ud835\uddc1\ud835\uddc8\ud835\uddc6\ud835\uddc8\ud835\uddc0\ud835\uddbe\u0301\ud835\uddc7\ud835\uddbe\ud835\uddc8\ud835\uddcc? \ud835\udda3\ud835\uddc2\ud835\uddbc\ud835\uddc1\ud835\uddba \ud835\uddc9\ud835\uddcb\ud835\uddbe\ud835\uddc0\ud835\uddce\ud835\uddc7\ud835\uddcd\ud835\uddba \ud835\uddc7\ud835\uddc8 \ud835\uddcc\ud835\uddbe \ud835\uddc1\ud835\uddba \ud835\uddcf\ud835\uddc2\ud835\uddcc\ud835\uddcd\ud835\uddc8 \ud835\uddcb\ud835\uddbe\ud835\uddcc\ud835\uddce\ud835\uddbe\ud835\uddc5\ud835\uddcd\ud835\uddba \ud835\uddbe\ud835\uddc7 \ud835\uddc5\ud835\uddba \ud835\uddc5\ud835\uddc2\ud835\uddcd\ud835\uddbe\ud835\uddcb\ud835\uddba\ud835\uddcd\ud835\uddce\ud835\uddcb\ud835\uddba, \ud835\uddd2 \ud835\uddbe\ud835\uddc5 \ud835\uddc8\ud835\uddbb\ud835\uddc3\ud835\uddbe\ud835\uddcd\ud835\uddc2\ud835\uddcf\ud835\uddc8 \ud835\uddbd\ud835\uddbe \ud835\uddbe\ud835\uddcc\ud835\uddcd\ud835\uddba \ud835\uddcd\ud835\uddc1\ud835\uddbe\ud835\uddcc\ud835\uddc2\ud835\uddcc \ud835\uddcc\ud835\uddbe\ud835\uddcb\ud835\uddba\u0301 \ud835\uddc9\ud835\uddcb\ud835\uddbe\ud835\uddbc\ud835\uddc2\ud835\uddcc\ud835\uddba\ud835\uddc6\ud835\uddbe\ud835\uddc7\ud835\uddcd\ud835\uddbe \ud835\uddba\ud835\uddbb\ud835\uddc8\ud835\uddcb\ud835\uddbd\ud835\uddba\ud835\uddcb\ud835\uddc5\ud835\uddba.<\/p>\n

Volver arriba<\/a><\/div>\n

Bruno Mart\u00ednez Barrera: <\/strong>Desarrollo de un modelo de optimizaci\u00f3n para el problema de selecci\u00f3n de pushbacks en miner\u00eda a cielo abierto<\/em><\/h2>\n
Universidad T\u00e9cnica Federico Santa Mar\u00eda | En coautor\u00eda con Mat\u00edas Reyes (Universidad de Chile) y Enrique J\u00e9lvez (Pontificia Universidad Cat\u00f3lica de Chile) | Financiada por el proyecto FONDECYT de Iniciaci\u00f3n N\u00b0 11221352.<\/h6>\n

La planificaci\u00f3n de la producci\u00f3n a largo plazo en miner\u00eda a cielo abierto se realiza tradicionalmente utilizando la metodolog\u00eda de pits anidados, cuyos fundamentos fueron establecidos en 1965 por Lerchs y Grossmann (LG). De un conjunto de pits anidados se genera un dise\u00f1o de fases, esencial para el agendamiento de producci\u00f3n. Sin embargo, este dise\u00f1o de fases presenta limitaciones: (i) el problema com\u00fanmente conocido como gap problem<\/em>, donde se generan grandes diferencias entre los tonelajes de fases consecutivas; y (ii) la falta de consideraci\u00f3n del tiempo en esta definici\u00f3n, lo cual es un aspecto condicionante que debe incluirse en las siguientes etapas del agendamiento de producci\u00f3n de bloques.<\/p>\n

En esta memoria, se presenta una extensi\u00f3n de la metodolog\u00eda tradicional de LG basada en pits anidados para generar una selecci\u00f3n de fases basada en tres ideas: (i) nuevas familias de pits anidados parametrizadas por el l\u00edmite de profundidad, (ii) incorporaci\u00f3n de la dimensi\u00f3n temporal para generar nuevas familias de pits; y (iii) intersecci\u00f3n de pits anidados tradicionales de LG con estos pits extendidos para ampliar a\u00fan m\u00e1s la base de selecci\u00f3n. Adem\u00e1s, se genera un modelo de selecci\u00f3n autom\u00e1tica de pushbacks para la b\u00fasqueda de planes de producci\u00f3n factibles. Una de las ventajas de este enfoque es la mejora en el proceso de selecci\u00f3n de fases, aumentando las opciones para el dise\u00f1o minero a cielo abierto, focalizando los esfuerzos de los planificadores mineros en etapas posteriores a la selecci\u00f3n de pushbacks, reduciendo el tiempo empleado en esta tarea y generando planes de producci\u00f3n operativos<\/p>\n

Volver arriba<\/a><\/div>\n

Sebasti\u00e1n Flores Sep\u00falveda: <\/strong>Continuaci\u00f3n \u00fanica en infinito para operadores integro-diferenciales el\u00edpticos<\/em><\/h2>\n
Universidad de Chile, Departamento de Ingenier\u00eda Matem\u00e1tica.<\/h6>\n

\ud835\uddb2\ud835\uddc2 \ud835\udc3c \ud835\uddbe\ud835\uddcc \ud835\uddce\ud835\uddc7 \ud835\uddc8\ud835\uddc9\ud835\uddbe\ud835\uddcb\ud835\uddba\ud835\uddbd\ud835\uddc8\ud835\uddcb \ud835\uddbe\ud835\uddc5\ud835\uddc2\u0301\ud835\uddc9\ud835\uddcd\ud835\uddc2\ud835\uddbc\ud835\uddc8 \ud835\uddc7\ud835\uddc8 \ud835\uddc5\ud835\uddc8\ud835\uddbc\ud835\uddba\ud835\uddc5, \ud835\udc5e \u2208 \ud835\udc3f\u221e(\u211d\u1d3a), \ud835\uddd2 \ud835\udc62 \ud835\uddbe\ud835\uddcc \ud835\uddce\ud835\uddc7\ud835\uddba \ud835\uddcc\ud835\uddc8\ud835\uddc5\ud835\uddce\ud835\uddbc\ud835\uddc2\ud835\uddc8\u0301\ud835\uddc7 \ud835\uddbd\ud835\uddbe:<\/p>\n

\ud835\udc3c[\ud835\udc62] + \ud835\udc5e(\ud835\udc65)\ud835\udc62(\ud835\udc65) = \ud835\udfe2, \u2200 \ud835\udc65 \u2208 \u211d\u1d3a,<\/p>\n

\u00bf\ud835\uddbc\ud835\uddce\ud835\uddba\u0301\ud835\uddc5 \ud835\uddbe\ud835\uddcc \ud835\uddbe\ud835\uddc5 \ud835\uddbd\ud835\uddbe\ud835\uddbc\ud835\uddba\ud835\uddc2\ud835\uddc6\ud835\uddc2\ud835\uddbe\ud835\uddc7\ud835\uddcd\ud835\uddc8 \ud835\uddc6\ud835\uddc2\u0301\ud835\uddc7\ud835\uddc2\ud835\uddc6\ud835\uddc8 \ud835\uddca\ud835\uddce\ud835\uddbe \ud835\uddbd\ud835\uddbe\ud835\uddbb\ud835\uddbe \ud835\uddcd\ud835\uddbe\ud835\uddc7\ud835\uddbe\ud835\uddcb \ud835\udc62 \ud835\uddbe\ud835\uddc7 \ud835\uddc2\ud835\uddc7\ud835\uddbf\ud835\uddc2\ud835\uddc7\ud835\uddc2\ud835\uddcd\ud835\uddc8 \ud835\uddc9\ud835\uddba\ud835\uddcb\ud835\uddba \ud835\uddbc\ud835\uddc8\ud835\uddc7\ud835\uddbc\ud835\uddc5\ud835\uddce\ud835\uddc2\ud835\uddcb \ud835\uddca\ud835\uddce\ud835\uddbe \ud835\udc62 \u2261 \ud835\udfe2?<\/p>\n

\ud835\uddab\ud835\uddba\ud835\uddc7\ud835\uddbd\ud835\uddc2\ud835\uddcc, \ud835\uddcc\ud835\uddbe\ud835\uddc0\ud835\uddce\u0301\ud835\uddc7 [\ud835\uddaa\ud835\uddab\ud835\udfea\ud835\udfea], \ud835\uddbe\ud835\uddc7 \ud835\uddc5\ud835\uddc8\ud835\uddcc \ud835\uddba\ud835\uddc7\u0303\ud835\uddc8\ud835\uddcc \ud835\udfe8\ud835\udfe2 \ud835\uddbc\ud835\uddc8\ud835\uddc7\ud835\uddc3\ud835\uddbe\ud835\uddcd\ud835\uddce\ud835\uddcb\ud835\uddc8\u0301 \ud835\uddca\ud835\uddce\ud835\uddbe, \ud835\uddcc\ud835\uddc2 \ud835\udc3c = \u0394, \ud835\uddbe\ud835\uddc7\ud835\uddcd\ud835\uddc8\ud835\uddc7\ud835\uddbc\ud835\uddbe\ud835\uddcc \ud835\uddbb\ud835\uddba\ud835\uddcc\ud835\uddcd\ud835\uddba \ud835\uddca\ud835\uddce\ud835\uddbe
\n\ud835\udc62(\ud835\udc65) = \ud835\udc42(\ud835\udc52\u207b\u1d9c\u2223\ud835\udc65\u2223) \ud835\uddc9\ud835\uddba\ud835\uddcb\ud835\uddba \ud835\uddba\ud835\uddc5\ud835\uddc0\ud835\uddce\ud835\uddc7 \ud835\udc36 > \ud835\udfe2 \ud835\uddc9\ud835\uddba\ud835\uddcb\ud835\uddba \ud835\uddbc\ud835\uddc8\ud835\uddc7\ud835\uddbc\ud835\uddc5\ud835\uddce\ud835\uddc2\ud835\uddcb \ud835\uddca\ud835\uddce\ud835\uddbe \ud835\udc62 \u2261 \ud835\udfe2. \ud835\udda3\ud835\uddbe\ud835\uddcc\ud835\uddbd\ud835\uddbe \ud835\uddbe\ud835\uddc7\ud835\uddcd\ud835\uddc8\ud835\uddc7\ud835\uddbc\ud835\uddbe\ud835\uddcc \ud835\uddcc\ud835\uddbe \ud835\uddc1\ud835\uddba\ud835\uddc7 \ud835\uddc8\ud835\uddbb\ud835\uddcd\ud835\uddbe\ud835\uddc7\ud835\uddc2\ud835\uddbd\ud835\uddc8 \ud835\uddbd\ud835\uddc2\ud835\uddcf\ud835\uddbe\ud835\uddcb\ud835\uddcc\ud835\uddc8\ud835\uddcc \ud835\uddcb\ud835\uddbe\ud835\uddcc\ud835\uddce\ud835\uddc5\ud835\uddcd\ud835\uddba\ud835\uddbd\ud835\uddc8\ud835\uddcc \ud835\uddc9\ud835\uddba\ud835\uddcb\ud835\uddbc\ud835\uddc2\ud835\uddba\ud835\uddc5\ud835\uddbe\ud835\uddcc \ud835\uddd2 \ud835\uddbe\ud835\uddd1\ud835\uddcd\ud835\uddbe\ud835\uddc7\ud835\uddcc\ud835\uddc2\ud835\uddc8\ud835\uddc7\ud835\uddbe\ud835\uddcc \ud835\uddbd\ud835\uddbe \ud835\uddbe\ud835\uddcc\ud835\uddcd\ud835\uddba \ud835\uddbc\ud835\uddc8\ud835\uddc7\ud835\uddc3\ud835\uddbe\ud835\uddcd\ud835\uddce\ud835\uddcb\ud835\uddba, \ud835\uddc9\ud835\uddbe\ud835\uddcb\ud835\uddc8 \ud835\uddcc\ud835\uddbe \ud835\uddc6\ud835\uddbe\ud835\uddc7\ud835\uddbc\ud835\uddc2\ud835\uddc8\ud835\uddc7\ud835\uddba \ud835\uddbe\ud835\uddcc\ud835\uddc9\ud835\uddbe\ud835\uddbc\ud835\uddc2\ud835\uddba\ud835\uddc5\ud835\uddc6\ud835\uddbe\ud835\uddc7\ud835\uddcd\ud835\uddbe \ud835\uddbe\ud835\uddc5 \ud835\uddcb\ud835\uddbe\ud835\uddcc\ud835\uddce\ud835\uddc5\ud835\uddcd\ud835\uddba\ud835\uddbd\ud835\uddc8 \ud835\uddbd\ud835\uddbe \ud835\uddb2\ud835\uddc2\ud835\uddcb\ud835\uddba\ud835\uddc4\ud835\uddc8\ud835\uddcf \ud835\uddd2 \ud835\uddb2\ud835\uddc8\ud835\uddce\ud835\uddc9\ud835\uddc5\ud835\uddbe\ud835\uddcd [\ud835\uddb2\ud835\uddb2\ud835\udfe4\ud835\udfe3] \ud835\uddca\ud835\uddce\ud835\uddbe \ud835\uddbd\ud835\uddba \ud835\uddce\ud835\uddc7\ud835\uddba \ud835\uddcb\ud835\uddbe\ud835\uddcc\ud835\uddc9\ud835\uddce\ud835\uddbe\ud835\uddcc\ud835\uddcd\ud835\uddba \ud835\uddc9\ud835\uddba\ud835\uddcb\ud835\uddba \ud835\uddce\ud835\uddc7\ud835\uddba \ud835\uddbc\ud835\uddc5\ud835\uddba\ud835\uddcc\ud835\uddbe \ud835\uddc6\ud835\uddce\ud835\uddd2 \ud835\uddba\ud835\uddc6\ud835\uddc9\ud835\uddc5\ud835\uddc2\ud835\uddba \ud835\uddbd\ud835\uddbe \ud835\uddc8\ud835\uddc9\ud835\uddbe\ud835\uddcb\ud835\uddba\ud835\uddbd\ud835\uddc8\ud835\uddcb\ud835\uddbe\ud835\uddcc \ud835\uddbe\ud835\uddc5\ud835\uddc2\u0301\ud835\uddc9\ud835\uddcd\ud835\uddc2\ud835\uddbc\ud835\uddc8\ud835\uddcc \ud835\uddbd\ud835\uddbe \ud835\uddcc\ud835\uddbe\ud835\uddc0\ud835\uddce\ud835\uddc7\ud835\uddbd\ud835\uddc8 \ud835\uddc8\ud835\uddcb\ud835\uddbd\ud835\uddbe\ud835\uddc7, \ud835\uddc2\ud835\uddc7\ud835\uddbc\ud835\uddc5\ud835\uddce\ud835\uddd2\ud835\uddbe\ud835\uddc7\ud835\uddbd\ud835\uddc8 \ud835\uddc5\ud835\uddc8\ud835\uddcc \ud835\uddc8\ud835\uddc9\ud835\uddbe\ud835\uddcb\ud835\uddba\ud835\uddbd\ud835\uddc8\ud835\uddcb\ud835\uddbe\ud835\uddcc \ud835\uddbe\ud835\uddd1\ud835\uddcd\ud835\uddcb\ud835\uddbe\ud835\uddc6\ud835\uddba\ud835\uddc5\ud835\uddbe\ud835\uddcc \ud835\uddbd\ud835\uddbe \ud835\uddaf\ud835\uddce\ud835\uddbc\ud835\uddbc\ud835\uddc2. \ud835\uddb2\ud835\uddce \ud835\uddc9\ud835\uddcb\ud835\uddce\ud835\uddbe\ud835\uddbb\ud835\uddba \ud835\uddbd\ud835\uddbe\ud835\uddcc\ud835\uddbc\ud835\uddba\ud835\uddc7\ud835\uddcc\ud835\uddba \ud835\uddbf\ud835\uddce\ud835\uddbe\ud835\uddcb\ud835\uddcd\ud835\uddbe\ud835\uddc6\ud835\uddbe\ud835\uddc7\ud835\uddcd\ud835\uddbe \ud835\uddbe\ud835\uddc7 \ud835\uddc9\ud835\uddcb\ud835\uddc2\ud835\uddc7\ud835\uddbc\ud835\uddc2\ud835\uddc9\ud835\uddc2\ud835\uddc8\ud835\uddcc \ud835\uddbd\ud835\uddbe\ud835\uddc5 \ud835\uddc6\ud835\uddba\u0301\ud835\uddd1\ud835\uddc2\ud835\uddc6\ud835\uddc8 \ud835\uddd2 \ud835\uddbd\ud835\uddbe\ud835\uddcc\ud835\uddbe\ud835\uddc0\ud835\uddce\ud835\uddba\ud835\uddc5\ud835\uddbd\ud835\uddba\ud835\uddbd\ud835\uddbe\ud835\uddcc \ud835\uddbd\ud835\uddbe \ud835\udda7\ud835\uddba\ud835\uddcb\ud835\uddc7\ud835\uddba\ud835\uddbc\ud835\uddc4 \ud835\uddcb\ud835\uddbe\ud835\uddbf\ud835\uddc2\ud835\uddc7\ud835\uddba\ud835\uddbd\ud835\uddba\ud835\uddcc.<\/p>\n

\ud835\udda4\ud835\uddc7 \ud835\uddbe\ud835\uddc5 \ud835\uddbc\ud835\uddba\ud835\uddcc\ud835\uddc8 \ud835\uddc7\ud835\uddc8 \ud835\uddc5\ud835\uddc8\ud835\uddbc\ud835\uddba\ud835\uddc5, \ud835\uddcc\ud835\uddc8\ud835\uddc5\ud835\uddc8 \ud835\uddcc\ud835\uddbe \ud835\uddcd\ud835\uddc2\ud835\uddbe\ud835\uddc7\ud835\uddbe\ud835\uddc7 \ud835\uddcb\ud835\uddbe\ud835\uddcc\ud835\uddc9\ud835\uddce\ud835\uddbe\ud835\uddcc\ud835\uddcd\ud835\uddba\ud835\uddcc \ud835\uddc9\ud835\uddba\ud835\uddcb\ud835\uddbc\ud835\uddc2\ud835\uddba\ud835\uddc5\ud835\uddbe\ud835\uddcc \ud835\uddbe\ud835\uddc7 \ud835\uddbe\ud835\uddc5 \ud835\uddbc\ud835\uddba\ud835\uddcc\ud835\uddc8 \ud835\uddbe\ud835\uddc7 \ud835\uddca\ud835\uddce\ud835\uddbe \ud835\uddbe\ud835\uddc5 \ud835\uddc8\ud835\uddc9\ud835\uddbe\ud835\uddcb\ud835\uddba\ud835\uddbd\ud835\uddc8\ud835\uddcb \ud835\uddbe\ud835\uddcc \ud835\uddbe\ud835\uddc5 \ud835\uddab\ud835\uddba\ud835\uddc9\ud835\uddc5\ud835\uddba\ud835\uddbc\ud835\uddc2\ud835\uddba\ud835\uddc7\ud835\uddc8 \ud835\uddbf\ud835\uddcb\ud835\uddba\ud835\uddbc\ud835\uddbc\ud835\uddc2\ud835\uddc8\ud835\uddc7\ud835\uddba\ud835\uddcb\ud835\uddc2\ud835\uddc8, \ud835\uddc8\ud835\uddbb\ud835\uddcd\ud835\uddbe\ud835\uddc7\ud835\uddc2\ud835\uddbd\ud835\uddba\ud835\uddcc \ud835\uddc9\ud835\uddc8\ud835\uddcb \ud835\uddb1\ud835\uddce\u0308\ud835\uddc5\ud835\uddba\ud835\uddc7\ud835\uddbd \ud835\uddd2 \ud835\uddb6\ud835\uddba\ud835\uddc7\ud835\uddc0 [\ud835\uddb1\ud835\uddb6\ud835\udfe3\ud835\udfeb], \ud835\uddd2 \ud835\uddaa\ud835\uddc8\ud835\uddd0 \ud835\uddd2 \ud835\uddb6\ud835\uddba\ud835\uddc7\ud835\uddc0 [\ud835\uddaa\ud835\uddb6\ud835\udfe4\ud835\udfe5]. \ud835\uddab\ud835\uddba\ud835\uddcc \ud835\uddcd\ud835\uddbe\u0301\ud835\uddbc\ud835\uddc7\ud835\uddc2\ud835\uddbc\ud835\uddba\ud835\uddcc \ud835\uddce\ud835\uddcc\ud835\uddba\ud835\uddbd\ud835\uddba\ud835\uddcc \ud835\uddc9\ud835\uddc8\ud835\uddcb \ud835\uddbe\ud835\uddcc\ud835\uddcd\ud835\uddc8\ud835\uddcc \ud835\uddbd\ud835\uddc8\ud835\uddcc \ud835\uddce\u0301\ud835\uddc5\ud835\uddcd\ud835\uddc2\ud835\uddc6\ud835\uddc8\ud835\uddcc \ud835\uddba\ud835\uddce\ud835\uddcd\ud835\uddc8\ud835\uddcb\ud835\uddbe\ud835\uddcc \ud835\uddc7\ud835\uddc8 \ud835\uddcc\ud835\uddc8\ud835\uddc7 \ud835\uddba\ud835\uddbd\ud835\uddba\ud835\uddc9\ud835\uddcd\ud835\uddba\ud835\uddbb\ud835\uddc5\ud835\uddbe\ud835\uddcc \ud835\uddba \ud835\uddc8\ud835\uddc9\ud835\uddbe\ud835\uddcb\ud835\uddba\ud835\uddbd\ud835\uddc8\ud835\uddcb\ud835\uddbe\ud835\uddcc \ud835\uddbe\ud835\uddc5\ud835\uddc2\u0301\ud835\uddc9\ud835\uddcd\ud835\uddc2\ud835\uddbc\ud835\uddc8\ud835\uddcc \ud835\uddc7\ud835\uddc8 \ud835\uddc5\ud835\uddc8\ud835\uddbc\ud835\uddba\ud835\uddc5\ud835\uddbe\ud835\uddcc \ud835\uddc6\ud835\uddba\u0301\ud835\uddcc \ud835\uddc0\ud835\uddbe\ud835\uddc7\ud835\uddbe\ud835\uddcb\ud835\uddba\ud835\uddc5\ud835\uddbe\ud835\uddcc, \ud835\uddbd\ud835\uddba\ud835\uddbd\ud835\uddc8 \ud835\uddca\ud835\uddce\ud835\uddbe \ud835\uddbd\ud835\uddbe\ud835\uddc9\ud835\uddbe\ud835\uddc7\ud835\uddbd\ud835\uddbe\ud835\uddc7 \ud835\uddbd\ud835\uddbe \ud835\uddce\ud835\uddc7\ud835\uddba \ud835\uddbc\ud835\uddba\ud835\uddcb\ud835\uddba\ud835\uddbc\ud835\uddcd\ud835\uddbe\ud835\uddcb\ud835\uddc2\ud835\uddd3\ud835\uddba\ud835\uddbc\ud835\uddc2\ud835\uddc8\u0301\ud835\uddc7 \ud835\uddbd\ud835\uddbe\ud835\uddc5 \ud835\uddab\ud835\uddba\ud835\uddc9\ud835\uddc5\ud835\uddba\ud835\uddbc\ud835\uddc2\ud835\uddba\ud835\uddc7\ud835\uddc8 \ud835\uddbf\ud835\uddcb\ud835\uddba\ud835\uddbc\ud835\uddbc\ud835\uddc2\ud835\uddc8\ud835\uddc7\ud835\uddba\ud835\uddcb\ud835\uddc2\ud835\uddc8 \ud835\uddc9\ud835\uddc8\ud835\uddcb \ud835\uddce\ud835\uddc7 \ud835\uddc9\ud835\uddcb\ud835\uddc8\ud835\uddbb\ud835\uddc5\ud835\uddbe\ud835\uddc6\ud835\uddba \ud835\uddbd\ud835\uddbe \ud835\uddbe\ud835\uddd1\ud835\uddcd\ud835\uddbe\ud835\uddc7\ud835\uddcc\ud835\uddc2\ud835\uddc8\u0301\ud835\uddc7 \ud835\uddbe\ud835\uddc7 \u211d\u1d3a\u207a\u00b9 \ud835\uddca\ud835\uddce\ud835\uddbe \ud835\uddc7\ud835\uddc8 \ud835\uddbe\ud835\uddcc \ud835\uddcf\ud835\uddba\u0301\ud835\uddc5\ud835\uddc2\ud835\uddbd\ud835\uddba \ud835\uddc9\ud835\uddba\ud835\uddcb\ud835\uddba \ud835\uddc8\ud835\uddcd\ud835\uddcb\ud835\uddc8\ud835\uddcc \ud835\uddc8\ud835\uddc9\ud835\uddbe\ud835\uddcb\ud835\uddba\ud835\uddbd\ud835\uddc8\ud835\uddcb\ud835\uddbe\ud835\uddcc.<\/p>\n

\ud835\udda4\ud835\uddc7 \ud835\uddc6\ud835\uddc2 \ud835\uddc9\ud835\uddcb\ud835\uddbe\ud835\uddcc\ud835\uddbe\ud835\uddc7\ud835\uddcd\ud835\uddba\ud835\uddbc\ud835\uddc2\ud835\uddc8\u0301\ud835\uddc7, \ud835\uddc9\ud835\uddcb\ud835\uddbe\ud835\uddcd\ud835\uddbe\ud835\uddc7\ud835\uddbd\ud835\uddc8 \ud835\uddc2\ud835\uddc7\ud835\uddcd\ud835\uddcb\ud835\uddc8\ud835\uddbd\ud835\uddce\ud835\uddbc\ud835\uddc2\ud835\uddcb \ud835\uddbe\ud835\uddc5 \ud835\uddcd\ud835\uddc2\ud835\uddc9\ud835\uddc8 \ud835\uddbd\ud835\uddbe \ud835\uddc8\ud835\uddc9\ud835\uddbe\ud835\uddcb\ud835\uddba\ud835\uddbd\ud835\uddc8\ud835\uddcb\ud835\uddbe\ud835\uddcc \ud835\uddca\ud835\uddce\ud835\uddbe \ud835\uddbe\ud835\uddcc\ud835\uddcd\ud835\uddce\ud835\uddbd\ud835\uddc2\ud835\uddc8, \ud835\uddc5\ud835\uddba \ud835\uddc1\ud835\uddc2\ud835\uddcc\ud835\uddcd\ud835\uddc8\ud835\uddcb\ud835\uddc2\ud835\uddba \ud835\uddbd\ud835\uddbe \ud835\uddc5\ud835\uddba \ud835\uddbc\ud835\uddc8\ud835\uddc7\ud835\uddc3\ud835\uddbe\ud835\uddcd\ud835\uddce\ud835\uddcb\ud835\uddba \ud835\uddbd\ud835\uddbe \ud835\uddab\ud835\uddba\ud835\uddc7\ud835\uddbd\ud835\uddc2\ud835\uddcc, \ud835\uddba\ud835\uddcc\ud835\uddc2\u0301 \ud835\uddbc\ud835\uddc8\ud835\uddc6\ud835\uddc8 \ud835\uddc6\ud835\uddc2\ud835\uddcc \ud835\uddcb\ud835\uddbe\ud835\uddcc\ud835\uddce\ud835\uddc5\ud835\uddcd\ud835\uddba\ud835\uddbd\ud835\uddc8\ud835\uddcc \ud835\uddc1\ud835\uddba\ud835\uddcc\ud835\uddcd\ud835\uddba \ud835\uddc1\ud835\uddc8\ud835\uddd2.<\/p>\n

Volver arriba<\/a><\/div>\n

Esteban Carrasco Barra: <\/strong>Modelaci\u00f3n Avanzada de la Propagaci\u00f3n de Incendios Forestales: Reformulaci\u00f3n del Modelo Kitral<\/em><\/h2>\n
Universidad de La Frontera.<\/h6>\n

Este trabajo presenta una reformulaci\u00f3n del modelo Kitral, utilizado para la simulaci\u00f3n de incendios forestales en Chile. La reformulaci\u00f3n incluye el desarrollo de nuevas funciones de aproximaci\u00f3n para calcular los factores de propagaci\u00f3n basados en el contenido de humedad, la pendiente del terreno y la velocidad del viento. Estas funciones se derivaron mediante ajustes no lineales a datos emp\u00edricos, mejorando la precisi\u00f3n de la simulaci\u00f3n en comparaci\u00f3n con las ecuaciones originales del modelo Kitral.<\/p>\n

El ajuste se llev\u00f3 a cabo utilizando datos emp\u00edricos obtenidos de incendios reales, y se emplearon nuevas formas funcionales, como funciones exponenciales, sigmoides y logar\u00edtmicas, que permiten una representaci\u00f3n m\u00e1s precisa de los factores que influyen en la propagaci\u00f3n del fuego. La comparaci\u00f3n de los resultados obtenidos con las funciones originales y las funciones ajustadas demuestra una mejora significativa en la predicci\u00f3n de la velocidad de propagaci\u00f3n del fuego, lo cual es crucial para una planificaci\u00f3n efectiva de estrategias de prevenci\u00f3n y combate de incendios.<\/p>\n

Finalmente, se propone el uso de estas nuevas funciones en sistemas de apoyo a la toma de decisiones en la gesti\u00f3n del riesgo de incendios forestales, con el objetivo de mejorar la seguridad de las comunidades afectadas y optimizar los recursos disponibles para el combate del fuego. El trabajo actual se centra en la aplicaci\u00f3n de modelos fisicomatem\u00e1ticos, incluyendo ecuaciones en derivadas parciales (EDP) con tensores y an\u00e1lisis vectorial para capturar de manera m\u00e1s detallada los procesos de propagaci\u00f3n del fuego.<\/p>\n

Tal como se discute en otros estudios sobre simulaci\u00f3n de incendios forestales, como el trabajo de Sero-Guillaume y Margerit (2002), la reducci\u00f3n de modelos tridimensionales de combusti\u00f3n a modelos bidimensionales de reacci\u00f3n-difusi\u00f3n ha mostrado ser una t\u00e9cnica eficaz para simplificar y hacer m\u00e1s manejable la simulaci\u00f3n num\u00e9rica de la propagaci\u00f3n del fuego. La implementaci\u00f3n de estas t\u00e9cnicas permitir\u00e1 una mayor fidelidad en la simulaci\u00f3n de incendios forestales, facilitando el desarrollo de herramientas m\u00e1s precisas para la planificaci\u00f3n y gesti\u00f3n de emergencias.<\/p>\n

Como parte de esta reformulaci\u00f3n, se considera la siguiente ecuaci\u00f3n en derivadas parciales, basada en modelos de reacci\u00f3n-difusi\u00f3n para la propagaci\u00f3n del fuego:<\/p>\n

\u2202<\/mi>T<\/mi><\/mrow>\u2202<\/mi>t<\/mi><\/mrow><\/mfrac>=<\/mo>D<\/mi>\u2207<\/mi>2<\/mn><\/msup>T<\/mi>+<\/mo>R<\/mi>(<\/mo>T<\/mi>)<\/mo>\u2212<\/mo>v<\/mi>\u22c5<\/mo>\u2207<\/mi>T<\/mi>,<\/mo><\/mrow>\\frac{\\partial T}{\\partial t} = D \\nabla^2 T + R(T) – \\mathbf{v} \\cdot \\nabla T,<\/annotation><\/semantics><\/math><\/h2>\n

donde T representa la temperatura, D es el coeficiente de difusi\u00f3n t\u00e9rmica, R(T) es un t\u00e9rmino de reacci\u00f3n que modela la producci\u00f3n de calor debido a la combusti\u00f3n, y v es el campo de velocidad que representa la influencia del viento en la propagaci\u00f3n del fuego. En este contexto, se utilizar\u00e1n tensores para describir las propiedades anisotr\u00f3picas del medio y su impacto en la difusi\u00f3n del calor, as\u00ed como herramientas de an\u00e1lisis vectorial para modelar la din\u00e1mica del viento y su interacci\u00f3n con la vegetaci\u00f3n. Esta EDP captura de manera simplificada los procesos de transferencia de calor y la din\u00e1mica de propagaci\u00f3n del frente de llama en incendios forestales.<\/p>\n

Volver arriba<\/a><\/div>\n

Javiera Guti\u00e9rrez Ram\u00edrez: <\/strong>Opiniones Asint\u00f3ticas de Agentes Parcialmente Obstinados en Grafos Aleatorios Erd\u00f6s\u2013R\u00e9nyi de Gran Tama\u00f1o<\/em><\/h2>\n
Universidad de Chile, Departamento de Ingenier\u00eda Matem\u00e1tica.<\/h6>\n

En este trabajo de tesis, se investigan las opiniones asint\u00f3ticas de agentes parcialmente obstinados en grafos aleatorios Erd\u0151s\u2013R\u00e9nyi de gran tama\u00f1o. Se desarrollan nuevos enfoques y se presentan teoremas claves que entregan resultados de concentraci\u00f3n cuando el tama\u00f1o de la red crece a infinito. En otras palabras, se mide la diferencia entre el valor esperado de la opini\u00f3n asint\u00f3tica temporal como variable aleatoria y la opini\u00f3n asint\u00f3tica temporal sobre un grafo determinista.<\/p>\n

El trabajo aporta una nueva perspectiva en el an\u00e1lisis de modelos de opini\u00f3n en grafos aleatorios de gran tama\u00f1o, usando modelos promediados. Se proponen nuevas direcciones de investigaci\u00f3n, como la consideraci\u00f3n de grafos aleatorios Erd\u0151s\u2013R\u00e9nyi no homog\u00e9neos y la exploraci\u00f3n de la aplicabilidad de los resultados en grafos aleatorios geom\u00e9tricos. Los hallazgos proporcionan una base s\u00f3lida para futuros estudios sobre la din\u00e1mica de opini\u00f3n en redes complejas.<\/p>\n

Volver arriba<\/a><\/div>\n","protected":false},"excerpt":{"rendered":"

\u00c1lvaro M\u00e1rquez: Generaci\u00f3n de modelos de atm\u00f3sferas estelares mediante PINNs Universidad de Chile, Departamento de Ingenier\u00eda Matem\u00e1tica. En el \u00e1rea de astronom\u00eda existe lo que se conoce como la espectroscopia estelar, que corresponde al estudio del espectro procedente de las estrellas captado mediante diversos instrumentos como ser\u00edan los telescopios usuales o las antenas usadas por … Contin\u00fae leyendo Tesistas<\/span> →<\/span><\/a><\/p>\n","protected":false},"author":120,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"inline_featured_image":false,"footnotes":""},"class_list":["post-495","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/eventos.cmm.uchile.cl\/enim2024\/wp-json\/wp\/v2\/pages\/495","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/eventos.cmm.uchile.cl\/enim2024\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/eventos.cmm.uchile.cl\/enim2024\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/eventos.cmm.uchile.cl\/enim2024\/wp-json\/wp\/v2\/users\/120"}],"replies":[{"embeddable":true,"href":"https:\/\/eventos.cmm.uchile.cl\/enim2024\/wp-json\/wp\/v2\/comments?post=495"}],"version-history":[{"count":36,"href":"https:\/\/eventos.cmm.uchile.cl\/enim2024\/wp-json\/wp\/v2\/pages\/495\/revisions"}],"predecessor-version":[{"id":682,"href":"https:\/\/eventos.cmm.uchile.cl\/enim2024\/wp-json\/wp\/v2\/pages\/495\/revisions\/682"}],"wp:attachment":[{"href":"https:\/\/eventos.cmm.uchile.cl\/enim2024\/wp-json\/wp\/v2\/media?parent=495"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}