{"id":8229,"date":"2024-07-29T07:43:02","date_gmt":"2024-07-29T05:43:02","guid":{"rendered":"http:\/\/nextbrain.ai\/?p=8229"},"modified":"2024-07-29T07:43:04","modified_gmt":"2024-07-29T05:43:04","slug":"can-ai-help-us-solve-complex-problems-in-physics","status":"publish","type":"post","link":"https:\/\/nextbrain.ai\/es\/blog\/can-ai-help-us-solve-complex-problems-in-physics","title":{"rendered":"\u00bfPuede la IA ayudarnos a resolver problemas complejos en f\u00edsica?"},"content":{"rendered":"
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El uso de la IA ahora se ha extendido a muchos campos, y la f\u00edsica no es una excepci\u00f3n.<\/p>

As\u00ed, cient\u00edficos del MIT y la Universidad de Basilea<\/a> han empleado recientemente inteligencia artificial generativa para abordar preguntas dif\u00edciles en f\u00edsica, particularmente en la clasificaci\u00f3n de fases de sistemas f\u00edsicos.<\/p>

Este m\u00e9todo avanzado podr\u00eda mejorar significativamente la investigaci\u00f3n de nuevos materiales. Veamos en detalle c\u00f3mo funciona.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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La Investigaci\u00f3n de las Transiciones de Fase<\/strong><\/h3>

Los investigadores aplicaron IA para estudiar las transiciones de fase en materiales reci\u00e9n desarrollados. Las transiciones de fase, como cuando el agua se congela en hielo, resultan en cambios significativos en propiedades como la densidad y el volumen. Mientras que estas transiciones en sustancias comunes est\u00e1n bien entendidas, las de materiales novedosos o sistemas complejos siguen siendo un \u00e1rea cr\u00edtica de investigaci\u00f3n. Reconocer y cuantificar estas transiciones en sistemas desconocidos es a menudo un desaf\u00edo debido a los datos limitados.<\/p>

Los investigadores del MIT y la Universidad de Basilea<\/a> han desarrollado un marco de aprendizaje autom\u00e1tico que emplea IA generativa para mapear autom\u00e1ticamente diagramas de fase para nuevos sistemas f\u00edsicos. Este enfoque informado por la f\u00edsica es m\u00e1s eficiente que las t\u00e9cnicas tradicionales, manuales, que dependen en gran medida de la experiencia te\u00f3rica y de grandes conjuntos de datos etiquetados.<\/p>

Los m\u00e9todos tradicionales para detectar transiciones de fase implican identificar un \u201cpar\u00e1metro de orden\u201d que cambia durante la transici\u00f3n. Por ejemplo, la transici\u00f3n del agua a hielo implica que la temperatura descienda por debajo de 0 grados Celsius, siendo el par\u00e1metro de orden la proporci\u00f3n de mol\u00e9culas de agua en un estado cristalino. Los modelos de IA, como los clasificadores generativos, ahora pueden manejar esta tarea de manera m\u00e1s eficiente y sin sesgo humano.<\/p>

Aplicaci\u00f3n en Materiales Avanzados y Sistemas Cu\u00e1nticos<\/strong><\/h3>

El nuevo marco de IA podr\u00eda impactar significativamente el estudio de las propiedades termodin\u00e1micas en materiales novedosos y la detecci\u00f3n del entrelazamiento cu\u00e1ntico. Esta t\u00e9cnica abre la puerta a descubrir fases de la materia previamente desconocidas de forma aut\u00f3noma, agilizando el proceso de descubrimiento cient\u00edfico.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Ventajas de los Modelos Generativos<\/strong><\/h3>

Los modelos generativos, que estiman distribuciones de probabilidad para generar nuevos puntos de datos, ofrecen ventajas significativas en la construcci\u00f3n de clasificadores. El enfoque del equipo del MIT implica utilizar estos modelos para incorporar profundamente las propiedades conocidas de los sistemas f\u00edsicos en el marco de aprendizaje autom\u00e1tico, mejorando la precisi\u00f3n y eficiencia de la detecci\u00f3n de fases.<\/p>

Las aplicaciones potenciales de este enfoque de IA generativa se extienden m\u00e1s all\u00e1 de las transiciones de fase. Podr\u00eda utilizarse para resolver diversas tareas de clasificaci\u00f3n binaria en sistemas f\u00edsicos, detectar el entrelazamiento cu\u00e1ntico e incluso mejorar los modelos de lenguaje grandes optimizando la sintonizaci\u00f3n de par\u00e1metros. La investigaci\u00f3n futura tiene como objetivo establecer garant\u00edas te\u00f3ricas sobre el n\u00famero de mediciones necesarias para detectar transiciones de fase de manera efectiva.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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A medida que la IA se integra cada vez m\u00e1s en varios aspectos de nuestras vidas, evitarla te pone en desventaja. Nuestra plataforma de an\u00e1lisis de datos basada en IA, NextBrain IA<\/a>, est\u00e1 dise\u00f1ado para ayudar a cada negocio a aprovechar los beneficios de la IA. Programa tu demostraci\u00f3n hoy.<\/a> para descubrir las ideas que la IA puede revelar a partir de tus datos.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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

Use of AI is now spread over many fields, and physics is no exception. Thus, scientists from MIT and the University of Basel have recently […]<\/p>\n","protected":false},"author":1,"featured_media":8230,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[70],"tags":[717,264,282,633,716,718,721,715,719],"class_list":["post-8229","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","tag-automated-scientific-discovery","tag-generative-ai","tag-machine-learning","tag-mit","tag-novel-materials","tag-phase-transitions","tag-physical-systems","tag-quantum-systems","tag-thermodynamic-properties"],"yoast_head":"\nCan AI Help Us Solve Complex Problems in Physics? - NextBrain AI | No-Code Machine Learning<\/title>\n<meta name=\"description\" content=\"This article explains how generative AI is improving physics research by automatically classifying phases in physical systems, paving the way for new material discoveries.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/nextbrain.ai\/es\/blog\/can-ai-help-us-solve-complex-problems-in-physics\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Can AI Help Us Solve Complex Problems in Physics? 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