About
Efforts to better understand the landscape and swampland can be stifled by computational complexity. I will discuss ways in which complexity could be overcome by learning random matrix approximations to string data, including both opportunities and caveats. As a concrete example, generative adversarial networks will be used to learn random matrix approximations to certain Calabi-Yau data, and physical implications will be discussed..
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The talk
Credits and licence
- Institution
- CERN
- Department
- Conferences & Workshops
- Speaker
- James Halverson
- Year
- 2019
- Language
- English
- Source
- CERN
- Licence
- CERN, as CERN states it. CERN's terms
- Attribution
- © CERN. CERN provides its audiovisual media free of charge for educational and informational use, credited to CERN; this lecture is shown in CERN's own player.
- Who can watch
- CERN gives no rating. Invisible Network shows it to Teens profiles and older, judged from what the source says about it. How courses are placed