Summary
"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."
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."
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/891169 |
| Start date: | 01-11-2020 |
| End date: | 31-10-2023 |
| Total budget - Public funding: | 275 619,84 Euro - 275 619,00 Euro |
Cordis data
Original description
"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."
Status
CLOSEDCall topic
MSCA-IF-2019Update Date
28-04-2024
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