{"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\/de\/blog\/can-ai-help-us-solve-complex-problems-in-physics","title":{"rendered":"Kann KI uns helfen, komplexe Probleme in der Physik zu l\u00f6sen?"},"content":{"rendered":"
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Der Einsatz von KI erstreckt sich jetzt \u00fcber viele Bereiche, und die Physik ist keine Ausnahme.<\/p>

Somit, Wissenschaftler vom MIT und der Universit\u00e4t Basel<\/a> haben k\u00fcrzlich generative k\u00fcnstliche Intelligenz eingesetzt, um schwierige Fragen in der Physik anzugehen, insbesondere bei der Klassifizierung von Phasen physikalischer Systeme.<\/p>

Dieses fortschrittliche Verfahren k\u00f6nnte die Untersuchung neuer Materialien erheblich verbessern. Lassen Sie uns im Detail sehen, wie es funktioniert.<\/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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Die Forschung zu Phasen\u00fcberg\u00e4ngen<\/strong><\/h3>

Die Forscher wandten KI an, um Phasen\u00fcberg\u00e4nge in neu entwickelten Materialien zu untersuchen. Phasen\u00fcberg\u00e4nge, wie wenn Wasser zu Eis gefriert, f\u00fchren zu erheblichen Ver\u00e4nderungen der Eigenschaften wie Dichte und Volumen. W\u00e4hrend diese \u00dcberg\u00e4nge in g\u00e4ngigen Substanzen gut verstanden sind, bleiben jene in neuartigen Materialien oder komplexen Systemen ein kritisches Forschungsgebiet. Das Erkennen und Quantifizieren dieser \u00dcberg\u00e4nge in unbekannten Systemen ist oft eine Herausforderung aufgrund begrenzter Daten.<\/p>

Die Forscher vom MIT und der Universit\u00e4t Basel<\/a> haben einen Machine-Learning-Rahmen entwickelt, der generative KI einsetzt, um automatisch Phasendiagramme f\u00fcr neue physikalische Systeme zu erstellen. Dieser physikinformierte Ansatz ist effizienter als traditionelle, manuelle Techniken, die stark auf theoretisches Fachwissen und gro\u00dfe, beschriftete Datens\u00e4tze angewiesen sind.<\/p>

Traditionelle Methoden zur Erkennung von Phasen\u00fcberg\u00e4ngen beinhalten die Identifizierung eines \u201eOrdnungparameters\u201c, der sich w\u00e4hrend des \u00dcbergangs \u00e4ndert. Zum Beispiel beinhaltet der \u00dcbergang von Wasser zu Eis, dass die Temperatur unter 0 Grad Celsius f\u00e4llt, wobei der Ordnungparameter der Anteil der Wassermolek\u00fcle in einem kristallinen Zustand ist. KI-Modelle, wie generative Klassifikatoren, k\u00f6nnen diese Aufgabe jetzt effizienter und ohne menschliche Voreingenommenheit bew\u00e4ltigen.<\/p>

Anwendung in fortschrittlichen Materialien und Quantensystemen<\/strong><\/h3>

Der neue KI-Rahmen k\u00f6nnte erhebliche Auswirkungen auf das Studium der thermodynamischen Eigenschaften in neuartigen Materialien und die Erkennung von Quantenverschr\u00e4nkung haben. Diese Technik \u00f6ffnet die T\u00fcr zur autonomen Entdeckung bisher unbekannter Materiephasen und optimiert den wissenschaftlichen Entdeckungsprozess.<\/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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Vorteile von Generativen Modellen<\/strong><\/h3>

Generative Modelle, die Wahrscheinlichkeitsverteilungen sch\u00e4tzen, um neue Datenpunkte zu generieren, bieten signifikante Vorteile beim Aufbau von Klassifikatoren. Der Ansatz des MIT-Teams besteht darin, diese Modelle zu verwenden, um bekannte Eigenschaften physikalischer Systeme tief in das Machine-Learning-Framework zu integrieren, was die Genauigkeit und Effizienz der Phasendetektion verbessert.<\/p>

Die potenziellen Anwendungen dieses generativen KI-Ansatzes gehen \u00fcber Phasen\u00fcberg\u00e4nge hinaus. Er k\u00f6nnte verwendet werden, um verschiedene bin\u00e4re Klassifikationsaufgaben in physikalischen Systemen zu l\u00f6sen, Quantenverschr\u00e4nkung zu erkennen und sogar gro\u00dfe Sprachmodelle durch Optimierung des Parameter-Tunings zu verbessern. Zuk\u00fcnftige Forschungen zielen darauf ab, theoretische Garantien f\u00fcr die Anzahl der Messungen zu etablieren, die erforderlich sind, um Phasen\u00fcberg\u00e4nge effektiv zu erkennen.<\/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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Da KI zunehmend in verschiedene Aspekte unseres Lebens integriert wird, benachteiligt es Sie, sie zu vermeiden. Unsere KI-basierte Datenanalyseplattform, NextBrain AI<\/a>, wurde entwickelt, um jedem Unternehmen zu helfen, die Vorteile von AI zu nutzen. Vereinbaren Sie noch heute Ihre Demo<\/a> um die Erkenntnisse zu entdecken, die KI aus Ihren Daten gewinnen kann.<\/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\"Demo\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\/de\/blog\/can-ai-help-us-solve-complex-problems-in-physics\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\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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