H2020Индивидуална стипендия2020–2023

ML4SFT · Machine Learning for String Field Theory and for the String- and F-Theory landscapes

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период: 2020-11-01 → 2023-10-31
Финансиране от ЕС: 275 620 €
Участници: 2
Схема: MSCA-IF

Линиите свързват координатора с партньорите.

Накратко на български

Теорията на струните изследва фундаменталните закони на Вселената, като използва машинно обучение за анализ на геометрията на скритите измерения. Това помага за създаването на по-ефективни методи за свързване на теоретичните модели с наблюдаемите физични явления.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Machine Learning for String Field Theory and for the String- and F-Theory landscapes

String theory is one of the leading theory for quantum gravity and unification. As such, it provides a complete description of the fundamental aspects of our Universe. Because it predicts a 10-dimensional Universe, one must compactify 6 dimensions into a tiny volume such that they are invisible to usual experimental scales. The objective of this project is to investigate two aspects of string theory: first, understanding the compactification geometries using machine learning, second to study the quantum field theory of strings and obtain interactions more explicitly. The latter part of the project includes both formal and machine learning techniques. This will push the boundary of our field by providing new and efficient techniques for bridging the gap between string theory and phenomenology. The project has succeeded in developing neural networks to compute topological properties of the compactification geometries and of the string interactions. Moreover, it has been demonstrated that, in some instances, string field theory can be rewritten in a simpler fashion by introducing extra auxiliary degrees of freedom. These results provide major conceptual and technical progress in string theory and will provide a strong basis for future developments.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

String theory is one of the leading endeavours in theoretical physics to construct a theory of quantum gravity unified with all interactions and matter. As such, it provides a complete description of the Universe and of its content. However, while all ingredients are present, the details for a precise contact with the Standard Model and with our Universe are missing, mostly because the number of possible realizations is huge and no selection mechanism is known. Moreover, progress is further hindered, for the description of string theory as a field theory - arguably its most fundamental formulation - is very intricate. Since the most immediate difficulties are computational - it boils down to studying statistics of geometries (the ""string landscape"") and to approximating functions and geometries (to build a string field theory) - machine learning seems to provide an adequate framework to address the challenges faced by string theory.The first aspect of this project is to elaborate machine learning tools for constructing the string field theory action while deepening in parallel our analytic understanding of closed string field theory. For the latter, the main objective is to include auxiliary fields and to investigate whether the action can be made cubic. The second aspect is to design machine learning algorithms to map the string landscape.This project holds the promise of important developments in our understanding of string theory and in its applications to phenomenology. It is located at the intersection of multiple disciplines - theoretical physics, mathematics (Riemann surfaces and homotopy algebras) and machine learning. The choice of institutions and supervisors reflect this interdisciplinary aspect: Prof. Zwiebach is an expert in string (field) theory and in the moduli space geometry, while Dr. Tamaazousti is a specialist of machine learning. Furthermore, both institutions are renowned in these domains.""

Оригинален текст от CORDIS (на английски).

Участници

COMMISSARIAT A L ENERGIE ATOMIQUE ET AUX ENERGIES ALTERNATIVES · ParisКоординаторФранция
MASSACHUSETTS INSTITUTE OF TECHNOLOGY · CambridgeСъединени щати